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Joint Communication and Sensing: Models and Potential of Using MIMO

Xinran Fang, Wei Feng, Yunfei Chen, Ning Ge, Yan Zhang

arXiv:2205.09409v2eess.SPcs.ITcs.SI

TL;DR

JCAS must reconcile communication and sensing despite their incompatible objectives and MIMO’s hardware, power, and processing burdens. This survey reviews MIMO’s roles, synthesizes coexistence and integration approaches, examines C-RAN, UAV, and RIS models, and identifies practical challenges and solutions for future JCAS networks.

  • Problem

    JCAS seeks to combine communication and sensing, but their incompatible objectives and MIMO’s overhead and processing complexity make balanced network design unresolved.

  • Method

    The survey reviews MIMO’s communication and sensing roles, existing coexistence and integration schemes, and JCAS models using C-RAN, UAVs, and RISs.

  • Results

    The review finds that MIMO primarily provides directional beamforming for communication and waveform shaping for sensing, while restricted spatial degrees of freedom complicate balancing their competing interests.

  • Takeaways & Limitations

    Practical JCAS development requires simple, robust ways to allocate restricted spatial degrees of freedom while addressing ubiquity, green operation, complexity, cooperation, and security.

  • Takeaways & Limitations

    Practical deployment remains constrained by imperfect CSI, synchronization issues, signal leakage, and the complexity of joint active and passive beamforming.

Abstract

from arXiv · show

The sixth-generation (6G) network is envisioned to integrate communication and sensing functions, so as to improve the spectrum efficiency (SE) and support explosive novel applications. Although the similarities of wireless communication and radio sensing lay the foundation for their combination, there is still considerable incompatible interest between them. To simultaneously guarantee the communication capacity and the sensing accuracy, the multiple-input and multiple-output (MIMO) technique plays an important role due to its unique capability of spatial beamforming and waveform shaping. However, the configuration of MIMO also brings high hardware cost, high power consumption, and high signal processing complexity. How to efficiently apply MIMO to achieve balanced communication and sensing performance is still open. In this survey, we discuss joint communication and sensing (JCAS) in the context of MIMO. We first outline the roles of MIMO in the process of wireless communication and radar sensing. Then, we present current advances in both communication and sensing coexistence and integration in detail. Three novel JCAS MIMO models are subsequently discussed by combining cutting-edge technologies, i.e., cloud random access networks (C-RANs), unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs). Examined from the practical perspective, the potential and challenges of MIMO in JCAS are summarized, and promising solutions are provided. Motivated by the great potential of the Internet of Things (IoT), we also specify JCAS in IoT scenarios and discuss the uniqueness of applying JCAS to IoT. In the end, open issues are outlined to envisage a ubiquitous, intelligent and secure JCAS network in the near future.

I. INTRODUCTION

JCAS combines communication and sensing to improve spectrum use and support dual-function applications, but balancing their incompatible requirements with MIMO remains unresolved.

  • JCAS can improve spectrum efficiency and provide dual-function services for intelligent transportation, smart factories, and IoT.
  • Spectrum scarcity and expanding frequency-band overlap have driven communication and sensing systems toward coexistence and integration.
  • MIMO improves communication rate and sensing resolution through spatial-domain resource use, while introducing hardware, power, overhead, and processing burdens.
  • The survey reviews coexistence and integration, defines MIMO-based JCAS models, and examines C-RANs, UAVs, RISs, and IoT deployments.

II. REVIEW OF THE POTENTIAL OF MIMO TECHNIQUES

MIMO evolved from communication capacity and space-time coding advances to distributed, massive, and reconfigurable architectures, while radar adopted arrays, digital processing, and waveform-diverse designs.

  • Communication MIMO progressed from capacity analysis and space-time codes to cooperative and massive MIMO architectures.
  • RISs use many low-cost, nearly passive units to provide large degrees of freedom and enhance communication links energy-efficiently.
  • Phased-array radar and digital signal processing established the foundations for modern MIMO sensing.
  • Hybrid phased-MIMO radars combine phased-array coherent gains with MIMO waveform diversity, and massive MIMO extends sensing capability.

B. Merits of MIMO in Communication Systems

MIMO communication benefits from spatial diversity, spatial multiplexing, and flexible beamforming, while MIMO radar uses diversity and adaptive waveforms for robust, high-resolution sensing.

  • Spatial diversity combats fading by providing multiple independent paths, reducing outage probability in communication systems.
  • Spatial multiplexing increases spectrum efficiency by transmitting multiple streams over parallel channel paths.
  • Flexible beamforming adapts directional, separable, and interference-suppressing beams to users and transmission conditions.
  • MIMO radar spatial diversity supplies independent observations that reduce missed-target probability and improve robustness to clutter.
  • Adaptive waveform manipulation directs power, suppresses clutter, separates targets, illuminates areas, and tracks moving targets.

D. Summary

JCAS MIMO must balance communication’s channel-oriented beamforming with sensing’s target-oriented waveform design under interference and waveform constraints.

  • D. Summary: MIMO mainly provides directional beamforming for communication and waveform shaping for sensing, whose incompatible interests require balanced design.
  • D. Summary: JCAS research progresses from spectrum-sharing coexistence toward integrated dual-function systems using MIMO to address communication–sensing trade-offs.
  • D. Summary: Coexistence designs require adaptations because radar waveforms impose requirements such as constant envelope and good correlation properties beyond SNR.

1) Communication Adaptation Schemes:

Communication adaptation schemes modify the communication system while leaving radar unchanged, using beamforming, precoding, power allocation, and waveform-aware optimization to manage interference and preserve sensing performance.

  • 1) Communication Adaptation Schemes:: Communication adaptation schemes modify communication systems while radar systems remain unchanged.
  • 1) Communication Adaptation Schemes:: Robust beamforming can maximize radar detection performance subject to user-rate constraints, while constructive interference can improve symbol discrimination instead of being eliminated.
  • 1) Communication Adaptation Schemes:: Radar-oriented designs use zero-forcing or relaxed null-space projection to suppress communication interference while reserving degrees of freedom for waveform shaping.
  • 1) Communication Adaptation Schemes:: Joint optimization addresses clutter, imperfect channel information, hardware distortions, waveform similarity, constant modulus, and direction-of-arrival estimation.
  • 1) Communication Adaptation Schemes:: MIMO coexistence designs use spatial separation, multi-antenna filtering, waveform shaping, and optimized power or filters to manage interference between communication and sensing.

4) Discussion:

The discussion contrasts MIMO-enabled coexistence and integration with their practical constraints, emphasizing cooperation requirements, hardware burdens, and communication limitations in radar-centric designs.

  • 4) Discussion:: MIMO separates communication and sensing spatially, supports receiver filtering and waveform shaping, but increases estimation, feedback, power, and processing burdens with antenna number.
  • 4) Discussion:: Current JCAS MIMO schemes generally require timely full CSI, continual adaptation, matched sampling and symbol timing, and accurate synchronization.
  • B. Communication and Sensing Integration: Unified hardware bypasses signaling, information exchange, and synchronization issues associated with separate communication and sensing configurations.
  • 1) Radar-Centric Designs:: Radar-centric integration embeds information through amplitude, phase, index, frequency, or hybrid modulation, while MIMO enables directional multi-user transmission and waveform diversity.
  • 1) Radar-Centric Designs:: Radar-centric designs retain sensing-oriented operation but communication remains limited by low rate, high outage probability, and weak serving ability.
  • 1) Radar-Centric Designs:: Fig. 2 illustrates communication–sensing trade-offs and transmit beam patterns across antenna numbers using mainlobe, sidelobe, waveform, user-direction, and symbol constraints.

2) Communication-Centric Designs:

Communication-centric JCAS reuses communication signals for sensing, but waveform randomness and competing objectives create a measurable communication–sensing trade-off. MIMO-enabled designs offer sensing opportunities while facing interference, synchronization, and deployment challenges.

  • Communication-centric sensing: Communication signals can support target detection and parameter estimation, including cm-level range and cm/s-level velocity accuracy with IEEE 802.11ad preambles.The reported accuracy relies on the preamble’s perfect auto-correlation property from Golay complementary sequences.
  • Communication-centric sensing: Random communication symbols produce high-range sidelobes after correlation, motivating waveform adaptations and dedicated sensing-oriented processing.Reported adaptations include modified OFDM waveforms, whole-array and bandwidth exploitation, and parameter selection for joint requirements.
  • Communication–sensing trade-off: User rate must compromise with sensing quality: stronger sensing performance reduces sum rate, whereas higher data-rate transmission reduces sensing accuracy.The example maximizes communication SINR subject to mutual-information or CRB-based sensing constraints.
  • Practical challenges: MIMO-enabled communication-centric designs use communication infrastructure and signals for sensing, but practical deployment still requires solutions for imperfect CSI, carrier offset, full-duplex leakage, and echo separation.OFDM carrier offset causes inter-carrier interference, while dual-function base stations must transmit and receive echoes simultaneously.

3) Novel Waveform Designs:

Novel waveform designs integrate communication and sensing through separate, shared, or unified hardware configurations. Their optimization exposes trade-offs between user SINR and sensing accuracy, while MIMO can provide extra spatial degrees of freedom to reduce conflicts.

  • Hardware configurations: Novel waveform designs span separate communication and sensing modules, separate RF chains sharing an antenna array, and totally unified hardware.The configurations progressively share antennas or modules to improve hardware, spatial, and energy utilization.
  • Design objectives: Novel designs address interference, waveform similarity, high peak-to-average power ratio, constant-envelope constraints, sidelobe control, power allocation, and physical security.Examples include radar-interference-aware precoding, joint communication–radar precoding with computation resources, NOMA, and artificial-noise optimization.
  • Performance trade-offs: CRB-oriented optimization achieves the minimal root-CRB among the compared schemes, while increasing user SINR generally increases root-CRB.The comparison covers three multiuser DFRC schemes with 16 transmit and 20 receive antennas, and a 30 dBw transmit-power setting.
  • Performance trade-offs: When the user number is small, such as k = 6, root-CRB increases only weakly with SINR because MIMO supplies surplus degrees of freedom to relieve conflicts.The comparison indicates that waveform similarity does not fully match real sensing metrics.
  • Hardware configurations: Unified hardware is the most cost-friendly configuration, while separate modules provide spatially separated beams and convenient access to communication and sensing information.Separate configurations use different RF chains and antenna arrays, whereas unified designs maximize hardware, space, and energy utilization.

4) Discussion:

JCAS remains an early-stage field whose practical progress depends on resolving system incompatibilities and high-dimensional optimization complexity. The paper also proposes exploiting communication–sensing interdependence rather than treating the functions solely as competitors.

  • Open challenges: JCAS integration is still in its infancy because radar-centric and communication-centric schemes are constrained by their pre-given primary functions.The discussion identifies the need for novel dual-function waveforms for longer-term integration.
  • Open challenges: FD operation, imperfect synchronization, and CSI acquisition remain open issues for practical JCAS deployment.These issues are identified as requiring further investigation alongside broader integration challenges.
  • Complexity: MIMO waveform design often becomes a non-convex, high-dimensional matrix optimization problem that is frequently NP-hard and costly even under convex approximation.The paper suggests intelligent methods that learn mappings from input information to output designs, avoiding troublesome optimization during implementation.
  • Future direction: Communication and sensing can form a reciprocal loop in which sensing assists channel estimation and beam-domain design, while communication improves sensing through signal- and data-level fusion.The proposed direction exploits interdependence instead of allocating limited degrees of freedom only to competing requirements.

IV. INTERPLAY OF MIMO-EMPOWERED JCAS AND CUTTING-EDGE TECHNOLOGIES

The paper presents three MIMO-enabled JCAS structures that combine distributed cooperation, UAVs, and passive RIS elements to expand spatial capabilities while introducing new coordination and complexity challenges.

  • IV. INTERPLAY OF MIMO-EMPOWERED JCAS AND CUTTING-EDGE TECHNOLOGIES: Three JCAS MIMO structures combine cooperative MIMO, dynamic UAV-assisted 3D MIMO, and hybrid active–passive MIMO.These structures exploit macro spatial degrees of freedom, aerial mobility, and RIS-assisted waveform shaping.
  • A. JCAS with Cooperative MIMO: Cooperative MIMO centrally processes signals from distributed nodes, enabling selective node activation, multi-perspective sensing, and separate communication-only or sensing-only tasks.This macro diversity improves both functions and provides more flexible configuration choices.
  • A. JCAS with Cooperative MIMO: C-RAN implements cooperative MIMO through distributed RRHs, a BBU for signal processing and resource allocation, and an MEC for scheduling and fabrication control.In the smart-factory example, different RRHs provide inspection, communication, or dual-function services, while echoes are jointly processed in the BBU.
  • A. JCAS with Cooperative MIMO: Extending C-RAN toward joint sensing, communication, computing, and control creates SC3 closed loops that provide end-to-end task solutions.The paper describes these feedback loops as supporting production control and potentially coordinating multiple intelligent machines.

B. JCAS with Dynamic 3D MIMO

Dynamic 3D MIMO uses UAV mobility and trajectory-aware signaling to complement terrestrial sensing, while RISs refine communication and sensing waveforms through passive beam control. These approaches improve flexibility but require predictive design, difficult channel acquisition, and costly joint optimization.

  • B. JCAS with Dynamic 3D MIMO: UAVs complement terrestrial base stations by providing maneuverable aerial observations and adapting sensing beams and signaling to their trajectories.The sea-task illustration depicts different communication and sensing schemes across time slots.
  • B. JCAS with Dynamic 3D MIMO: UAV energy limitations make timely CSI unavailable, so dynamic-MIMO JCAS design must be predictive and process-oriented.This constraint motivates offline optimization when planning UAV actions.
  • B. JCAS with Dynamic 3D MIMO: Jointly optimizing transmit precoding, UAV trajectory, and sensing start time can maximize user rate under a sensing-beam-pattern constraint.The cited scheme lets communication occupy the full frame while detection uses only part of it.
  • C. JCAS with Hybrid Active and Passive MIMO: RIS-assisted JCAS refines low-quality waveforms by amplifying mainlobes and suppressing sidelobes, improving compatibility between communication and sensing functions.The RIS adjusts its elements to reshape the transmitted waveform without being an active transmitter.
  • C. JCAS with Hybrid Active and Passive MIMO: RIS deployment remains difficult because cascaded CSI and environmental data are challenging to acquire, while joint active–passive optimization has constant-modulus and discrete-phase constraints.The paper notes that current approximate solutions still have polynomial-level computational loads.

D. Discussion

The discussion frames JCAS in IoT as a setting requiring tailored, cooperative, and resource-aware designs across ubiquitous deployments, constrained devices, and integrated computing and control. It highlights unresolved trade-offs between efficiency, cost, energy, reliability, and complexity.

  • D. Discussion: Global IoT coverage requires airborne and spaceborne JCAS because remote seas, mountains, and deserts are difficult to serve with terrestrial mesh networks.These scenarios introduce long transmission latency and large Doppler shifts as important constraints.
  • D. Discussion: Deploying JCAS on IoT nodes requires reducing computing and hardware requirements while developing energy-efficient waveforms and lower-complexity processing.The paper identifies green operation and complexity reduction as central design concerns for low-cost, battery-powered nodes.
  • D. Discussion: IoT cooperation lets sensing nodes exchange data with a central unit and form virtual arrays that exploit both micro- and macro-spatial degrees of freedom.This cooperation reflects the cost and energy limitations that prevent many IoT devices from operating independently.
  • D. Discussion: JCAS in IoT must be adapted to specific settings while addressing ubiquity, green operation, complexity, and cooperation.The paper emphasizes that maintaining communication and sensing performance under these constraints remains open.
  • D. Discussion: SC3 integration extends JCAS beyond communication and sensing toward computing and control, requiring task-oriented objectives that describe their relationships.The paper presents SC3 co-design as an end-to-end approach for supported tasks.

VI. OPEN ISSUES AND FUTURE DIRECTIONS

Future JCAS research must address uncertainty, intelligence, security, and integration with other technologies. Space-air-ground integration is highlighted as a route to wider coverage but introduces heterogeneous-link and orchestration challenges.

  • VI. OPEN ISSUES AND FUTURE DIRECTIONS: Future JCAS work is expected to emphasize intelligence, security, and interaction with other cutting-edge technologies.The paper presents these directions against substantial uncertainty because communication–sensing integration is still developing.
  • A. JCAS in Integrated Space-Air-Terrestrial Network: Space-air-ground integrated networks can extend communication and sensing coverage but must accommodate distinct rate, latency, and reliability characteristics across links.Coupling communication–sensing integration with space-air-ground integration creates additional system-design challenges.
  • A. JCAS in Integrated Space-Air-Terrestrial Network: A hierarchical architecture based on minimal space-air-ground JCAS models could orchestrate larger hybrid systems while preserving basic platform and functionality relationships.The paper proposes combining and agilely orchestrating basic models to analyze complex deployments.

B. JCAS Using Artificial Intelligence

AI is presented as a possible way to reduce the computing burden of MIMO-based JCAS, while future systems must also address security, intelligent environments, and broader closed-loop machine intelligence.

  • B. JCAS Using Artificial Intelligence: AI may reduce the computing burden of MIMO design by directly outputting JCAS schemes from raw data, although extracting high-level information remains difficult.The learning process is described as a black box, while its output policies may still provide heuristics for theoretical analysis.
  • B. JCAS Using Artificial Intelligence: The survey envisions intelligent 6G networks built around closed loops linking sensing, communication, computing, and control, but calls for a unified theoretical model.Such loops are intended to let machines adapt to their environments and perform tasks automatically, while optimization spans information, estimation, and control theory.
  • B. JCAS Using Artificial Intelligence: JCAS security remains open because environmental interaction can imprint surrounding information onto waveforms and expose it to eavesdropping.Targets are not authenticated like communication users and may be malicious, creating a need to limit information leakage while illuminating targets.
  • B. JCAS Using Artificial Intelligence: Combining JCAS with RISs or ambient backscatter could make propagation environments controllable, but their relationship with communication and sensing remains unknown.The paper characterizes smart-radio-environment JCAS research as still in its infancy.
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