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Twelve Scientific Challenges for 6G: Rethinking the Foundations of Communications Theory

Marwa Chafii, Lina Bariah, Sami Muhaidat, Merouane Debbah

arXiv:2207.01843v3cs.ITeess.SP

TL;DR

6G communication theory must address degraded spectral efficiency, delay, nonlinear hardware effects, and scalable coordination in large wireless systems. This paper revisits communication principles across non-coherent mechanisms, integrated sensing and communications, multi-agent learning, semantic communication, and queuing–information-theory interactions, identifying research directions for the field.

  • Problem

    Existing linear, Gaussian, block-by-block, and independently layered approaches become limited under 6G conditions such as nonlinear hardware, non-Gaussian noise, bursty traffic, and massive-scale coordination.

  • Method

    The paper reconsiders communication paradigms and surveys twelve scientific challenges spanning signal processing, sensing, multi-agent systems, semantic communication, non-coherent mechanisms, and cross-layer theory.

  • Results

    The paper identifies research opportunities and open questions for rebuilding communications theory around large-scale 6G networks and their emerging requirements.

  • Takeaways & Limitations

    Future 6G theory must revisit non-coherent communication, integrate sensing with communication, develop scalable multi-agent learning, incorporate semantic information, and connect queuing with information theory.

Abstract

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The research in the sixth generation of communication networks needs to tackle new challenges in order to meet the requirements of emerging applications in terms of high data rate, low latency, high reliability, and massive connectivity. To this end, the entire communication chain needs to be optimized, including the channel and the surrounding environment, as it is no longer sufficient to control the transmitter and/or the receiver only. Investigating large intelligent surfaces, ultra massive multiple-input multiple-output, and smart constructive environments will contribute to this direction. In addition, to allow the exchange of high dimensional sensing data between connected intelligent devices, semantic and goal oriented communications need to be considered for a more efficient and context-aware information encoding. In particular, for multi-agent systems, where agents are collaborating together to achieve a complex task, emergent communications, instead of hard coded communications, can be learned for more efficient task execution and communication resources use. Moreover, new physics phenomenon should be exploited such as the thermodynamics of communication as well as the the interaction between information theory and electromagnetism to better understand the physical limitations of different technologies, e.g, holographic communications. Another new communication paradigm is to consider the end-to-end approach instead of block-by-block optimization, which requires exploiting machine learning theory, non-linear signal processing theory, and non-coherent communications theory. Within this context, we identify twelve scientific challenges for rebuilding the theoretical foundations of communications, and we overview each of the challenges while providing research opportunities and open questions for the research community.

I. INTRODUCTION

6G research is framed as a need to revisit communication theory because emerging applications demand extreme performance and connected intelligence. The paper surveys twelve scientific challenges spanning physical limits, signal processing, learning, sensing, large-scale systems, and finite-length information theory.

  • Contribution: The paper distinguishes its contribution from application- and technology-oriented 6G surveys by rebuilding theoretical foundations and outlining research opportunities and open questions.Its stated goal is to provide a roadmap for addressing the question of what 6G will be.
  • Scientific challenges: The survey examines electromagnetic information theory, nonlinear signal processing, multi-agent learning, super-resolution, thermodynamics, large-scale systems, and non-equilibrium information theory.These challenges address electromagnetic limits, nonlinearities, distributed intelligence, sensing reconstruction, energy limits, scalable modeling, and finite-length operation.
  • 6G vision: 6G targets extremely high data rates, ultra-low latency, high reliability, massive connectivity, and connected intelligence across digital, physical, and human worlds.The envisioned system also integrates native AI, communication, computation, and sensing services.
  • Communication paradigms: Semantic communication addresses how connected intelligent entities can exchange representations of massive sensing data for smart and efficient interaction.Multi-agent interaction can involve sensing, exchanging representations, acting, and receiving environmental feedback.

III. ELECTROMAGNETIC INFORMATION THEORY

Electromagnetic information theory studies communication limits jointly with electromagnetic laws, including how Maxwell-constrained fields determine capacity and degrees of freedom. It frames emerging technologies as ways to increase usable spatial dimensions while exposing unresolved theoretical constraints.

  • Electromagnetic information theory connects information-theoretic principles with antenna engineering under electromagnetic-law constraints.It examines the interaction between wave physics and information theory.
  • Spatial capacity maximizes mutual information over both transmitted signals and electromagnetic-field distributions.Its constraints include total power, wave equations, and boundary conditions.
  • Maxwell-constrained capacity optimization can vary the channel G(E), for example by changing antenna positions.The explicit form of the Maxwell constraint is not known.
  • The generalized wireless channel G(E) combines transmit and receive arrays, a wireless link, and multipath propagation described through steering vectors and fading gains.The number of multipath components and their arrival and departure angles characterize the physical channel.
  • EIT is presented as a tool for assessing physical limits and advancing technologies intended to increase the channel’s degrees of freedom for 6G.The discussion includes emerging directions that address growing data-traffic demands.

A. Reconfigurable Intelligent Surfaces

This section surveys electromagnetic and nonlinear effects relevant to emerging 6G communication technologies, emphasizing that conventional information-theoretic and linear models can become inadequate. It discusses RIS, time reversal, orbital angular momentum, and nonlinear signal-processing challenges.

  • A. Reconfigurable Intelligent Surfaces: RISs manipulate electromagnetic waves by controlling the amplitude, phase, and frequency of RF signals through reflective elements.Adjusting element phases can shape propagation for energy-efficient and reliable transmissions.
  • A. Reconfigurable Intelligent Surfaces: Time reversal uses rich multipath environments for spatial-temporal focusing, but insufficient symbol duration causes intersymbol interference.Multiuser settings add inter-user interference, motivating signature-waveform design based on channel information.
  • A. Reconfigurable Intelligent Surfaces: Time reversal can realize virtual massive MIMO with a single antenna while offering energy efficiency and scalability.EIT is proposed for determining its performance limits, especially its degrees of freedom.
  • A. Reconfigurable Intelligent Surfaces: Orbital angular momentum encodes information through helical wavefronts and theoretically provides infinitely many orthogonal modes in a single beam.The passage presents OAM as an electromagnetic degree of freedom for transmission.
  • A. Reconfigurable Intelligent Surfaces: Current tools do not fully characterize RIS, time reversal, OAM, and ultra-massive MIMO because they rely on physically inconsistent simplifying assumptions.The cited assumptions include scalar quantities, far-field propagation, and planar wavefronts.

B. Non-linearities in Communications Systems

6G-wideband hardware and non-Gaussian environments make nonlinear effects significant, challenging conventional linear signal-processing assumptions. The section presents model-based and data-driven approaches while noting unresolved generalization and guarantee problems.

  • B. Non-linearities in Communications Systems: Standard transceivers combine linear coding and waveform operations with nonlinear mapping and finite-quantization DAC and ADC stages.These stages introduce nonlinear transforms and quantization noise across the communication chain.
  • B. Non-linearities in Communications Systems: The effective transceiver model maps digital transmitted sequences to received sequences through a nonlinear transform F_h with additive noise.The model groups transmitter, channel, and receiver effects at the digital-processing interface.
  • C. MLD Problem in non-Gaussian Noise: Minimum-distance detection is optimal for Gaussian noise but can be non-optimal for arbitrarily distributed or non-Gaussian noise.The exact maximum-likelihood detector is hard to solve for impulsive noise, and Gaussian-based MMSE methods can deviate from exact nonlinear MMSE.
  • D. Non-linear Signal Processing Approaches: Nonlinear transceiver design can use mathematical modeling or end-to-end machine learning to handle combined hardware and channel effects.Learned models may fail when hardware or channel conditions change, while deep learning lacks general performance guarantees or bounds.

V. MULTI-AGENT LEARNING SYSTEMS

Multi-agent learning systems use autonomous agents that collaborate through learned interaction, but large dynamic wireless networks make agent organization, scalability, and communication efficiency difficult. Emergent communication is proposed to reduce feedback and overhead by learning task-relevant messages rather than relying on hard-coded protocols.

  • Challenges: Conventional feedback mechanisms become inadequate in massive MIMO and large-scale connectivity because pilot transmission and CSI feedback consume wireless throughput resources.
  • Multi-agent learning systems: Multi-agent systems coordinate autonomous agents that learn collaboratively, solve complex tasks, and make decisions through interaction with one another and their environment.
  • Challenges: Fixed agent formation requires selecting, organizing, and maintaining an arrangement suited to each task, which is difficult in highly dynamic large-scale wireless networks.
  • Challenges: Dynamic agent behavior can require frequent sensing, reconnection, and retraining, increasing processing and communication overhead and draining onboard resources.
  • Emergent Communications: Emergent communication lets agents learn messages and communication protocols whose meaning and syntax arise through interaction, rather than being hard-coded.
  • Summary: Emergent communication can potentially reduce communication costs and processing overhead while helping agents coordinate complex tasks in partially observable environments.

B. Super-resolution Techniques in Wireless Systems

Super-resolution methods infer fine-grained wireless parameters such as delay, Doppler, and angles from coarse or indirect measurements, but noise, nonlinearity, hardware impairments, and resolution limits remain central challenges.

  • Frequency-response measurements estimate multipath delays τl and gains hl, supporting parameter recovery for applications such as localization.
  • Sparse and atomic-norm formulations estimate multidimensional parameters, but common formulations can ignore noise and rely on assumptions about signal structure.
  • Available approaches impose resolution requirements linking bandwidth with time resolution and array geometry with angular resolution, even under noise-free assumptions.
  • Research opportunities include performance limits under noise and nonlinearity, measurement fusion across bands and sensors, error distributions, and machine-learning-based super-resolution.
  • Super-resolution can infer delay, Doppler, and angles from radio signals for high-precision positioning, but hardware impairments and interference challenge conventional methods.

B. Joint Communication and Computation Co-design

Joint communication and computation co-design treats distributed applications as coupled processing and networking functions, optimizing their communication, computation, latency, energy, and hardware requirements together.

  • The connect-compute platform links input/output terminals, processing units, and network units across wired and wireless technologies.
  • An application function maps input data di to output data do, while receiver functions invert transmitter functions to deliver processed data.
  • Co-design must account for transmission technology, distance, data rate, analog frontends, and propagation through the channel.
  • Communication requirements depend on channel conditions and jointly shape analog functions, DSP functions, complexity, and energy consumption.
  • Preprocessing that compresses input data relaxes communication requirements by reducing the data rate that must be transmitted.

2) Digital video transmission example:

Digital video transmission illustrates how sensing, compression, communication, decoding, computation, latency, and energy interact across an end-to-end application pipeline.

  • A camera converts an optical scene into sampled, quantized bits, while a display reconstructs signals to drive visual elements.
  • 2.986 × 10^9 bps is the raw full-HD video rate for 1920 × 1080 pixels, 60 Hz refresh, and 8-bit quantization.
  • A 1/216 compression ratio reduces the example video rate to Rc = 13.815×10^6, lowering storage and transmission bandwidth requirements.
  • Lossy compression cannot perfectly reconstruct raw data, so perceived quality and the computation required for high fidelity become design considerations.
  • End-to-end latency spans acquisition, buffering, communication, processing, propagation, and server components, while energy depends on frontend and processing demands.

2) Physical limits of communication and computing:

The paper frames communication and computing as subject to physical limits, while emphasizing that mobile 6G channels and applications require joint optimization under time-varying propagation constraints.

  • Physical-limit analysis asks for the minimum energy to transmit a bit over a distance and perform an operation within a specified time.
  • Communication limits depend on power, channel geometry, and cross-sectional area, while computational limits depend on the operations required by an algorithm.
  • A joint communication-computation framework can evaluate energy costs and adapt optimization to application, resource, architecture, and hardware constraints.
  • Mobility makes wireless channels doubly selective in time and frequency, motivating channel estimation, tracking, and robust waveform design.
  • Coherence time characterizes temporal variation, while coherence bandwidth distinguishes frequency-selective from frequency-flat channels.

B. Spreading Waveforms

Spreading waveforms distribute data symbols across time and frequency to provide equalized channel gains, particularly in selective and time-varying channels. Their design balances performance, implementation complexity, orthogonality, and resource requirements.

  • Design rationale: The equal gain criterion spreads data symbols across the channel’s selective domain so all symbols experience the same gain.This criterion assumes receiver-side CSI and a perfect feedback equalizer.
  • Design rationale: Block multiplexing divides the data vector into sub-blocks so symbols reach the receiver with the same gain despite time-selective channels.Perfect equal gain is prevented by unpredictable channel variation, but BM waveforms satisfy the criterion when the channel is practically static during one sub-block.
  • Waveform construction: Full spreading precodes data in frequency, applies an IFFT, then spreads the resulting signal over time, producing symbols distributed across bandwidth and the time frame.Sparse spreading limits symbol energy to coherence bands and intervals rather than all subcarriers and subblocks.
  • Design conditions: Orthogonal spreading matrices prevent transmitter-induced symbol coupling, while equal-magnitude BM coefficients distribute data equally across subblocks.FFT- or WHT-based transforms support low-complexity transmitted-signal implementation.
  • Complexity and performance: Walsh-Hadamard and sparse spreading waveforms match OTFS FER while reducing modem complexity, and BM-OCDM outperforms OTFS for CP-free transmissions.The waveform matrix expressions and complexities are summarized in Table III.

C. Challenges

The section frames 6G communication theory around adaptable waveforms, semantic communication, and challenges created by mobility, computational demands, and the limits of conventional symbol-centric communication.

  • B. Spreading Waveforms: Spreading waveforms improve resilience to time variation and frequency selectivity, but iterative receivers create substantial complexity that hinders practical deployment.Approximate LMMSE matrices are being investigated to reduce receiver complexity.
  • B. Spreading Waveforms: Channel-estimation accuracy must be balanced against control overhead through appropriate frame and pilot allocation design.The required overhead should be related to target reliability for defined system parameters.
  • B. Spreading Waveforms: Future radios should adapt waveform, modulation, coding, spectrum band, and transmission architecture to channel selectivity and available resources.The Gearbox PHY concept describes switching among such radio “gears” according to channel conditions and communication resources.
  • IX. Semantic Communication Theory: Semantic communication shifts emphasis from reliably transmitting symbols toward enabling machines to understand message meaning and each other’s behavior.Its architecture is presented for machine-centric communication and human-oriented tasks.
  • IX. Semantic Communication Theory: A central open question is whether intelligent network nodes can develop common understanding for meaningful communication without human intervention.The section connects this question to pervasive service-based networks and knowledge- or experience-based applications.

A. Challenges

Integrated sensing and communication combines radar and wireless communication through shared hardware, spectrum, or signals, while semantic communication faces demanding fidelity, computation, and theoretical challenges.

  • IX. Semantic Communication Theory: Holographic-type semantic communication requires close-to-real remote presence, creating a need for data rates on the order of terabits per second.High-fidelity holograms motivate this extreme data-rate requirement.
  • IX. Semantic Communication Theory: Semantic information acquisition relies on clustering, identification, classification, and recognition, but deep neural networks require substantial computation and labeled data.These requirements form a key efficiency challenge for semantic communication frameworks.
  • IX. Semantic Communication Theory: Semantic communication research must address multiple objects, multi-user communication, semantics generalization, performance bottlenecks, and integration across wireless use cases.The section identifies these as continuing research directions.
  • X. Integrated Sensing and Communication: ISAC combines radar and communications using a shared reconfigurable front end, shared spectrum, or a joint signal, motivated by their common electromagnetic architecture.These integration levels are relevant to applications such as autonomous vehicles and industrial UAVs.
  • X. Integrated Sensing and Communication: ISAC waveform design must serve communication decoding and radar environmental probing, with monostatic systems additionally requiring transmitter self-interference cancellation.Bistatic systems reduce interference through transmitter-receiver isolation but require suitable reference-signal design.
  • X. Integrated Sensing and Communication: ISAC evaluation should combine sensing-estimation bounds such as the Cramer-Rao bound with communication capacity limits.The paper also discusses detection, false-alarm, localization, channel-modeling, and computational-electromagnetics considerations.

E. Impact of Integrated Sensing on the Communications performance

The section examines how sensing affects communication-centric ISAC systems, including radar fusion, joint signal models, PHY-layer security, and waveform design under imperfect CSI.

  • Impact of Integrated Sensing: Communication-centric ISAC adds radar functionality to an existing communication system, making the impact of integrated sensing on communication performance a central concern.Algorithms and hardware are then optimized primarily to support communications while adding sensing.
  • Radar fusion: Radar fusion combines communication bounces with radar echoes and other sensor information to improve confidence in sensing-parameter estimation and environmental control.A monostatic base station can process a probing signal to estimate target angle and delay.
  • Radar fusion: Joint sensing can exploit uplink communication signals and target reflections alongside the base station’s probing signal.The received uplink contains user transmissions and target-bounce components characterized by communication and propagation delays.
  • PHY Layer Security for ISAC: ISAC waveforms expose both communication and sensing information, increasing the priority of PHY-layer security against sophisticated eavesdroppers.An obfuscated physical channel can permit user communication while requiring authorization to access sensing parameters.
  • Imperfect Channel State Information: Imperfect CSI arises from limited channel estimates and RF-front-end imperfections such as nonlinearities and saturation, affecting both DFRC communications and radar.Joint waveform design uses a shared signal for the communication and radar models.
  • Imperfect Channel State Information: The joint optimization imposes communication outage and power constraints while shaping radar output power, yielding rank-1 solutions after relaxations and approximations.Closed-form single-user solutions and trade-offs depending on outage parameters are also reported.
  • Imperfect Channel State Information: When a target approaches a communication user, SINR-maximization effort is obtained through the radar metric, with proximity determined by an outage-parameter-dependent correlation threshold.The paper identifies this as a trade-off between communication and radar objectives.

I. Summary

The paper presents large-scale wireless networking as a central 6G challenge and surveys mathematical tools for modeling, analyzing, and optimizing such systems. It also highlights distributed optimization and tensor methods for scalable network intelligence.

  • Integrated sensing and communication: Integrated sensing and communication must address self-interference in monostatic radar and eavesdropping risks in secure ISAC designs.A combined antenna-isolation, analog-suppression, and digital-suppression design achieved more than 70 dB self-interference suppression; future designs must also consider multicellular coordination.
  • Large-scale networks: Vertical networks spanning underwater, ground, air, and space increase management and coordination difficulty as deployments become massive and heterogeneous.The paper motivates scalable mechanisms for large network scenarios involving human- and machine-based communications.
  • Mathematical tools: Random matrix theory models and optimizes large wireless systems by characterizing eigenvalues and eigenspaces of large random matrices.Its applications include antenna, spectrum, precoder, user, and cell dimensions.
  • RMT and deep learning: Random matrix theory is proposed as a way to understand and optimize large neural networks whose internal operations are difficult to analyze.The paper suggests using it to study depth and weight initialization for improved learning in large-scale environments.
  • Mathematical tools: Decentralized stochastic optimization enables resource-constrained devices to cooperatively solve network-wide problems without centralized coordination.It combines stochastic approximation with gossip-based information exchange to reduce the limitations of centralized optimization in dense networks.
  • Mathematical tools: Tensor algebra and low-rank tensor approximations address large multiobjective optimization problems by decomposing them into smaller, less complex subproblems.The approach is motivated by conventional optimization algorithms becoming unscalable as datasets grow and diversify.

D. Summary

This section revisits coding and cross-layer communication design for 6G requirements that challenge separated, fixed, and independently optimized schemes. It advocates adaptive joint coding and scheduling based on both physical-layer conditions and application or queue information.

  • Channel coding: 6G channel coding must move beyond one-size-fits-all schemes because heterogeneous use cases impose diverse quality-of-service requirements.Future codes are expected to be flexible and adaptive across scenarios rather than requiring increasingly complex scheme selection.
  • Channel coding: Joint source-channel coding is envisioned to support Tbps rates, sub-200-bit code lengths, and low-complexity decoding.The expected gains come with higher compression complexity, minimum-distance optimization, and sophisticated hardware requirements.
  • Cross-layer scheduling: Cross-layer optimization combines MAC scheduling with physical-layer information because independently treating these layers can produce suboptimal solutions for URLLC.Scheduling must account for both channel conditions and source statistics while meeting application quality-of-service constraints.
  • Cross-layer scheduling: The cross-layer scheduler produces rate and power allocation vectors at each time slot from current channel-state and queue-state information.The resulting policies are constrained by physical-layer capacity, power limits, and application-layer quality-of-service requirements.
  • Cross-layer scheduling: For delay-insensitive applications, the utility objective maximizes achievable system capacity through average user throughput.For delay-sensitive applications, the objective instead concerns system stability or delay minimization.

B. Multiuser Physical Layer Model

The multiuser physical-layer model defines feasible rate regions under channel and power constraints and connects them to cross-layer scheduling decisions. Its reported comparisons show that scheduling benefits from both channel and buffer-state information.

  • Capacity regions: A feasible rate tuple permits reliable transmission for all users, while the capacity region is the closure of all achievable rate regions.In downlink systems this is described by a broadcast region.
  • Capacity regions: For degraded broadcast channels, the instantaneous capacity region is conditioned on channel gains and constrained by total transmit power.For non-degraded broadcast channels, dirty paper coding determines the capacity region.
  • Capacity regions: Uplink multiple-access capacity regions are achieved through successive interference cancellation.The region is specified over user subsets under the channel conditions.
  • Scheduling comparisons: CSI-aware scheduling outperforms regular scheduling because channel knowledge enables exploitation of multiuser diversity.The comparison is stated as a qualitative performance advantage rather than a numerical gain.
  • Scheduling comparisons: Buffer-aware scheduling achieves better average delay performance than scheduling without buffer information.Buffer status is particularly valuable when the objective is average-delay minimization.
  • Delay minimization: Finite buffers make information-theoretic water filling non-optimal because optimal cross-layer scheduling must adapt to both CSI and buffer status.The water-filling optimum relies on an assumption of large buffer size.
  • Implementation challenges: Practical deployment faces communication overhead from frequent allocation updates and high complexity because general cross-layer problems are usually NP-hard.These challenges remain before successful deployment of cross-layer optimization.

D. Summary

The paper argues that large-scale, dynamic 6G networks require revisiting coherent communication, coding, and cross-layer foundations. It presents non-coherent modulation and cross-layer theory as parts of a broader program for rethinking communication theory.

  • Non-coherent communication: Massive growth in antennas, users, and nodes makes CSI acquisition increasingly costly and motivates a shift toward non-coherent communication.The paper identifies Grassmannian and tensor-based non-coherent modulation as responses to this scaling pressure.
  • Non-coherent communication: Grassmannian signaling can provide close-to-optimal pilot-free detection at moderate-high SNR, especially in rapidly varying channels.Information is conveyed using tall unitary matrices representing fixed-length binary sequences.
  • Non-coherent communication: Grassmannian constellation design remains difficult because curved-space optimization and very large unitary constellations increase computational complexity.Efficient labeling is also challenging, motivating machine-learning-based and geometry-aware design methods.
  • Massive connectivity: Unsourced random access supports massive IoT connectivity by allowing randomly activated devices to transmit short packets without known device identities.The setting is motivated by strict on-board power and limited-spectrum constraints.
  • Massive connectivity: Non-coherent tensor-based modulation encodes each user message in a rank-1 tensor for reliable, uncoordinated uplink access at massive scale.The tensor representation provides higher degrees of freedom than ordinary matrices.
  • Conclusion: The paper concludes that 6G theory must integrate non-coherent communication, sensing and communication, multi-agent learning, semantic transmission, and cross-layer queueing-information-theoretic design.It also examines electromagnetic limits and energy-performance balance in future networks.
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