6G    

 

 

 

6G Challenges/Questions

As of now (Feb 2021), nothing is fixed about 6G. It may not make much sense of having questions about something that is not clearly defined. But at least there is one thing that seems to be relatively clear. It would be that 6G would be based on Thz (from a few hundreds Ghz to a few Ghz) and my question is based on this assumption.

NOTE 1 : This note is more of brain storming purpose. All the questions here would be considered meaningful, but the answers for each question would still be open for other opinions from readers. These challenges or preliminary questions can be categorized as below.

NOTE 2 : [Q14]~[Q33] are from the paper 6G Takes Shape

[Q1]Isn't it too early to get into this ?

I asked the same question when I started looking into things about 5G around mid 2013. 5G activities in some pioneering organizations started even earlier. Now I heard some leading company in 5G has started their research back to 2007 or so.

I would say, "Yes it is too early if you are interested only in solid/determined 3GPP specification". I don't think you would see much formal activities in 3GPP until release 18 or later (probably sometime around 2026 or later ?).

However if you are interested in observing the whole process of how a new technology is being formed and evolved and finally turns into operational products, nothing is too learly. To me, it is always enjoyable to follow up from the very early conception through the full developmental process.

  • Historical Perspective on Early Research in Wireless Technology
    • The same question was asked when 5G research began around mid-2013.
    • Some pioneering organizations had started working on 5G much earlier.
    • Reports suggest that leading companies began their 5G research as early as 2007.
  • Current Timeline for 6G Standardization
    • If the focus is on solid/determined 3GPP specifications, it may indeed be early.
    • Formal 6G activities in 3GPP are unlikely before Release 18 or later, expected around 2026 or beyond.
    • The industry is still in the exploratory phase, with foundational research and early discussions taking place.
  • Why It’s Never Too Early to Observe and Engage
    • If one is interested in understanding the entire technological evolution, engaging early is beneficial.
    • Observing how a technology transitions from concept to operational products provides valuable insights.
    • Following early discussions helps in anticipating market trends, investment opportunities, and research directions.
  • Personal Perspective on Early Engagement
    • Tracking the progress of new wireless technologies from the earliest stages can be insightful and rewarding.
    • Staying updated allows better preparation for future industry shifts.
    • Whether for research, business, or technological curiosity, engaging early ensures a deeper understanding when formal development begins.

[Q2]Do we have enough time ?

Assuming that 6G is targeted to be deployed in 2030, we have roughly 10 years as of now (Feb 2021). It implies that most of critical component of technology (especially the technologies related to physical layer implementation) should be ready a few years before the deployment target. It means that we have only around 5 years or so until those PHY related technology is ready. Would this be enough time ? I think it would be very challenging.

Looking backwards to the process of conceptualization to realization for 5G which also took almost 10 years, there wasn't much difference in terms of physics between 4G and 5G. You may say that regular radio frequency (mostly under 2Ghz) to mmWave transition is a big difference in terms of physics, but I think the technology gap is small enough that most of 4G technology (semiconductor, OFDM, Antenna Technology etc) can be reutilized. How much of 5G technology can be reutilized in Thz technology ? I don't think there would be much of overlap between 5G and 6G physical layer implementation and a lot of technology need to be reinvented.

A way to shorten the time line for the technical readiness would be to redefine the definition of 6G PHY. For example, instead of targetting 300 Ghz and above as the first target, setting D(110Ghz-170Ghz)  or G(110-300Ghz) band as the first target and try to extend it to higher frequency as 6G evolution. In this case, there would be disputes over whether we can call the D band as Thz technology or not... but this kind of approach has been employed very often. Even in 5G case, under 40 Ghz was set as the first mmWave target and 50~70 Ghz as 5G evolution target.

==> (May 2021) I think it is worth noting the statement from this whitepaper saying "it is evident that we are still far from achieving Tbps speed even in test beds with a relatively low technology readiness level. For mass volume consumer products, we still lack proven technologies for all areas, from digital, through packaging increasing the integration level, to antennas,which will be a challenge for both academia and industry in many ways in the coming years"

NOTE : As far as I recall, there was proof of concept implementation / demonstration for 5G around early 2013. With the similar time frame, can we have something like this for 6G around 2023 ? To be honest, I think the chance is high. However, if we set D band as the first target as the first target I don't think it is impossible to come up with something to show in a few years.

In short, The 10-year timeline for 6G development remains challenging, particularly for THz-based systems. However, the adoption of FR3 (7-24 GHz) as part of the 6G spectrum strategy could accelerate readiness by reusing key 5G technologies while allowing THz research to continue in parallel. This stepwise approach increases the likelihood of meeting the 2030 deployment target without excessive technological hurdles.

  • Timeframe for 6G Deployment
    • 6G is targeted for deployment in 2030, leaving approximately 10 years from the time of writing (Feb 2021).
    • Critical physical layer technologies must be ready several years before deployment, reducing the actual development window to about 5-7 years.
    • This compressed timeline makes the challenge of readiness even more demanding.
  • Historical Perspective from 4G to 5G
    • The transition from 4G to 5G took around 10 years, making the 6G timeline even tighter.
    • The move from sub-6 GHz frequencies to mmWave was a significant technological leap.
    • However, many 4G technologies (such as modulation schemes, OFDM, and antenna technologies) were reusable in 5G, shortening the development cycle.
  • Challenges in 6G PHY Development
    • 6G will likely operate in THz frequencies, where much of the 5G technology may not be directly applicable.
    • There is a larger gap between 5G and 6G physical layer implementation compared to previous generations.
    • New materials, circuit designs, and antenna technologies must be developed almost from scratch.
  • The Role of FR3 in 6G Readiness
    • Recent industry discussions suggest the adoption of Frequency Range 3 (FR3) for 6G.
    • FR3, covering 7 GHz to 24 GHz, serves as an intermediate step between 5G sub-6 GHz and mmWave.
    • If FR3 becomes a primary focus, 6G development may leverage many existing 5G advancements, significantly reducing the technology gap.
    • This approach could allow faster 6G readiness while THz-band research continues for future enhancements.
  • Redefining 6G PHY for Faster Readiness
    • One approach to shorten the timeline is to redefine the initial target frequencies for 6G.
    • Instead of immediately focusing on 300 GHz and above, an incremental evolution approach could be taken:
    • D-band (110-170 GHz) or G-band (110-300 GHz) could be used as the first step.
    • This would enable gradual adaptation from 5G technology while allowing research to mature for higher frequencies.
    • This stepwise strategy has been employed before, such as mmWave adoption in 5G starting with sub-40 GHz before extending to 50-70 GHz.
  • Current Readiness and Technical Gaps
    • A whitepaper from May 2021 highlights that achieving Tbps speeds in real-world tests is still in early stages.
    • Major challenges remain in:
      • Digital processing, packaging, and integration of high-frequency components.
      • Antenna miniaturization and efficiency at THz frequencies.
      • Power efficiency and heat dissipation for compact consumer devices.
  • Lessons from 5G Concept Development
    • 5G concept demonstrations began around 2013, which was 10 years before deployment.
    • If 6G follows a similar path, early proof-of-concept implementations should be expected by 2023.
    • The feasibility of this depends on whether lower bands (FR3, D/G bands) are used first, allowing partial reuse of existing technologies.

[Q3]How to generate Thz Signal ?

I think the first step for PHY implementation is to develop signal (like CW or pulse) for the targeted frequency. In this case, we need to develop a device that can generate the signal at Thz range. There has been a few conventional approach for this. One is to downconvert the optical signal to Thz range and the other one is to upconvert high frequency mmWave to Thz range signal. Recently some researches has been done to develop devices to generate Thz signal directly in Thz range.

Once this type of device is developed, next step would be to reduce the size enough to fit for the communication device (especially size reduction for mobile device would be the most challenging) and develop the process for mass production.

  • Conventional Approaches for THz Signal Generation
    • Optical Downconversion:
      • Converts high-frequency optical signals (such as laser outputs) into THz signals.
      • Common techniques include photonic mixing and optical heterodyning.
    • Electronic Upconversion:
      • Upconverts millimeter-wave (mmWave) signals into the THz range using nonlinear electronic devices.
      • Methods include frequency multipliers and high-speed semiconductor oscillators.
    • Direct THz Generation:
      • Some research focuses on direct THz signal generation using specially designed semiconductor devices such as quantum cascade lasers (QCLs) and graphene-based transistors.
  • Challenges in THz Signal Generation for Communication
    • Device miniaturization:
      • THz signal generators must be small enough for integration into mobile and base station equipment.
      • This is especially critical for portable devices and low-power applications.
    • High efficiency and power output:
      • THz sources typically have low power output, making efficient amplification necessary.
    • Mass production feasibility:
      • Fabricating THz components with cost-effective, scalable manufacturing is a major hurdle.
    • Material and integration challenges:
      • Novel semiconductor materials (e.g., GaN, InP, graphene) are being explored to improve device efficiency and reliability.

[Q4]How to modulate Thz Signal ?

Can we use the OFDM which is good for wideband implementation ?  or do we need to turn to single carrier modulation ? If we need to turn to single carrier technology, how can we implement ultra wideband ?   

==> (May 2021) I think it is worth noting the statement from this whitepaper saying "In practice, this bandwidth requirement is then even wider for single carrier modulation due to the larger guard band compared with OFDM. Yet the digital signal processing (DSP) of OFDM signals consumes so much energy that it is unlikely to be a viable solution for any DSP in the foreseeable future"

Modulating THz signals presents unique challenges due to the ultra-wideband nature of the spectrum and the energy consumption of digital signal processing (DSP) at such high frequencies. While OFDM offers high spectral efficiency, its energy demands make it impractical for THz frequencies. Single-carrier modulation is a promising alternative but comes with spectral efficiency trade-offs. Hybrid or novel modulation techniques could bridge the gap, making THz communication viable for 6G applications.

There are two main modulation approaches under consideration:

  • OFDM (Orthogonal Frequency Division Multiplexing) for THz Communications
    • Advantages:
      • Well-suited for wideband implementation due to its high spectral efficiency.
      • Provides robustness against multipath fading, which is important for THz propagation.
    • Challenges:
      • The high DSP complexity and energy consumption at THz frequencies make OFDM less practical for power-limited devices.
      • Processing THz OFDM signals requires ultra-fast ADCs and efficient channel estimation, which are still developing.
  • Single-Carrier Modulation for THz Signals
    • Advantages:
      • Avoids the high DSP power requirements of OFDM.
      • Can achieve lower latency and better efficiency in THz bands.
    • Challenges:
      • Requires larger guard bands, leading to lower spectral efficiency than OFDM.
      • Implementing ultra-wideband transmission with single-carrier schemes remains a challenge.
  • Potential Hybrid and Alternative Modulation Strategies
    • DWT (Discrete Wavelet Transform) based modulation: Reduces the need for complex FFT processing in OFDM.
    • Adaptive Hybrid OFDM-SCM (Single Carrier Modulation) schemes: Dynamically switch between OFDM and single-carrier transmission based on network conditions.
    • Orbital Angular Momentum (OAM) Multiplexing: Uses structured electromagnetic waves to carry multiple parallel data streams, improving spectral efficiency in THz bands.
  • Key Industry Insights
    • A May 2021 whitepaper highlights the energy challenge of DSP-based OFDM at THz frequencies.
    • Even though single-carrier modulation requires larger guard bands, it remains a viable alternative due to lower processing complexity.
    • Future research should focus on low-power DSP architectures and efficient modulation schemes to balance spectral efficiency and energy consumption.

[Q5]Do we have proper antenna technology for Thz ?

One of the hot topics when we were talking about 5G technology in comparison to 4G. Now experts are talking about ultra massive mimo. Applying the logic explained here, it would be understandable that we would need to put a lot of more antenna elements to work in such a high frequency like Thz, but there would be a lot of challenges to integrate very high number of elements. In addition, it would be critical to develop analog and digital solutions required for beam foraming (e.g, phase shift and amplification control) that can work in this high frequency region.

THz communication requires specialized antenna designs due to the unique challenges posed by high-frequency operation. While ultra-massive MIMO and advanced beamforming techniques are being explored, significant hurdles remain in making THz antennas practical for real-world deployment. While significant progress has been made in ultra-massive MIMO and beamforming, THz antennas still face major integration and fabrication challenges. New materials, miniaturized designs, and hybrid beamforming techniques will be key to making THz antennas viable for 6G communication.

  • Need for High-Density Antenna Arrays
    • Higher frequencies lead to shorter wavelengths, allowing more antenna elements to be packed into a smaller area.
    • Ultra-massive MIMO (UM-MIMO) is being considered to improve beamforming and spatial multiplexing at THz frequencies.
    • However, integrating a very high number of antenna elements into practical devices is a major challenge.
  • Challenges in THz Antenna Integration
    • Fabrication complexity: Traditional materials and manufacturing techniques may not scale well to THz frequencies.
    • High power loss: At THz frequencies, conductive and dielectric losses significantly impact antenna efficiency.
    • Thermal management: The compact design of THz antennas can lead to heat dissipation challenges, especially in mobile applications.
  • Beamforming and Phased Array Challenges
    • Analog and digital beamforming solutions must be adapted for extremely high frequency operation.
    • Precise phase shifting and amplification control are required to maintain effective directional beams.
    • New architectures such as hybrid beamforming may offer a balance between performance and hardware complexity.
  • Emerging Solutions for THz Antennas
    • Graphene and nanomaterial-based antennas: Provide high conductivity with low energy loss, improving efficiency.
    • Plasmonic antennas: Use surface plasmons to enhance radiation efficiency at THz frequencies.
    • Metamaterial-based antennas: Enable beam steering and adaptive radiation patterns, reducing the need for complex phase-shifting circuits.

[Q6]Do we know about the characteristics of Thz Channel and do we have any good model for it ?

When we talk about moving towards 6G technology, a big question is whether we understand the THz (terahertz) channel well enough and if we have a good model for it. This is similar to past concerns about antenna technology for very high frequencies. Just like when we moved from 4G to 5G and started using many more antennas to handle higher frequencies, with 6G and THz frequencies, we face even bigger challenges.

The THz frequency is very high and can carry a lot of data quickly, but it's also new territory. We don't yet have good models that show us how signals behave at these frequencies. Signals at THz frequencies can be absorbed by the atmosphere more easily and are affected by things like water vapor, which makes it hard to predict how they will travel.

Understanding and overcoming these challenges is key to making 6G technology work and getting the most out of its ability to transmit data at very high speeds.

Understanding the THz channel is a critical challenge for 6G development. While THz frequencies promise ultra-high data rates, their propagation characteristics are not yet fully modeled, making it difficult to predict and optimize signal behavior. THz communication faces significant propagation challenges, and existing models are not yet mature enough for reliable deployment. Continued research into experimental validation, AI-based modeling, and adaptive transmission techniques is essential to fully harness THz frequencies for 6G networks.

  • THz Channel Propagation Challenges
    • THz waves experience high free-space path loss, leading to short transmission distances.
    • Atmospheric absorption is significant, especially due to water vapor, oxygen, and other atmospheric molecules, which cause signal attenuation.
    • Scattering effects are more pronounced at THz frequencies, making transmission paths harder to model.
    • Limited diffraction capability reduces the ability of THz signals to bend around obstacles, requiring precise beam alignment.
  • Comparing THz with Lower Frequency Bands
    • Transitioning from 4G to 5G mmWave required a major shift in antenna and propagation modeling.
    • Moving from mmWave to THz frequencies presents even greater challenges due to shorter wavelengths and increased absorption losses.
    • Unlike sub-6 GHz and mmWave, where propagation models are well-established, THz still lacks a widely accepted channel model.
  • Current Efforts in THz Channel Modeling
    • Researchers are working on ray-tracing-based models to predict THz signal behavior in different environments.
    • Machine learning-based channel prediction is being explored to compensate for complex propagation effects.
    • Experimental THz testbeds are being deployed to gather real-world data and refine theoretical models.
  • Need for Advanced THz Channel Models
    • Future THz models must consider dynamic environmental effects, such as humidity changes and object movements.
    • Hybrid modeling approaches, combining physics-based simulations and AI-driven predictions, may provide more accurate results.
    • Developing adaptive beamforming techniques will be essential to mitigate signal degradation in real-world scenarios.

[Q7]How to handle the issue of estimating channel  and reporting channel state information ?

With 6G and THz frequencies, this becomes much harder than low frequencies in previous technology. The reason is that signals at these high frequencies are more sensitive to obstacles, they can get absorbed by materials easily, and even the air or weather can affect them a lot. This makes it difficult to predict how the signal will behave, which is what we need to do for good channel estimation and CSI reporting.

To handle these issues, we need to develop new tools and techniques. These might include advanced algorithms that can quickly and accurately figure out the state of the channel, even when conditions change rapidly. We also need to design systems that can automatically adjust how they send data based on the latest channel information to ensure that communication remains clear and fast.

Estimating and reporting Channel State Information (CSI) is significantly more challenging at THz and 6G frequencies due to the highly dynamic nature of the channel. THz signals are more sensitive to obstacles, atmospheric absorption, and environmental changes, making real-time CSI tracking essential for reliable communication.

THz channel estimation and CSI reporting require advanced AI-driven algorithms, compressed feedback techniques, and intelligent surfaces to compensate for rapid channel fluctuations and high path loss. Efficient CSI management will be critical in ensuring high-speed, low-latency 6G communication

  • Challenges in THz Channel Estimation and CSI Reporting
    • Severe path loss and signal blockage: THz signals are easily absorbed by materials and affected by environmental factors like humidity and temperature variations.
    • Rapid channel variations: High mobility scenarios (e.g., vehicles, drones) introduce fast-changing channel conditions that require real-time adaptation.
    • High overhead in CSI feedback: With ultra-massive MIMO and beamforming, reporting CSI for large antenna arrays increases system overhead.
  • Techniques to Improve Channel Estimation
    • AI-Driven CSI Prediction:
      • Machine learning can predict CSI based on historical data and environmental conditions.
      • Deep learning-based models can fill in missing CSI data for better accuracy.
    • Compressed Sensing for CSI Reduction:
      • Instead of reporting full CSI, compressed sensing techniques can extract and transmit only essential channel information.
      • Reduces the amount of CSI feedback required, minimizing overhead.
    • Reconfigurable Intelligent Surfaces (RIS):
      • Smart surfaces can dynamically shape and reflect THz signals, improving CSI stability.
      • Helps mitigate sudden blockages and improves overall channel reliability.
  • Adaptive CSI Reporting Strategies
    • Hybrid Beamforming: Reduces the number of CSI parameters needed for feedback.
    • Sparse CSI Feedback Mechanisms: Selectively reporting CSI based on network conditions to balance accuracy and overhead.
    • Predictive Feedback Models: Using AI-based estimators to forecast channel conditions, reducing real-time feedback dependency.

[Q8]How to handle such a high sampling rate requirement of ADC for ultra-wide bandwidth ?

I think this has always been a serious challenges for every new technology. Evolving from 4G to 5G, we needed to revolutionize the ADC handling 20 Mhz BW to 400 Mhz BW. As of writing (Feb 2021), it seems that ADC handling 200 Mhz BW seems to be ready with reasonal cost even for the mobile device, but not sure of the one that can handle 400 Mhz. In 6G, we are talking about 100 Ghz Bandwidth and we can easily guess how hard it would be.

Analog-to-Digital Converters (ADCs) face significant challenges in 6G due to the massive bandwidth requirements of 100 GHz or more. The transition from 4G (20 MHz BW) to 5G (400 MHz BW) already required revolutionary advancements in ADC technology, and 6G presents an even greater challenge.

Handling ultra-wide bandwidth in 6G requires a combination of advanced ADC architectures, such as compressed sensing, time-interleaving, photonics-based ADCs, and hybrid analog-digital techniques. Efficient signal processing and power optimization will be critical for making high-speed ADCs feasible for 6G mobile devices and infrastructure.

  • Challenges in High-Speed ADCs for 6G
    • Extremely high sampling rates: Traditional Nyquist sampling requires sampling rates exceeding 200 GSps (gigasamples per second) for 100 GHz bandwidth.
    • Power consumption concerns: High-speed ADCs require significant power, making them impractical for mobile devices.
    • Data processing bottlenecks: Ultra-fast ADCs generate massive data streams, overwhelming signal processing units.
    • Cost and scalability: High-performance ADCs at THz frequencies remain expensive and difficult to integrate into consumer devices.
  • Potential Solutions to Overcome ADC Limitations
    • Compressed Sensing ADCs
      • Leverages sparse signal properties to reduce the required sampling rate.
      • Allows lower-speed ADCs to reconstruct high-bandwidth signals efficiently.
    • Time-Interleaved ADCs
      • Uses multiple parallel ADCs to increase effective sampling rate without increasing individual ADC speed.
      • Requires precise clock synchronization and calibration to avoid artifacts.
    • Sigma-Delta ADCs for High-Frequency Applications
      • Used in oversampling architectures to improve signal-to-noise ratio (SNR).
      • Can help mitigate quantization noise in ultra-high-speed ADCs.
    • Photonics-Based ADCs
      • Optical sampling techniques using photonic ADCs can surpass electronic ADC limits, enabling THz signal digitization.
      • Research in silicon photonics could make this approach scalable.
    • Hybrid Analog-Digital Processing
      • Pre-processing signals in the analog domain (e.g., filtering, downconversion) before digitization reduces ADC burden.
      • AI-driven adaptive sampling techniques can further optimize resource use.

[Q9]Do we have such a high performance of DSP or revolutionaly channel coding algorithm  ?

Achieving such a high data rate like 1 Tbs implies that we would need to perform such a wide baseband processing. Among these process, channel coding would be the most performance demanding. So we may need to completely redesign the channel coding algorith to be more efficient or to be possible for parallel processing.

Achieving ultra-high data rates like 1 Tbps in 6G requires extremely powerful digital signal processing (DSP) architectures and highly efficient channel coding algorithms to handle the massive bandwidth and high computational complexity of baseband processing. To achieve 1 Tbps data rates, 6G will require radically new DSP architectures and highly parallel, energy-efficient channel coding techniques. AI-driven DSP, photonic processing, and advanced error correction will be key enablers for meeting 6G’s extreme computational demands.

  • Challenges in DSP for 6G
    • Ultra-wideband signal processing: Processing 100+ GHz bandwidth demands a huge computational load, requiring highly parallelized architectures.
    • Power efficiency: Traditional DSP approaches would consume excessive power, making them impractical for mobile applications.
    • Latency constraints: High-speed DSP operations must keep latency below microsecond levels to support real-time applications.
  • Potential DSP Solutions for 6G
    • AI-accelerated DSP
      • Machine learning and AI can help optimize adaptive filtering, equalization, and error correction dynamically.
    • Photonic DSP
      • Optical computing and photonics-based DSP can provide faster and more energy-efficient signal processing than conventional electronics.
    • Edge Computing for Baseband Processing
      • Offloading intensive DSP tasks to edge servers or distributed processing units could reduce local computational overhead.
    • Massively Parallel Architectures
      • Using GPU-accelerated or FPGA-based processing can enhance performance while reducing energy consumption.
  • Challenges in Channel Coding for 6G
    • Traditional error correction methods (e.g., LDPC, Polar codes) may not scale efficiently to ultra-high-speed data rates.
    • New coding techniques must balance performance, complexity, and parallelizability to support 6G’s extreme requirements.
  • Potential Channel Coding Solutions
    • Quantum-Inspired Error Correction
      • New error correction schemes based on quantum information theory could provide better performance at ultra-high speeds.
    • Turbo-LDPC Hybrid Coding
      • Combining turbo and LDPC codes could enhance reliability while enabling parallel decoding.
    • Sparse Graph-Based Coding
      • Reducing computational complexity by using sparsely connected coding structures to minimize processing overhead.
    • Neural Network-Assisted Decoding
      • AI-based channel decoding can learn and adapt to real-time channel conditions, optimizing error correction dynamically.

[Q10]We may need AI-driven Smart Hardware

In order to enable dynamic communication and networking solutions needed to support the applications of THz networks agile reconfigurable hardware orchestrated. Considering the complexity of these configurations and unavailability of deterministic (closed form) of solution, we may need AI based solution for this (refer to this paper)

As THz networks and 6G introduce highly dynamic communication and networking requirements, traditional hardware-based solutions may not be flexible enough. AI-driven smart hardware could be essential for optimizing adaptive, reconfigurable, and self-orchestrating 6G systems.

To support high-speed, ultra-dynamic 6G applications, AI-driven hardware will be crucial. Reconfigurable AI-controlled circuits, intelligent beamforming, neuromorphic computing, and energy-efficient AI processing will define the next generation of adaptive 6G hardware.

  • Challenges in Traditional Hardware for 6G
    • Rigid architectures: Conventional hardware lacks the flexibility to dynamically adjust configurations for variable THz channel conditions.
    • Complex system configurations: The enormous parameter space of 6G (beamforming, network slicing, power allocation) requires real-time decision-making beyond human or pre-programmed capabilities.
    • Lack of deterministic (closed-form) solutions: Many optimization problems in THz communication, RF design, and network control have no direct mathematical solutions, making AI-driven approaches necessary.
  • AI-Driven Smart Hardware Solutions
    • Reconfigurable AI-Orchestrated Hardware
      • AI can dynamically adjust circuit parameters, network configurations, and hardware resources in real time.
      • Neuromorphic computing can optimize hardware adaptation for ultra-fast 6G systems.
    • AI-Assisted THz Antennas and Beamforming
      • AI-driven hybrid beamforming can optimize antenna configurations to adapt to rapid channel variations.
      • Reinforcement learning can train antenna arrays to maximize efficiency in dynamic wireless environments.
    • AI for Power-Efficient Processing
      • AI can optimize power allocation and processing load distribution, reducing energy consumption in mobile devices and base stations.
      • Edge AI processing reduces reliance on cloud computation, improving real-time decision-making.
    • AI-Enhanced Reconfigurable Intelligent Surfaces (RIS)
      • AI can dynamically adjust metamaterial-based surfaces to control THz signal reflections and improve coverage.

[Q11]How to overcome the difficulties of Heterogeneous integration and fabrication ?

In current technology (as of Sep 2022), most of the electronic components are based on the similar materials (i.e, silicon CMOS), but in 6G for super high frequency it would be likely that we need to depend on various different materials and technologies for the hardware components (e.g, combination of CMOS, III-V, graphene, nano-materials and others). In this case, how to integrate / fabricate this kind of different materials/technologies on a same chip.

6G hardware, especially for super high-frequency (THz) communication, will require the integration of diverse materials and technologies to achieve high performance, energy efficiency, and scalability. Unlike current semiconductor technology, which relies predominantly on silicon CMOS, future 6G devices may need to incorporate III-V compounds, graphene, nanomaterials, and other advanced materials.

The future of 6G chip fabrication will depend on advancements in 3D stacking, wafer-scale integration, advanced lithography, and AI-driven process control. Overcoming heterogeneous integration challenges will be key to realizing high-performance, low-power, and cost-effective THz communication hardware.

  • Challenges in Heterogeneous Integration for 6G
    • Material Compatibility Issues
      • Different materials (e.g., CMOS, III-V, 2D materials) have incompatible thermal expansion coefficients, leading to stress and defects during fabrication.
      • Electrical and thermal conductivity mismatches between graphene, silicon, and compound semiconductors require novel interface solutions.
    • Manufacturing Complexity
      • Monolithic integration (fabricating all components on a single chip) is difficult when combining different material types.
      • Hybrid bonding and 3D integration techniques need to be improved for multi-material stacking.
    • Scalability and Cost
      • Fabrication techniques for new materials (e.g., graphene, nanowires, plasmonic materials) are still in the research phase and not yet suitable for mass production.
      • High-precision fabrication techniques like e-beam lithography are expensive and slow.
  • Potential Solutions for Heterogeneous Integration
    • 3D Stacked Chip Architectures
      • Through-Silicon Vias (TSVs) can enable vertical stacking of different materials, improving integration efficiency.
      • 3D integration reduces footprint while improving performance for high-speed THz processing units.
    • Wafer-Scale Heterogeneous Integration
      • Heterogeneous wafer bonding techniques can enable the seamless integration of III-V materials (e.g., GaN, InP) with CMOS.
      • Silicon photonics can be co-integrated with plasmonic and photonic THz components for ultra-fast data processing.
    • Advanced Lithography and Printing Techniques
      • Nanoimprint lithography (NIL) offers precise patterning for nanomaterial integration at lower costs.
      • 2D material transfer printing allows scalable integration of graphene and MoS₂ onto silicon wafers.
    • AI-Driven Process Optimization
      • Machine learning models can optimize fabrication parameters for defect-free integration of multi-material structures.
      • AI can also predict and correct fabrication-induced stress issues to improve yield rates.

[Q12]How to develop technologies to meet the end-to-end latency ?

From 4G, there has always been key user cases that requires very tight latency. But in most cases the specification that wireless industry came out was about the effort to reduce the latency within radio protocol stack. In some aspect, this is understanble because wireless industry does not have full control over the specification of the whole network end-to-end, but it would be better be more cautious about promoting new technologies and take into acount those factors controlled by other industries (e.g, IP networks).

Reducing end-to-end (E2E) latency is crucial for 6G applications such as real-time AI, holographic communication, and autonomous systems. While previous generations (4G, 5G) primarily focused on reducing latency within the radio protocol stack, achieving ultra-low E2E latency requires optimization across the entire network, including IP transport, edge computing, and cloud processing.

Meeting E2E latency requirements for 6G requires a holistic approach, optimizing latency not just in the radio network but across transport, edge computing, and cloud infrastructure. AI-driven network optimization, edge processing, TSN, and next-generation networking technologies will be essential for achieving sub-millisecond E2E latency for next-gen applications.

  • Challenges in Achieving E2E Low Latency
    • Network-wide latency contributors: While the wireless link latency can be minimized, backhaul, core networks, and application processing also introduce delays.
    • Lack of unified control: The wireless industry can optimize radio networks, but IP networks, cloud computing, and content delivery are controlled by different stakeholders.
    • Variability in network conditions: Latency can fluctuate due to traffic congestion, routing inefficiencies, and cloud processing delays.
  • Key Technologies to Reduce E2E Latency
    • AI-Driven Dynamic Latency Optimization
      • Machine learning models can predict network congestion and dynamically re-route traffic for lower delays.
      • AI-assisted predictive caching can pre-fetch critical data to minimize processing delays.
    • Edge Computing and Fog Networking
      • Processing at the edge reduces dependence on central cloud data centers, significantly lowering latency.
      • Fog networking allows distributed computing closer to the user, avoiding unnecessary backhaul delays.
    • Integrated Network and Transport Layer Optimization
      • Cross-layer design should be used to coordinate radio, transport, and application layers for latency-aware networking.
      • Ultra-reliable low-latency communication (URLLC) mechanisms need end-to-end implementation beyond the radio stack.
    • Time-Sensitive Networking (TSN) for Deterministic Latency
      • TSN-based Ethernet ensures predictable delay for critical applications like industrial automation and vehicular networks.
      • Works alongside 5G/6G deterministic networking to guarantee bounded latency.
    • Quantum Networking for Future Applications
      • Research into quantum entanglement-based networking could theoretically enable instantaneous data exchange.

[Q13]6G KPI / Requirement is good enough for everything that we promise ?

Looking back to what industry promised (at least vaguely claim to deliver) at the early conceptual discussion, I got the impression that the industry commited too much (sounds like commiting what the technology is not capable of). This kind of over-commitment lead to over-disappointment among user when the technology is delivered and such a disappointment would raise a negative impact on the success of the technology. I think it would be good for us to become a little bit more cautious when we try to promote new comming technology. We all know what would be the eventual outcome of 'overcommit - underdelivery'.

For now, one thing poping up in my mind whenever I see 6G KPI and 6G use case. It is Hologram Application. At least with the current technology (as of Feb 2022), it would require several Tera bps of throughput for hologram application for single user. Then my question is 'With 1 Tbps max throughput proposed in 6G KPI, how can we implement this kind of application ?'. Are we just expecting some super genious engineers to come out with another disruptive technology to greatly reduce the required throughput for the transmission of holograms ?

As with previous generations, early discussions on 6G have led to ambitious promises. However, there is a risk of overcommitting to capabilities that the technology may not be able to deliver, which could lead to user disappointment and negative industry impact.

6G's current KPI targets (such as 1 Tbps peak data rates) may not be sufficient to fulfill all promised use cases, particularly data-intensive applications like holographic communication. Advancements in compression, edge computing, and semantic communication will be necessary to make these applications viable within 6G’s practical constraints.

  • The Risk of Overpromising and Underdelivering
    • In previous generations (e.g., 5G), early expectations were set very high, sometimes beyond realistic technological feasibility.
    • 6G must learn from past experiences to ensure that KPI promises align with practical deployment scenarios.
    • Overpromising can lead to market skepticism and delayed adoption if the expectations are not met.
  • Holographic Communication as a Use Case Concern
    • One of the most discussed 6G applications is real-time holographic communication.
    • Current technology (as of Feb 2022) requires several terabits per second for a single user’s holographic transmission.
    • Given that 6G proposes a maximum throughput of 1 Tbps, this raises the question:
      • Is 6G’s KPI truly sufficient to support immersive holographic applications?
      • Will we need an entirely new breakthrough to reduce the data requirement?
  • Possible Solutions to Address the Gap
    • Advanced Data Compression
      • AI-driven compression algorithms could dramatically reduce the bandwidth requirements of real-time holograms.
      • Perceptual coding techniques could prioritize key visual elements while discarding redundant data.
    • Edge Computing and Distributed Processing
      • Offloading hologram rendering and transmission to edge servers could reduce real-time network load.
    • Semantic Communication
      • Instead of sending raw data, semantic-based transmission could interpret and reconstruct holographic data efficiently.

[Q14]There is a widespread sense that 5G has been a disappointment. Why is that? Is it true?

Overall, 5G is progressing but has yet to deliver on its loftiest promises, making expectations for 6G more cautious. 5G has been both a success and a disappointment.

  • Successes:
    • Rapid Adoption: Over 2 billion 5G smartphones have been shipped, with adoption outpacing LTE.
    • Ongoing Evolution: Many operators still rely on 4G cores (NSA mode), but 5G is advancing with new features like Reduced Capability (RedCap) introduced in Release 17.
    • Emerging Markets Leading the Way: Some developing countries, like India, have leapfrogged LTE and directly adopted 5G.
    • Fixed Wireless Access (FWA): Millions use 5G for home internet, proving its value.
    • Network Flexibility: 5G’s design allows for diverse applications, setting the stage for future growth.
  • Disappointments:
    • Missed Expectations: Predictions of revolutionary IoT applications, such as smart cities and autonomous vehicles, haven’t materialized at scale.
    • Millimeter Wave (mmWave) Challenges: Despite high-speed potential, mmWave faces technical and economic barriers, such as poor indoor penetration, high infrastructure costs, and lack of widespread device support.
    • Limited URLLC Adoption: Ultra-Reliable Low Latency Communications (URLLC) hasn’t gained traction due to high implementation costs and diminishing benefits beyond existing 5G latency improvements.
    • High Costs and Power Consumption: Operators face rising electricity costs and spectrum expenses, leading to financial strain and hesitancy toward further network expansion.

[Q15]Based on lessons learned from 5G, what should 6G do differently?

Rather than chasing unrealistic new performance benchmarks, 6G should focus on making current 5G aspirations feasible at scale—with better energy efficiency, lower costs, and a more sustainable approach to network expansion.

  • Shift in Focus: Efficiency Over Pure Performance
    • 6G will prioritize energy and cost efficiency over pushing communication KPIs (e.g., higher peak speeds, lower latency).
    • 5G already set aggressive targets for speed, latency, and reliability—6G should focus on achieving these KPIs more efficiently rather than exceeding them.
    • The "6G Efficiency Triangle" will replace the 5G use-case triangle, focusing on:
      • Area spectral efficiency (bits/Hz/area)
      • Cost efficiency (bits/currency unit)
      • Energy efficiency (bits/Joule)
  • Tackling Power Consumption and Cost Constraints
    • Power and cost limitations have become major barriers to network expansion.
    • 6G must bring significant advancements in power-efficient hardware, smarter algorithms, and cost-effective network deployments to be viable.
  • Handling 10x Growth in Network Capacity
    • Cellular data traffic is growing exponentially, requiring 6G to handle 10x the traffic of 5G.
    • Capacity increases will rely on three key strategies:
      • More spectrum allocation
      • Higher spectral efficiency via advanced signal processing
      • Network densification (more small cells), although cost concerns may slow this
  • Smarter Network Densification
    • Unlike in past generations, densification in 5G has been cost-prohibitive, with new mid-band spectrum (C-band) shouldering much of the traffic.
    • 6G must find ways to make small cell deployment more cost-effective to support increased data demand.

[Q16]Besides Fixing 5G,?what new use cases, services, and applications will drive 6G?

6G is expected to move beyond simply improving 5G by expanding into new domains that integrate sensing, AI, and computing, rather than just enhancing communication speeds and latency. 6G will be more than a communication network—it will be an intelligent, sensing, and computing infrastructure that supports digital twins, AI-powered services, and real-time edge computing. These innovations will transform industries beyond telecom, from autonomous systems to smart cities and immersive experiences.

  • Integrated Sensing and Digital Twins
    • 6G networks will act as large-scale sensor systems, offering real-time situational awareness.
    • RF sensing will improve communication (e.g., beamforming, handover) and enable new applications.
    • Urban Digital Twins:
      • 6G will create high-resolution 3D models of cities using network data, improving logistics, transportation, construction, and urban planning.
      • Mobile Network Operators (MNOs) could monetize anonymized digital twin data for industries needing real-time spatial awareness.
    • On-demand drone networks could be deployed from base stations to enhance local environmental monitoring.
  • AI-Integrated Compute as a Service
    • 6G networks will merge with cloud computing, effectively becoming cloud providers offering compute resources (CPUs, GPUs, NPUs, and FPGAs).
    • Key innovations in AI integration:
      • AI-powered edge computing for ultra-low latency applications (e.g., AR glasses, mobile robotics).
      • Dynamic workload balancing between mobile devices, edge computing, and cloud resources to optimize performance, power consumption, and cost.
      • AI-assisted network optimization to allocate resources more efficiently.

[Q17]What will be the new value proposition for 6G?

The true value of 6G lies in its ability to deliver efficiency gains and new sensing/computing services, rather than merely improving network speed or latency. The 6G Value Prism highlights how success will depend on adoption, cost-effectiveness, and expanded capabilities beyond basic communications.

  • The 6G Value Prism
    • The value of 6G is visualized as a prism combining two key elements:
      • Efficiency Triangle (energy efficiency, cost efficiency, spectral efficiency)
      • Services Triangle (sensing, computing, global connectivity)
    • Larger prism volume = greater 6G success, meaning:
      • Higher adoption of sensing, computing, and global connectivity
      • More energy- and cost-efficient networks
      • Widespread infrastructure rollout and market penetration
  • Example: Mobile XR as a 6G Use Case
    • Extended Reality (XR) (VR/AR/MR) is a demanding application requiring:
      • High data rates and low latency
      • Reliable connectivity with edge computing
      • Energy-efficient networks and devices
    • 6G will support XR better than 5G, but not by drastically improving communication KPIs.
      • Instead, it will improve energy efficiency, lower costs, and integrate computing & sensing capabilities.
    • This pattern applies to other emerging 6G applications—the goal is not just enabling new verticals but making 5G’s envisioned use cases practical at scale.

[Q18]Will 6G will be backward-compatible with 5G?

6G will not abandon 5G but will ensure a smooth transition by focusing on continuity, modular design, and long-term coexistence. Some 5G features may be phased out, but key applications—especially in IoT—will remain supported well into the 6G era.

  • 6G Will Prioritize Backward Compatibility
    • 6G will be designed for continuity, ensuring that most 5G deployments remain functional throughout the 6G era.
    • Backward compatibility will be a core design principle, unlike past transitions (e.g., 3G CDMA → 4G OFDMA) that required a complete reset.
  • Continuity Enables Faster Innovation
    • A stable baseline standard allows for incremental improvements rather than waiting for a new G to introduce major changes.
    • 5G has already introduced forward compatibility with flexible designs like:
      • Bandwidth Parts (BWPs)
      • Variable OFDM subcarrier spacing
      • Self-contained TDD slots
      • Extensive carrier aggregation
    • This modular approach reduces the need for radical changes, making compatibility easier.
  • Long-Term Coexistence of 5G and 6G
    • Strict backward compatibility (supporting all 5G features in 6G BSs) is unlikely, but 5G will still coexist with 6G for a long time.
    • IoT applications (e.g., smart meters, connected cars) have lifespans of 10+ years, requiring stable network support even into the 6G era.
    • RedCap (Reduced Capability 5G) and other IoT-centric features will either be incorporated into 6G or persist within dedicated spectrum allocations.

[Q19]Will 6G be the last G?

6G probably won’t be the last G, but it will change the way new generations are defined. Instead of distinct, decade-long cycles, future network advancements may evolve continuously, reducing the need for abrupt xG transitions.

  • The “xG” Model Will Persist for Now
    • Spectrum allocation cycles (~10 years) and commercial marketing strategies will likely keep the generational (xG) model alive.
    • However, from a technical perspective, 6G may mark the beginning of the end of the xG paradigm.
  • 6G as the “Pentium” of Cellular
    • Historical analogy: Intel’s x86 processors followed a clear numbering system (286, 386, 486) before transitioning to Pentium, which became a dominant brand without incremental numbering.
    • 6G might follow a similar path, where future innovations happen within a continuous framework rather than requiring a new G.
  • Shift Toward Continuous Upgradability
    • 6G will be more modular and flexible, allowing ongoing enhancements without needing an entirely new generation.
    • This decoupling of technical innovation from rigid generational shifts could enable faster, more adaptive advancements beyond the traditional xG model.

[Q20]Which spectrum bands will have a major impact on 6G?

The 7-8 GHz lower midband will be essential for 6G’s capacity expansion, while the 12.7-15.35 GHz range could deliver the high-capacity urban connectivity mmWave failed to provide. These bands strike a balance between coverage, cost-efficiency, and high-data-rate capability.

  • 6G Will Use and Expand 5G Spectrum
    • 6G will incorporate all existing 5G spectrum while adding new bands, particularly in Frequency Range 3 (FR3: 7-24 GHz).
    • Global coordination will be crucial to maximize spectrum efficiency.
  • Key Frequency Bands for 6G
    • 7-8 GHz “Lower Midband” (e.g., 7.125-8.4 GHz in the U.S.)
      • Best balance between coverage and capacity (unlike mmWave, it can penetrate buildings).
      • Can use existing cell sites (cost-effective).
      • Massive MIMO (256×16 or larger) is feasible due to smaller wavelengths (~4 cm).
      • High spatial multiplexing potential, making it key for 6G’s 10x capacity target.
    • 12.7-15.35 GHz “Upper Midband”
      • FCC and ITU exploring use for 6G.
      • Promising for urban ultra-high capacity deployments.
      • Better than mmWave:
        • Supports larger MIMO arrays with practical, low-cost antennas.
        • Less beam misalignment issues than mmWave.
        • Comparable to Starlink’s low-cost Ku-band technology.

[Q21]Will millimeter wave actually be used in 6G?

mmWave struggled in early 5G, but 6G will make it a more competitive and practical technology with better range, lower costs, and improved power efficiency. While it won’t replace mid-band for broad coverage, it will be critical for high-capacity applications in dense environments and advanced RF-sensing use cases.

  • mmWave Will Play a Bigger Role in 6G than in 5G
    • mmWave (28 GHz and above) will be used more widely in 6G but mainly for high-density areas and specialized applications rather than broad coverage.
    • Key deployment scenarios include:
      • Dense urban areas (stadiums, concert halls, malls, airports).
      • Fixed Wireless Access (FWA) in urban and suburban locations.
      • RF sensing applications (radar, localization).
  • Why mmWave Deployment in 5G Was Limited
    • mmWave struggles with coverage due to small cell range and high penetration losses (10s of dB).
    • Operators prioritized baseline coverage with mid-band (C-band) before expanding mmWave.
    • Few global deployments:
      • The U.S. has most mmWave sites, but China has none despite strong 5G adoption.
    • Widespread mmWave handsets are still rare, but increasing adoption will drive demand and lower costs.
  • Engineering Improvements in 6G mmWave
    • Larger and more efficient antenna arrays at both base stations (BS) and handsets.
    • Faster, AI-driven beam alignment for better connection reliability.
    • Machine learning-based mobility support (beam tracking, prediction using digital twins).
    • Lower power consumption due to improved efficiency and smarter transmission scheduling.
    • Better hardware and economies of scale making mmWave more cost-effective.
    • Enhanced backhaul solutions to improve small cell connectivity

[Q22]How will the different spectral bands in 6G be utilized?

6G will introduce a clearer division of spectral bands for different applications, following a layered approach similar to Nokia’s "spectrum wedding cake." This layered approach ensures efficient spectrum use, balancing coverage, capacity, and energy efficiency for a variety of applications.

Image Source : 6G Takes Shape

  • Sub-1 GHz: The IoT Band
    • Primary use: Wide-area, low-power applications (e.g., public safety, voice, IoT, metering, tracking).
    • Smartphone traffic should largely be moved off these bands, except in rural or coverage-limited areas where they provide fallback.
  • 1-2.5 GHz: The 6G FDD Coverage Layer
    • Primary use: Wide-area coverage for 6G UEs, control signaling, and high-priority data.
    • Advantages:
      • FDD (Frequency Division Duplex) offers equal uplink and downlink bandwidth, valuable for power-constrained UEs.
      • Up to 100% capacity gain possible through:
        • Advanced MIMO feedback and precoding
        • Improved coding and modulation
        • Reduced frequency guard bands
        • Optimized reference symbol design
    • Fallback coverage for 6G smartphones and data services.
  • 3-4 GHz & 7-16 GHz: The 6G Capacity Workhorse
    • C-band (3-4 GHz) will continue to be a key capacity driver, extending down to 3.1-3.4 GHz in some regions.
    • FR3 (7-16 GHz) will be a major capacity band for 6G, providing a continuum of capacity-coverage tradeoffs.
    • MIMO and antenna scaling considerations:
      • 7-8 GHz antennas: 4x more elements per unit area than C-band.
      • Higher frequencies (~13-16 GHz): Can support macrocell BSs with 1 Gbps at 1 km range.
      • Hybrid architectures needed to efficiently manage additional antenna elements for capacity and coverage.
  • mmWave: Capacity Hotspots
    • Primary use: Localized high-capacity coverage for urban hotspots, stadiums, venues, and Fixed Wireless Access (FWA).
    • Offloads traffic from lower bands, enhancing network performance.
  • Conclusion
    • 6G spectrum will be more structured and optimized for specific use cases:
    • Sub-1 GHz → IoT & public safety.
    • 1-2.5 GHz → Wide-area coverage & control signaling.
    • 3-16 GHz (C-band & FR3) → Primary workhorse for high-speed data.
    • mmWave (28+ GHz) → Localized high-capacity deployments.

[Q23]What roles will Terahertz (THz) spectrum and Reflective Intelligent Surfaces (RIS) play?

THz is not expected to be a core component of 6G cellular, but it will have niche applications in sensing, backhaul, and satellite communications. RIS is promising for improving mmWave and THz coverage, but cost concerns could limit widescale deployment in favor of small cells.

  • THz (100+ GHz) Will Have a Limited Role in 6G Cellular Networks
    • THz spectrum (above 100 GHz) is unlikely to play a major role in 6G coverage or capacity enhancement due to severe propagation limitations.
    • Lessons from 5G mmWave show low adoption, with less than 1% of 5G data traffic carried on mmWave bands due to poor penetration and limited coverage.
  • Potential Use Cases for THz in 6G
    • Short-range sensing (e.g., localization, gesture recognition) using THz’s high resolution.
    • Point-to-point outdoor backhaul for terrestrial networks.
    • High-speed data center interconnects (short-range indoor applications).
    • Non-Terrestrial Networks (NTN) and Low Earth Orbit (LEO) satellite links for inter-satellite communications.
    • Key challenges:
      • Efficient power consumption for large phased arrays.
      • Dynamic beam steering to maintain connections.
      • Avoiding interference with ultra-sensitive applications like weather satellites and radio astronomy.
  • Reconfigurable Intelligent Surfaces (RIS) Have Potential but High Cost
    • RIS can extend mmWave and THz coverage, particularly for NLOS and indoor penetration (e.g., smart windows that reflect signals inside).
    • Main challenge: High deployment and configuration costs, making small cells a more practical alternative.

[Q24]How will spectrum be allocated in 6G? Will dynamic spectrum sharing across operators and systems become the norm?

6G will move toward more complex spectrum allocation and limited sharing, but full dynamic spectrum sharing is unlikely due to service reliability concerns and power constraints. Instead, network infrastructure sharing will play a much larger role, driving cost savings and energy efficiency while enabling greater network expansion.

  • 6G Will Require New and More Complex Spectrum Allocation Models
    • FR3 (7-24 GHz) is the key new 6G spectrum, but it already has incumbents (e.g., satellite services, defense applications).
    • Protecting incumbents while allocating new spectrum will demand advanced spectrum management techniques.
  • Spectrum Sharing Will Be Limited and Cautious
    • Unreliable spectrum (due to pre-emption by incumbents) is of little value to mobile operators since service outages are unacceptable.
    • Dynamic spectrum sensing adds complexity and power consumption, which contradicts 6G’s goals of cost and energy efficiency.
    • A balanced approach will involve:
    • Relocating some incumbent services where possible.
    • Allowing partial spectrum sharing in a small fraction of the available bands to minimize impact on service quality.
  • The Era of “Clean” Spectrum Auctions is Over
    • Future spectrum allocation will come with caveats, meaning operators must adapt to shared environments.
    • Example: LEO satellite constellations (Starlink, OneWeb, Kuiper) already share spectrum under evolving regulations, hinting at a broader shift in spectrum policy.
  • 6G Will Expand Beyond Spectrum Sharing to Network Equipment Sharing
    • Shared infrastructure will become more common to improve cost and energy efficiency.
    • Example: In the U.S., third-party-owned towers (e.g., Crown Castle, American Tower) already host multiple operators.
    • China’s government-mandated infrastructure sharing has saved billions per year, lowering costs and enabling greater network densification.
    • 6G will likely see voluntary expansion of network sharing among MNOs worldwide.

[Q25]Will there be a new waveform for 6G beyond OFDM/OFDMA?

OFDM/OFDMA will remain the primary waveform for 6G, thanks to its efficiency, flexibility, and cost-effectiveness. While alternative waveforms may be used for niche applications, they will likely be integrated within the OFDMA framework rather than replacing it.

  • OFDM Will Remain the Dominant Waveform in 6G
    • OFDM is nearly optimal for cellular data communications due to:
      • Efficient MIMO precoding and equalization.
      • Low implementation complexity (enabled by FFT and one-tap equalization).
      • Flexible multiple access in time and frequency domains (OFDMA).
      • Opportunistic scheduling to maximize channel capacity with full CSI.
    • OFDM is cost-effective, well-understood, and beyond patent protections, making it difficult to replace.
    • Its alleged drawbacks (e.g., high peak-to-average power ratio, long symbol time, slow frequency rolloff) are manageable with known techniques.
    • Adopted in modern clean-slate broadband networks like Starlink and Kuiper, as well as WiFi.
  • OFDMA’s Flexibility Allows Integration of Other Waveforms
    • OFDM is not exclusive—it supports sub-waveforms and multiplexing of non-OFDM signals.
    • Potential alternative waveforms within OFDMA:
    • Sensing-centric or estimation-centric signals transmitted over unoccupied subcarriers.
    • OTFS (Orthogonal Time Frequency Space) for high-mobility scenarios.
    • Custom narrowband signals for IoT and low-power applications that prioritize efficiency over spectral throughput.

[Q26]Will there be new types of coding, modulation, or duplexing?

6G will refine coding and modulation for better spectral efficiency and cost-effectiveness, while introducing full-duplex (FD) capabilities for improved uplink and sensing applications. Expect incremental improvements to LDPC, Polar codes, and PAS-modulated QAM rather than radical changes. FD will make 6G more efficient for uplink-heavy applications and sensing-driven services.

  • Coding and Modulation in 6G Will Prioritize Cost and Energy Efficiency
    • 6G coding and modulation advancements will focus on:
      • Improving spectral efficiency (especially in lower bands).
      • Reducing power and cost for hardware implementations, especially in high-frequency bands.
    • Likely coding evolution:
      • Upgraded LDPC and Polar codes for backward compatibility with 5G.
      • New coding families (e.g., spinal codes, staircase codes, PAC codes) are unlikely due to hardware incompatibility despite minor performance gains.
    • Modulation advancements:
      • QAM-based extensions (due to efficient MIMO demapping at peak data rates).
      • Constellation shaping for efficiency gains:
        • Probabilistic Amplitude Shaping (PAS): Adjusts the probability distribution of QAM symbols to mimic a Gaussian distribution, improving spectral efficiency.
        • Geometric shaping: Used in broadcasting (DVB, ATSC), but too complex for MIMO in 6G.
        • WiFi-7 has already adopted PAS, and 6G may integrate it with LDPC for further gains.
  • Duplexing: 6G May Introduce Full-Duplex (FD) Capabilities
    • Past cellular generations used only FDD (Frequency-Division Duplex) and TDD (Time-Division Duplex).
    • 6G will likely introduce Full-Duplex (FD) for two key reasons:
      • Enhanced Uplink (UL) Performance:
        • UL traffic is increasing due to XR and interactive applications.
        • TDD struggles with UL coverage since the UE transmits only 20-30% of the time.
        • Sub-band full-duplex (SBFD) can improve this by enabling a base station to transmit and receive simultaneously using self-interference mitigation.
        • SBFD turns TDD into a more FDD-like system, improving uplink power and efficiency.
      • Joint Communications and Sensing (JCAS):
        • FD transceivers will enable 6G networks to double as sensor networks.
        • Applications include:
          • Security & surveillance
          • eHealth monitoring
          • Autonomous vehicles, drones, and robotics

[Q27]Will we see any new types of MIMO in 6G?

These innovations will bridge the gap between theoretical MIMO potential and real-world implementation in ways previous generations couldn’t fully achieve.

  • Evolution of MIMO from LTE to 5G
    • LTE MIMO was experimental, introducing multiple transmission modes (TM1-TM10) including:
      • Alamouti codes & frequency diversity.
      • Spatial multiplexing (BLAST-like schemes).
      • Multi-user MIMO (MU-MIMO) and codebook-based precoding.
    • 5G MIMO shifted to a beamforming-centric approach:
      • Precoding is transparent to the UE, focusing on beamforming rather than open-loop MIMO.
      • TDD-based channel reciprocity allows for better precoding but is limited by weak pilot signals at the cell edge.
      • MU-MIMO adoption has been limited due to scheduling and feedback challenges.
  • 6G Will Feature “Native MIMO”
    • 6G will be MIMO-first, integrating MIMO principles into:
      • Pilot design
      • Channel estimation & feedback
      • Scheduling
      • Beamforming & directional transmission assumptions
    • Key Challenges in 6G MIMO:
      • Two-way beam alignment (especially in mmWave & THz).
      • Improved channel estimation & feedback for large MIMO systems.
      • Optimizing MU-MIMO, which has been underutilized in 5G due to scheduling complexity.
  • Key MIMO Innovations in 6G
    • More Digital Beamforming & Hybrid Precoding Architectures
    • Machine Learning-Assisted MIMO
      • AI/ML will predict channel estimates, improve feedback quantization, and enhance beam tracking.
    • FDD MIMO Revamp
      • 5G focused heavily on TDD (C-band & mmWave), but 6G will improve FDD MIMO with:
        • Active antenna arrays for better coverage & spectral efficiency.
        • Higher-order MIMO in FDD bands for better uplink-downlink balance.
  • Advanced MIMO Concepts in 6G
    • Near-Field MIMO
      • Becomes significant when the antenna array is large relative to the wavelength.
      • Allows higher multiplexing gains even in line-of-sight (LOS) conditions.
    • Holographic MIMO
      • Involves ultra-dense antenna arrays or continuously radiating surfaces.
      • Can significantly increase spectral efficiency in low-mobility urban environments.
    • Cell-Free MIMO
      • Expands CoMP (4G) and Multi-TRP (5G) concepts into a fully distributed multi-BS MIMO system.
      • Reduces inter-cell interference and boosts spectral efficiency in dense urban areas.
      • Enabled by lower network latency & increased edge computing in 6G.
  • Conclusion
    • 6G will fully integrate MIMO as a core design principle with:
      • Smarter beamforming and MU-MIMO optimizations.
      • AI-powered feedback and hybrid precoding.
      • Enhanced FDD MIMO for better coverage and efficiency.
      • Advanced concepts like Near-Field MIMO, Holographic MIMO, and Cell-Free MIMO to maximize spectral efficiency in dense environments.

[Q28]Will the 6G PHY be replaced by neural networks and run on GPUs?

6G will not replace the traditional PHY with neural networks, as existing methods are already near-optimal and more power-efficient. However, DL will play an important role in PHY enhancements, particularly in CSI estimation, beamforming, MIMO, and adaptive signal processing, making the network smarter but not entirely AI-driven.

  • Deep Learning (DL) in Wireless PHY: A Potentially Disruptive Development
    • Neural networks can theoretically perform all signal processing operations in a wireless transceiver.
    • Advantages of a DL-based PHY:
      • End-to-end optimized designs could outperform traditional model-based approaches.
      • Removes assumptions about channel and noise models, electronics, and ideal feedback.
      • Can be implemented on GPUs, enabling software-defined baseband processing.
      • NVIDIA's vision for 6G (e.g., Siona, AerialSIM) promotes this approach.
  • Why DL Will Not Fully Replace the 6G PHY
    • 5G PHY is already near-optimal for spectral efficiency and reliability based on information theory.
    • OFDM, LDPC, and frequency-domain equalization are highly efficient and generalizable.
    • DL-based PHY improvements (e.g., channel estimation, equalization) have shown at most minor gains (~1 dB), with limited generalizability and explainability.
    • Power Efficiency is Crucial:
      • Existing hardware implementations (e.g., FFT/IFFT, LDPC decoders) are already extremely efficient.
      • DL approaches require significantly more power and processing area, especially at the UE side.
      • A better case for DL-based PHY exists at the BS, but it must be power-efficient and trainable.
  • The Real Role of DL in 6G PHY
    • DL will enhance, not replace, the traditional PHY pipeline.
    • DL-assisted optimizations may include:
      • Better CSI estimation & feedback compression for improved beamforming.
      • Optimized hybrid precoding for MIMO.
      • Adaptive coding/modulation selection based on real-time conditions.
      • Neural-enhanced signal detection and equalization in challenging environments.
    • The integration of DL will be selective and efficiency-driven, not a full replacement of existing DSP techniques.

[Q29]What are the key roles for machine learning in 6G?

Generative AI may revolutionize many application/use cases, but its practicality will depend on energy efficiency and computational feasibility.

  • ML Will Address High-Dimensional, Intractable Problems
    • Machine Learning (ML) is ideal for complex, high-dimensional problems that are impractical to solve using traditional optimization methods. Three key areas of impact include:
    • A. Enhancing MIMO Performance
      • Channel State Information (CSI) estimation & feedback compression for large MIMO systems.
      • Sparse CSI-based precoding & user scheduling for MU-MIMO to improve efficiency.
      • Joint channel estimation, equalization, and symbol detection for better receiver performance.
    • B. Overcoming Hardware Imperfections
      • Compensating for low-resolution quantization and RF distortions to allow the use of cheaper, lower-power hardware.
      • Mitigating power amplifier nonlinearities to improve spectral efficiency and reduce interference.
    • C. Improving Localization Accuracy
      • Traditional ToA-based localization struggles in dense urban areas due to multipath effects.
      • ML can leverage vast operator ToA datasets to improve sub-meter positioning accuracy without costly ray tracing.
  • ML for Cell-Specific Learning & Optimization
    • 6G networks will have even more configurable parameters (e.g., MIMO setups, beamforming patterns, handover thresholds).
    • ML can dynamically optimize network parameters to:
      • Reduce handover failures and improve edge-user SINR by 5-10 dB.
      • Optimize beam alignment in mmWave and FR3 bands, reducing search overhead by orders of magnitude.
    • ML-powered Digital Twins could simulate real-world network behavior to refine and optimize deployments before real-world implementation.
  • The Role of Deep Generative Models (DGMs) in 6G
    • While real-time DGM inference is too computationally expensive, offline/cloud-based DGMs can provide value in:
    • A. Channel Model Generation for AI Training
      • Real-world channel data is scarce, noisy, and expensive to collect.
      • DGMs can generate synthetic yet realistic datasets to train ML-based beamforming, CSI prediction, and MIMO algorithms.
    • B. AI-Driven Video & XR Compression
      • Future 6G XR applications require ultra-low-latency, high-bandwidth streaming.
      • DGMs could compress video content by encoding only essential sensory representations, drastically reducing bandwidth needs.
      • Generative AI can reconstruct realistic images/video at the receiver from ultra-low-bit-rate inputs.
  • Conclusion
    • ML will not replace traditional wireless PHY but will significantly enhance 6G in:
    • MIMO efficiency and precoding
    • Hardware adaptation and power efficiency
    • Cell-specific tuning for real-time network optimization
    • Improved localization and beam alignment
    • AI-powered channel modeling and compression

[Q30]Can we really achieve global broadband coverage directly to smartphones with emerging LEO satellite constellations, or is that a pipe dream?

While not a full replacement for 5G/6G cellular broadband, D2C will be a critical part of the 6G connectivity ecosystem, ensuring truly global mobile coverage for the first time

  • The Growing Credibility of Direct-to-Cell (D2C) Services
    • LEO satellite-based D2C connectivity was once seen as unrealistic, but recent developments from Apple, SpaceX (Starlink), and AST SpaceMobile (ASTS) have changed perceptions.
    • ASTS is leading the way with purpose-built Block 2 BlueBird satellites, designed specifically for direct connectivity to unmodified 5G smartphones.
  • Case Study: AST SpaceMobile’s Technical Feasibility
    • Each ASTS BlueBird satellite features a massive 223 m² phased array antenna, capable of forming 2500 adjustable beams, each with 40 MHz bandwidth.
    • Calculated performance:
      • Beam footprint: ~18.5 km diameter (~269 km² coverage area)
      • Downlink spectral efficiency: ~3 bps/Hz
      • Max data rate per beam: ~120 Mbps
      • Example rural area scenario: 4035 potential smartphone users per beam, ~200 peak-active users
      • Broadband-level speeds (25 Mbps downlink) would only support ~5 users per beam, making true broadband infeasible.
      • If reduced to 3 Mbps per user, ~40 users per beam can be supported, making the service viable for light connectivity.
  • D2C Will Not Be True Broadband, But It Will Be Revolutionary
    • D2C will not match terrestrial broadband speeds, as satellite networks have much lower area spectral efficiency.
    • Expected 6G-era D2C capabilities:
      • 3G-like speeds (3-5 Mbps) for general users.
      • High-quality voice (85 kbps) and text coverage even in cars, trains, and some indoor areas.
      • Global connectivity for emergencies and underserved rural areas.
    • Cost comparison:
      • D2C will be ~50x more expensive per GB than terrestrial mobile networks, limiting its viability for heavy data use.
      • However, D2C coverage per km² is ~1000x cheaper than terrestrial networks, making it ideal for low-density rural coverage and government-subsidized connectivity.
  • Conclusion
    • D2C via LEO satellites will not provide full broadband speeds but will still be game-changing. It will enable:
    • Global, always-available voice & text connectivity.
    • Basic mobile data (3-5 Mbps) anywhere with a clear sky view.
    • Affordable rural & emergency connectivity, complementing terrestrial networks rather than replacing them.

[Q31]What role will the 6G cellular standard play in D2C?

Early D2C deployments bypass 3GPP NTN standards by modifying base station software to handle latency and Doppler shifts, allowing connectivity with existing LTE/5G phones. While proprietary solutions work for LEO satellites, future large-beam and high-orbit NTN services will benefit from standardized 6G NTN protocols. The FCC’s 2024 SCS framework now allows MNOs to use their terrestrial spectrum for D2C, paving the way for seamless terrestrial-satellite 6G networks.

  • Current D2C Deployments Bypass 3GPP NTN Standards
    • Early D2C providers (ASTS, Starlink) claim they don’t need 3GPP’s NTN enhancements from Releases 17 & 18 to support satellite connectivity.
    • Instead, they modify base station software (either in the satellite or on the ground) to compensate for:
      • High Round Trip Time (RTT) due to LEO distances.
      • Carrier Frequency Offset (CFO) caused by Doppler shifts.
    • This allows them to connect even pre-Release-17 LTE and 5G phones without requiring device modifications.
  • Why This Works (Short-Term Adaptations)
    • Satellite beams are narrow (~10 km radius), so D2C providers know user locations and latency with high accuracy (~20 µs).
    • They can adjust gNB software to overcome 3GPP-defined limitations, such as:
      • Random Access (RA) timing constraints: The gNB can shift its acceptance window for Message 1 responses.
      • Random Access Response (RAR) window expiry: 5G allows RAR windows of 20+ ms, which is sufficient for LEO D2C latencies.
    • As a result, these proprietary modifications allow efficient scheduling and access without requiring 3GPP NTN features.
  • Long-Term Need for 6G NTN Enhancements
    • While proprietary fixes work for LEO D2C, future NTN services with:
    • Larger beams
    • Higher orbits (MEO, GEO, or hybrid constellations)
    • Global voice & text services
    • ...will benefit from standardized 6G NTN protocols to ensure scalability and interoperability.
  • Regulatory Shift: FCC Greenlights D2C Using MNO Spectrum
    • A major regulatory challenge was whether D2C would be restricted to MSS (Mobile Satellite Service) bands (like Apple’s emergency messaging via Globalstar).
    • The FCC’s 2024 Supplemental Coverage from Space (SCS) framework allows MNOs to use their terrestrial spectrum for D2C, as long as:
      • It does not interfere with existing MSS bands.
      • It does not disrupt other MNO spectrum allocations.
    • This effectively opens the door for a fully integrated terrestrial-satellite cellular network.
  • Conclusion
    • In the short term, D2C providers will continue using proprietary workarounds to support existing LTE/5G phones without needing 3GPP NTN upgrades.
    • However, long-term standardization (likely in 6G) will be necessary for scaling global NTN services across different satellite orbits and operators.
    • Regulatory frameworks (such as FCC SCS) now allow MNOs to extend terrestrial spectrum into space, accelerating the vision of seamless terrestrial-satellite 6G networks.

[Q32]What about other non-terrestrial network (NTN) paradigms such as High Altitude Platform Stations (HAPS) for achieving global coverage?

High-Altitude Platform Stations (HAPS), including Google’s Project Loon and Facebook’s Aquila, were once considered viable solutions for delivering global broadband coverage but failed due to commercial infeasibility. The rapid decline in space launch costs (from $16,000/kg in 2011 to $2,500/kg in 2021 via SpaceX’s Falcon 9) and breakthroughs in LEO-based Direct-to-Cell (D2C) services made satellite-based NTN solutions far more cost-effective. While HAPS may still serve niche applications (e.g., temporary broadband for events or regional coverage), they are unlikely to achieve large-scale global connectivity compared to space-based NTNs like Starlink and AST SpaceMobile.

  • Early HAPS Efforts and Viability
    • HAPS concepts (e.g., high-altitude balloons, solar-powered drones) were explored by tech giants.
    • Google’s Project Loon (2011) and Facebook’s Aquila (2016) aimed to provide remote cellular connectivity.
    • At the time, space-based alternatives were too expensive, with launch costs around $16,000/kg in 2011.
  • The Shift to Space-Based NTNs
    • By 2021, the cost of space launches had dropped significantly, with SpaceX’s Falcon 9 reducing costs to ~$2,500/kg.
    • This made large-scale LEO satellite networks more viable, leading to the rise of Starlink, AST SpaceMobile, and other NTN solutions.
    • ASTS’s 2019 proof-of-concept for Direct-to-Cell (D2C) links from LEO further demonstrated that satellites could replace HAPS.
  • The End of Large-Scale HAPS Projects
    • Project Loon was shut down in 2021 due to a lack of commercial viability.
    • Facebook also abandoned its HAPS program, shifting focus to other connectivity projects.
    • Space-based alternatives became cheaper and more scalable, eliminating the need for continent-wide HAPS deployments.
  • Future Role of HAPS in 6G
    • HAPS may still be useful for localized or temporary broadband coverage, such as:
      • Emergency response and disaster relief.
      • Temporary network deployments for major events.
      • Supplementing connectivity in highly remote or maritime regions.
    • However, HAPS will not provide global broadband at scale, as LEO satellite networks are far more cost-effective and scalable.

[Q33]Is O-RAN going to be transformative, or will the operators stick with a more vertically integrated system?

O-RAN offers significant advantages by disaggregating the RAN, standardizing interfaces, and enabling greater flexibility, AI/ML integration, and cost reduction. However, it also faces challenges in complexity, performance, and security, which may limit its adoption in fully open, multi-vendor ecosystems. While RAN disaggregation is inevitable, operators will likely rely on a small set of trusted vendors and system integrators, balancing openness with performance, efficiency, and security.

  • The Promise of O-RAN
    • Disaggregation and standardization allow operators to mix and match vendors, reducing costs and increasing flexibility.
    • Open interfaces enable software virtualization and AI/ML-based optimizations, making networks more adaptive and efficient.
    • O-RAN facilitates a gradual AI-driven RAN evolution, avoiding the slow 3GPP standardization process while enabling modular AI adoption.
    • 3GPP and O-RAN will likely coordinate on 6G architecture, but major players like Huawei are pursuing alternative solutions.
  • RAN Disaggregation is Inevitable
    • 5G started RAN disaggregation with Distributed Units (DUs) and Centralized Units (CUs), which make sense even in single-vendor deployments.
    • Layer-2 processing on off-the-shelf (OTS) servers reduces costs and increases software agility.
    • O-RAN can reduce the "G staircase" by allowing continuous RAN software upgrades rather than major generational shifts.
  • Challenges of O-RAN Adoption
    • A. Complexity
      • Operators must manage multi-vendor integration, either in-house or through system integrators.
      • High-performance multi-vendor setups will be limited to well-tested combinations, reducing the competitive advantage of O-RAN.
    • B. Performance
      • Sole-vendor RANs can pass proprietary messages across modules, allowing deeper cross-layer optimization than multi-vendor O-RAN setups.
      • O-RAN running on virtualized OTS servers may consume significantly more energy than proprietary ASIC-based RANs, potentially offsetting cost savings.
    • C. Security
      • Open and modular architectures increase attack surfaces, making networks more vulnerable to cyber threats.
      • Operators and regulators may be wary of allowing third-party xApps/rApps with unrestricted access to RAN data, limiting AI-driven innovation.
  • 4. The Future of O-RAN in 6G
    • RAN disaggregation will dominate, but within a controlled ecosystem where only a few trusted vendors and integrators operate.
    • Third-party xApps and rApps will run in tightly controlled environments, similar to Apple’s App Store model, restricting deep access to network data.
    • Vendor/system integrator lock-in will persist because operators will prioritize security, energy efficiency, and high performance over full openness.

[Q34]What strategies can be implemented to enhance energy efficiency in 6G networks, considering the anticipated increase in data traffic and device connectivity?

Rather than solely focusing on maximizing performance, 6G should adopt a holistic approach to energy efficiency, ensuring sustainable network growth while minimizing power consumption. By prioritizing these strategies, 6G can achieve significant energy efficiency improvements while maintaining high performance and scalability.

  • Shift Toward Energy-Aware Network Design
    • 6G should optimize energy consumption at every layer of the network, from radio access to core infrastructure.
    • AI-driven power management techniques can dynamically adjust resource allocation based on real-time traffic demands.
    • The “6G Green Network Triangle” will replace traditional efficiency metrics, emphasizing:
    • Area energy efficiency (bits/Joule/m²)
    • Energy-aware cost models (bits/currency unit)
    • Adaptive power scaling for dynamic workloads
  • Harnessing AI and ML for Energy Optimization
    • Intelligent network orchestration will enable proactive energy-saving measures, such as predictive load balancing.
    • AI-driven traffic forecasting can optimize base station activation, reducing power usage during low-demand periods.
  • Leveraging New Materials and Hardware Innovations
    • Energy-efficient semiconductor technologies, such as ultra-low-power transceivers, will minimize power consumption.
    • Use of intelligent reflecting surfaces (IRS) can reduce transmission power requirements while maintaining coverage.
  • Network Virtualization and Edge Computing
    • Moving processing closer to end-users via edge computing can reduce energy-intensive backhaul transmission.
    • Virtualized network functions (VNFs) will allow dynamic scaling, reducing idle power consumption.
  • Sustainable Infrastructure Deployment
    • Renewable energy sources, such as solar and wind-powered base stations, should be integrated into 6G deployments.
    • Smart energy harvesting techniques (e.g., RF energy harvesting) can power IoT devices, reducing reliance on traditional power sources.

[Q35]In what ways can artificial intelligence be seamlessly integrated into 6G networks to optimize performance and enable intelligent decision-making?

Unlike previous generations, 6G will be deeply intertwined with AI at every layer, from physical infrastructure to network orchestration, ensuring real-time adaptability and self-optimization. By integrating AI seamlessly into every layer of 6G, networks will become more adaptive, efficient, and intelligent, supporting next-generation applications with unprecedented levels of automation and performance.

  • AI-Driven Network Automation and Self-Optimization
    • 6G networks will leverage AI to automate resource allocation, dynamically adjusting spectrum and power usage based on demand.
    • Self-optimizing networks (SON) will use real-time analytics to predict congestion and optimize traffic flow.
  • AI-Enhanced Radio Access and Spectrum Management
    • AI will enable dynamic spectrum sharing, allowing networks to allocate spectrum efficiently across different services and operators.
    • Reinforcement learning-based algorithms can optimize beamforming and MIMO configurations for better coverage and efficiency.
  • Predictive Maintenance and Fault Management
    • Machine learning will enable proactive network maintenance by predicting hardware failures before they occur.
    • AI-driven anomaly detection will enhance cybersecurity by identifying and mitigating threats in real time.
  • Edge AI for Ultra-Low Latency Applications
    • AI models deployed at the edge will reduce the need for centralized processing, enhancing response times for mission-critical applications.
    • Federated learning will allow distributed AI training without transmitting sensitive data to centralized cloud servers.
  • Energy Efficiency through AI Optimization
    • AI-based power management will optimize energy consumption, reducing the carbon footprint of 6G networks.
    • Intelligent sleep mode mechanisms will allow base stations to dynamically adjust power levels based on usage patterns.

[Q36]What innovative approaches can be employed to reduce the costs and complexities associated with 6G infrastructure deployment, especially in underserved areas?

Deploying 6G in underserved areas presents significant cost and logistical challenges. To ensure widespread accessibility, innovative approaches must be adopted to reduce expenses while maintaining network performance. By adopting these innovative approaches, 6G can overcome traditional deployment barriers, making advanced connectivity more accessible and affordable for underserved communities worldwide.

  • Leveraging Low-Cost and Sustainable Infrastructure
    • Use of low-cost, energy-efficient base stations powered by renewable energy sources (e.g., solar, wind) to reduce operational costs.
    • Deployment of software-defined networking (SDN) and virtualized network functions (VNFs) to minimize reliance on expensive proprietary hardware.
  • Utilization of Non-Terrestrial Networks (NTN)
    • Integration of satellite-based 6G coverage, particularly for rural and remote areas where fiber deployment is impractical.
    • High-altitude platform stations (HAPS) such as drones and balloons can provide temporary or supplementary network coverage.
  • Dynamic Spectrum Sharing and Open RAN Adoption
    • Implementing spectrum-sharing mechanisms to maximize efficiency without requiring exclusive, high-cost spectrum allocations.
    • Open RAN (O-RAN) architectures will enable interoperability, reducing dependence on costly vendor-specific equipment.
  • AI-Driven Network Optimization
    • AI-powered network management can optimize infrastructure usage, reducing the need for extensive manual configurations.
    • Predictive analytics will allow proactive maintenance, minimizing downtime and reducing long-term operational expenses.
  • Decentralized and Community-Driven Networks
    • Encouraging local and cooperative networks where communities manage small-scale 6G deployments using shared infrastructure.
    • Implementation of blockchain-based network management to reduce administrative overhead and enhance security.
  • Hybrid Connectivity Models
    • A mix of fiber, wireless backhaul, and satellite links can provide cost-effective connectivity in challenging terrains.
    • Use of mesh networking to extend coverage without requiring extensive additional infrastructure.

[Q37]How can regulatory and policy frameworks be adapted to support the rapid development and deployment of 6G technologies?

As 6G technologies advance, regulatory and policy frameworks must evolve to foster innovation, ensure security, and enable seamless global deployment. By adapting regulatory and policy frameworks in these areas, governments and industry stakeholders can facilitate the smooth and secure deployment of 6G technologies while fostering innovation and ensuring equitable global access.

  • Flexible Spectrum Allocation and Management.
    • Dynamic spectrum sharing policies should be implemented to maximize efficient use of available frequencies.
    • Governments should consider more flexible licensing models, including unlicensed and lightly licensed spectrum for 6G applications.
  • Global Standardization and Interoperability
    • International collaboration between regulatory bodies, telecom operators, and technology providers is essential to establish unified 6G standards.
    • Open RAN (O-RAN) initiatives should be encouraged to prevent vendor lock-in and foster a competitive ecosystem.
  • Security and Privacy Regulations
    • Stricter cybersecurity policies should be mandated, including AI-driven threat detection mechanisms for 6G networks.
    • Enhanced data protection laws should be established to regulate AI-driven analytics and prevent misuse of personal data.
  • Facilitating Infrastructure Deployment
    • Regulatory bodies should streamline permits for deploying small cells, fiber networks, and satellite-based 6G connectivity.
    • Incentives, such as tax benefits or public-private partnerships, should be provided to accelerate 6G rollout in underserved regions.
  • Sustainability and Energy Efficiency Policies
    • Regulations should encourage the use of green technologies, including renewable energy-powered base stations and energy-efficient networking equipment.
    • Carbon footprint monitoring of telecom operators should be standardized to promote eco-friendly network deployments.
  • Support for AI and Automation in 6G Networks
    • Policies should promote responsible AI usage in network management while ensuring transparency and accountability.
    • Guidelines for AI-driven spectrum allocation and automated network maintenance should be defined to enhance efficiency.

[Q38]What advancements in materials science and device engineering are required to support the high-frequency operations of 6G networks?

This would be mainly for subThz implementation. If 6G networks will operate in the sub-terahertz (sub-THz) and terahertz (THz) frequency bands, requiring significant breakthroughs in materials science and device engineering to overcome signal propagation challenges and enhance system efficiency. By advancing these material and device engineering technologies, 6G networks can overcome the physical limitations of high-frequency communications, enabling robust, efficient, and scalable wireless connectivity.

  • Development of High-Performance Semiconductor Materials
    • Use of compound semiconductors like gallium nitride (GaN) and indium phosphide (InP) for high-frequency, high-power applications.
    • Integration of 2D materials such as graphene and transition metal dichalcogenides (TMDs) to enhance signal processing and minimize losses.
  • Advanced Antenna Technologies
    • Implementation of metamaterial-based antennas to improve signal directivity and beamforming capabilities.
    • Use of reconfigurable intelligent surfaces (RIS) to counteract high path loss and enable dynamic signal reflection.
  • Efficient Thermal Management Solutions
    • Development of nanoengineered heat sinks and thermal interface materials to manage heat dissipation in high-power 6G devices.
    • Use of phase-change materials for adaptive cooling in compact transceivers and base stations.
  • High-Speed and Low-Power Transistor Innovations
    • Adoption of high-electron-mobility transistors (HEMTs) for faster signal processing at THz frequencies.
    • Research into spintronic and quantum transistors for ultra-low-power, high-speed data processing.
  • Next-Generation Photonics and Optical Communication
    • Integration of silicon photonics to enable ultra-fast, energy-efficient data transmission.
    • Use of plasmonic materials for compact, high-speed optical-electronic signal conversion.
  • Durable and Flexible Electronics for 6G Devices
    • Development of flexible and stretchable circuits for wearable and embedded 6G applications.
    • Use of self-healing materials to enhance device longevity and reliability.

[Q39]How can we effectively manage and integrate heterogeneous networks within the 6G framework to ensure seamless connectivity and service continuity?

6G will need to integrate a diverse set of network technologies, including terrestrial, satellite, and non-terrestrial networks, to provide seamless connectivity and ensure robust service continuity. Managing this complexity requires advanced coordination mechanisms and intelligent networking approaches. By implementing these strategies, 6G can seamlessly integrate heterogeneous networks, ensuring continuous, high-performance connectivity across diverse environments and applications.

  • AI-Driven Network Orchestration
    • Machine learning algorithms can dynamically optimize resource allocation across different network layers and technologies.
    • Reinforcement learning-based traffic steering will ensure optimal handovers between 6G, 5G, satellite, and Wi-Fi networks.
  • Software-Defined Networking (SDN) and Network Function Virtualization (NFV)
    • SDN enables centralized control, allowing seamless integration of different access networks.
    • NFV allows flexible deployment of network functions, enabling scalable service delivery across heterogeneous infrastructures.
  • Dynamic Spectrum Management and Sharing
    • Cognitive radio technologies can facilitate adaptive spectrum access for different network types.
    • Dynamic spectrum sharing will ensure efficient coexistence between terrestrial, satellite, and IoT networks.
  • Multi-Layered Network Architecture
    • Seamless interworking between terrestrial base stations, high-altitude platforms (HAPS), and satellite networks.
    • Edge computing integration to enable low-latency processing for time-sensitive applications.
  • Cross-Domain Security and Authentication Mechanisms
    • Blockchain-based identity management can provide secure authentication across different network domains.
    • Zero-trust security frameworks will ensure continuous validation of users and devices moving across heterogeneous networks.
  • Adaptive Quality of Service (QoS) Mechanisms
    • AI-powered QoS adaptation to balance bandwidth and latency requirements across different access technologies.
    • Network slicing to allocate dedicated resources for critical applications, such as autonomous vehicles and remote healthcare.

[Q40]What strategies can be implemented at the network level to achieve sub-millisecond latencies required for time-sensitive 6G applications?

6G will enable ultra-low latency applications such as holographic communication, industrial automation, and autonomous systems. Achieving sub-millisecond latencies requires a multi-faceted approach that optimizes every aspect of network architecture and communication protocols. By integrating these strategies, 6G will be able to support real-time applications with unprecedented reliability, ensuring seamless ultra-low-latency communication for next-generation services.

  • Edge Computing and Fog Networking
    • Distributed edge processing reduces data transmission time by processing workloads closer to users.
    • Fog computing enables hierarchical computing layers, minimizing round-trip latency for time-critical applications.
  • AI-Driven Network Optimization
    • Predictive traffic management uses machine learning to anticipate congestion and dynamically adjust network resources.
    • AI-based packet prioritization ensures mission-critical data is transmitted with minimal delay.
  • Advanced Radio Access Technologies
    • Terahertz (THz) and sub-THz communication reduce transmission delays with ultra-wideband data rates.
    • Massive MIMO and beamforming improve spectral efficiency, reducing the time required for data exchanges.
  • Optimized Network Slicing for Ultra-Low Latency
    • Dedicated network slices for time-sensitive applications, such as remote surgery and industrial automation.
    • Dynamic slice reconfiguration to adapt to fluctuating network conditions and maintain low latency.
  • High-Speed Backhaul and Fronthaul Networks
    • Optical fiber integration with 6G core networks ensures high-speed, low-latency data transport.
    • Wireless fronthaul using millimeter-wave (mmWave) and free-space optics (FSO) to reduce latency in mobile networks.
  • Cross-Layer Latency Optimization
    • Flexible MAC scheduling to minimize contention and transmission delays.
    • Ultra-reliable low-latency communication (URLLC) enhancements to further reduce packet transmission time.
  • Quantum Communication and Next-Gen Networking Protocols
    • Quantum entanglement-based communication could eliminate transmission delays in future ultra-fast networks.
    • Next-generation TCP/IP alternatives, such as Information-Centric Networking (ICN), can reduce protocol overhead.

[Q41]How can 6G systems be designed to ensure backward compatibility with existing networks, facilitating a smooth transition for users and operators?

Ensuring backward compatibility in 6G is critical for a seamless transition from 5G and earlier generations. A well-designed 6G system must support interoperability, phased upgrades, and coexistence with legacy networks to minimize disruptions for users and operators. By incorporating these strategies, 6G systems can ensure a smooth transition, enabling operators and users to adopt next-generation technologies without disruption.

  • Multi-Mode and Multi-Band Support
    • 6G devices should support multi-mode operation, allowing connectivity with 5G, 4G, and Wi-Fi networks.
    • Dynamic frequency band allocation should enable flexible spectrum usage across different network generations.
  • Flexible and Software-Defined Core Network
    • Cloud-native and software-defined networking (SDN) should be integrated to allow seamless interworking with existing 5G cores.
    • Network slicing extensions should allow 6G slices to coexist with 5G network slices.
  • Standardized Interoperability and Protocols
    • Cross-generation protocol adaptation layers should enable smooth communication between 5G and 6G networks.
    • Backward-compatible signaling mechanisms should be developed to support legacy devices.
  • AI-Driven Adaptive Networking
    • AI-powered traffic steering should enable intelligent handovers between 5G and 6G networks.
    • Machine learning-driven spectrum sharing should ensure efficient coexistence and avoid interference.
  • Infrastructure Reuse and Phased Deployment
    • Upgrade existing 5G infrastructure, such as base stations and antennas, for cost-effective 6G deployment.
    • Hybrid 5G/6G core networks should be implemented to allow gradual migration.
  • Unified API and Service Frameworks
    • Service orchestration frameworks should enable seamless application and service migration from 5G to 6G.
    • Backward-compatible APIs should be provided to allow smooth integration of new applications with legacy network functions.

And many other challenges

Followings are the list of challenges or Open Questions from other papers/white papers.

Source : 6G WHITE PAPER-ON VALIDATION AND TRIALS FOR VERTICALS TOWARDS 2030S

Reference

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