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Enabling Joint Communication and Radar Sensing in Mobile Networks -- A Survey

J. Andrew Zhang, Md Lushanur Rahman, Kai Wu, Xiaojing Huang, Y. Jay Guo, Shanzhi Chen, Jinhong Yuan

arXiv:2006.07559v4eess.SPcs.NI

TL;DR

Large-scale radio sensing is limited by existing sensing infrastructure, while mobile networks offer broad coverage and reusable communication infrastructure. This survey examines how JCAS can evolve such networks into PMNs, reviewing architectures, sensing operations, signal choices, system modifications, research challenges, and open problems. It concludes that PMNs offer a framework for ubiquitous sensing, but practical realization remains constrained by complex mobile signals, sensing ambiguities, and communication–sensing trade-offs.

  • Problem

    Existing sensing systems have limited coverage and high infrastructure costs, while communication-only mobile networks do not yet provide integrated radio sensing.

  • Method

    The paper surveys PMN research, covering JCAS designs, PMN infrastructure and sensing operations, required network modifications, nine research topics, solutions, and open problems.

  • Results

    The survey synthesizes PMN capabilities, sensing methods, performance findings, and unresolved technical challenges for integrating sensing into mobile networks.

  • Takeaways & Limitations

    PMNs could provide ubiquitous radio sensing by reusing mobile-network infrastructure and signals while supporting simultaneous communication and sensing.

Abstract

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Mobile network is evolving from a communication-only network towards one with joint communication and radar/radio sensing (JCAS) capabilities, that we call perceptive mobile network (PMN). In PMNs, JCAS integrates sensing into communications, sharing a majority of system modules and the same transmitted signals. The PMN is expected to provide a ubiquitous radio sensing platform and enable a vast number of novel smart applications, whilst providing non-compromised communications. In this paper, we present a broad picture of the motivation, methodologies, challenges, and research opportunities of realizing PMN, by providing a comprehensive survey for systems and technologies developed mainly in the last ten years. Beginning by reviewing the work on coexisting communication and radar systems, we highlight their limits on addressing the interference problem, and then introduce the JCAS technology. We then set up JCAS in the mobile network context and envisage its potential applications. We continue to provide a brief review of three types of JCAS systems, with particular attention to their differences in design philosophy. We then introduce a framework of PMN, including the system platform and infrastructure, three types of sensing operations, and signals usable for sensing. Subsequently, we discuss required system modifications to enable sensing on current communication-only infrastructure. Within the context of PMN, we review stimulating research problems and potential solutions, organized under nine topics: performance bounds, waveform optimization, antenna array design, clutter suppression, sensing parameter estimation, resolution of sensing ambiguity, pattern analysis, networked sensing under cellular topology, and sensing-assisted communications. We conclude the paper by listing key open research problems for the aforementioned topics and sharing some lessons that we have learned.

I. INTRODUCTION

JCAS uses shared communication and sensing resources to evolve mobile networks into perceptive mobile networks with ubiquitous radio-sensing capabilities. PMNs could reduce the infrastructure burden of large-scale sensing while supporting applications across transportation, cities, homes, industry, and the environment.

  • Background: Coexisting communication and radar systems use separate signals and focus on interference management, whereas JCAS seeks one transmitted signal for both functions.JCAS addresses the interference created by transmitting two separate signals by jointly designing and using a shared signal.
  • Background: Passive sensing can use cellular and other ambient radio signals, but unsynchronized clocks create timing and ranging ambiguity and complicate joint measurement processing.Passive receivers may also lack signal-structure knowledge, limiting interference suppression and separation of multiuser signals.
  • Background: JCAS in mobile networks forms a perceptive mobile network that adds environmental perception to existing communication infrastructure while aiming to preserve communication services.The paper describes PMNs as ubiquitous radio-sensing networks built on current mobile-network infrastructure without significant structural changes.
  • Motivation: Large-scale sensing is constrained by the infrastructure cost and limited coverage of existing sensors, motivating PMNs as a potentially ubiquitous alternative.PMNs combine broad coverage, broadband signals, and powerful infrastructure to provide simultaneous communication and radio sensing.
  • Potential Applications: PMN applications span smart transportation, smart cities, smart homes, industrial IoT, environmental sensing, and sensing-assisted communications.The paper also notes complementary capabilities such as day-and-night operation and sensing through fog, foliage, and some solid objects.

D. Contributions and Structure of this Paper

This survey examines how JCAS can evolve communication-only mobile networks into perceptive mobile networks, covering system frameworks, infrastructure changes, research challenges, and open problems. It distinguishes three JCAS design categories and organizes the PMN review around cellular-network-specific technologies and sensing capabilities.

  • Scope and contribution: The survey focuses on JCAS techniques tailored to cellular and mobile networks, emphasizing signal-processing perspectives and PMN evolution.It addresses heterogeneous network architectures, sophisticated mobile signal formats, and complicated propagation environments.
  • PMN framework: The paper introduces a PMN framework that integrates sensing into existing mobile-network structures and signals at network and signal levels.The framework covers system architecture, three unified sensing options, and usable signals.
  • Infrastructure evolution: The survey reviews infrastructure paths for evolving communication-only networks, including full-duplex and three near-term options requiring limited modifications, especially for TDD systems.These options support uplink and downlink sensing with different degrees of infrastructure enhancement.
  • Research agenda: Research challenges and opportunities span performance bounds, waveform optimization, antenna arrays, clutter suppression, parameter estimation, ambiguity resolution, pattern analysis, networked sensing, and sensing-assisted communications.The paper also summarizes technical maturity, research difficulty, and key open problems across these areas.
  • JCAS classification: JCAS systems are classified as radar-centric, communication-centric, or jointly designed without constraint to an underlying system.PMNs belong to the communication-centric category because they add radio sensing to primary mobile communication networks.
  • Signal foundations: The paper contrasts radar and communication signals, which differ in waveform structure and design priorities relevant to JCAS integration.Radar signals commonly prioritize sensing-oriented processing and low PAPR, whereas communication signals prioritize information carrying and may use complex modulated structures.

B. Radar-Centric Design: Realizing Communication in Primary Radar Systems

Radar-centric JCAS embeds communication into a primary radar system, typically by modifying radar waveforms or parameters while preserving radar operation. The surveyed literature identifies communication protocol design and receiver processing as important unresolved issues, alongside challenges in integrating sensing and communications across networked systems.

  • Radar-centric design: Radar-centric JCAS realizes communication in a primary radar system, where radar operation remains the underlying design priority.Communication is commonly embedded through waveform or signal-parameter modifications.
  • Information embedding: Index modulation embeds communication information in combinations or permutations of signal parameters across space, time, frequency, or code domains.Its stated advantage is preserving the basic radar waveform and signal structure with negligible influence on radar operation.
  • Open limitations: Communication protocol design and receiver signal processing remain insufficiently studied for extracting information embedded in radar waveforms.The challenge is fitting medium-access and physical-layer frame structures seamlessly into radar operation.
  • Communication-centric contrast: Communication-centric JCAS in mobile networks must address full-duplex operation in mono-static setups and clock asynchronization in bi-static or multi-static systems.Existing radar approaches are not directly practical for communication systems without dedicated sensing receiver hardware.
  • Mobile-network context: Mobile-network JCAS research considers cellular signal, system, and network structures while developing sensing from modern communication signals.The survey organizes associated techniques and open problems within the PMN framework.
  • Joint design and emerging systems: Jointly designed JCAS systems have greater freedom to optimize signals and systems for communication and sensing, while mmWave and Terahertz systems offer large bandwidth and short wavelengths.Their deployment and standardization remain emerging issues, and multi-channel sensing must address signal imperfections and concatenation.

E. Advantages of JCAS Systems

JCAS integrates communication and sensing functions to improve spectrum and beamforming efficiency, reduce transceiver cost and size, and create mutual benefits between communications and sensing. The survey places these advantages within PMN architectures that can evolve from current mobile networks using standalone BSs or collaborative CRAN deployments.

  • Sharing communication and radar spectrum can ideally double spectral efficiency.
  • Sensing-derived channel structures can improve beamforming through faster beam adaptation and beam-direction optimization.
  • Compared with separated systems, joint transceivers can significantly reduce cost and size.
  • Communication links support coordination among sensing nodes, while sensing provides environmental awareness that can improve communication security and performance.
  • PMNs can evolve from current mobile networks through hardware, system, and algorithm modifications, with sensing implemented at UEs or BSs.The survey mainly considers BS-side sensing because BSs offer networked cooperation, larger antenna arrays, greater computation, and fixed locations.
  • JCAS sensing can be deployed in standalone BSs or collaborative CRANs, whose distributed RRUs are coordinated by a central unit.CRAN RRUs are typically connected to the central unit through optical fibre and can form distributed sensing networks when synchronized.

B. Three Types of Sensing Operations

PMNs unify uplink sensing with downlink active and passive sensing, using communication signals from UEs, the sensing node itself, or other RRUs. The three operations differ in transmitter–receiver geometry, synchronization, hardware requirements, and sensing scope, while practical results show useful detection and resolution capabilities.

  • PMNs support uplink sensing, downlink active sensing, and downlink passive sensing as three unified sensing operations.They can be implemented individually or together across PMN deployments.
  • Downlink Active Sensing: Downlink active sensing uses echoes of a BS’s own downlink signals with a co-located transmitter and receiver, enabling synchronized mono-static sensing but requiring full-duplex capability or an equivalent.
  • Downlink Passive Sensing: Downlink passive sensing uses downlink signals transmitted by other RRUs, creating a bi-static or multi-static setup that senses the environment between RRUs.Under cooperative SDMA, passive and active signals may overlap in time and frequency, so sensing algorithms must account for both.
  • Uplink Sensing: Uplink sensing uses signals received from UE transmitters and can be implemented without changing hardware, network setup, or requiring full-duplex operation.Unlike passive sensing generally, the BS knows the uplink protocol and signal structure.
  • Downlink sensing can potentially be more accurate than uplink sensing because BSs have more antennas, higher transmit power, centrally known signals, and fewer privacy concerns.
  • With transmission power below 25 dBm, downlink and uplink sensing can detect objects beyond 150 and 50 meters, respectively, in dense multipath environments.A 100 MHz bandwidth can provide distance resolution of a few meters, while a 16-antenna uniform linear array provides about 10-degree angle resolution.

1) Reference Signals Used for Channel Estimation:

PMNs can reuse communication signals for sensing, with reference signals such as DMRS generally offering the best sensing properties. Signal choice must balance sensing quality, resource allocation, and communication impact across reference, synchronization, and data signals.

  • Reference Signals Used for Channel Estimation: 5G NR provides DMRS, SRS, and CSI-RS reference signals that can support sensing alongside channel estimation.DMRS symbol positions are known to BSs and can be adjusted across slots, subcarriers, and resource blocks.
  • Reference Signals Used for Channel Estimation: DMRS resource-grid allocation can be optimized jointly for communication and sensing under different channel conditions.
  • Synchronization signal blocks can be used for sensing, but their limited subcarrier occupancy may restrict identification of multipath delays.
  • Data payloads can support downlink sensing directly because symbols are known, whereas uplink sensing requires decision-directed use after demodulation.Random data symbols and non-orthogonal spatial streams make payloads less ideal for sensing.
  • Summary and Insights: Almost all communication signals can be used for sensing, but reference signals typically have the best properties and combined signal usage requires careful planning and optimization.
  • JCAS can share the whole transmitter and many receiver modules, while sensing estimation can operate in time or frequency domains.Existing communication platforms still require hardware and network modifications to support sensing, including solutions for full-duplex limitations and uplink synchronization ambiguity.

B. Using Full-Duplex Radios for Downlink Sensing

Full-duplex radios offer an ideal route to seamless downlink sensing but remain difficult to implement, especially in mobile MIMO systems. Near-term alternatives use receiving-only or spatially separated sensing hardware, with single separated receive antennas identified as the most cost-effective option.

  • Using Full-Duplex Radios for Downlink Sensing: Full-duplex sensing must suppress strong transmitter leakage while preserving weak communication signals and environmental echoes.Practical suppression combines antenna separation, RF suppression, and baseband suppression.
  • Using Full-Duplex Radios for Downlink Sensing: JCAS full-duplex operation may be easier than communication full-duplex when sensing is co-located and simultaneous transmission by separate communication nodes is unnecessary.The receiver primarily removes directly leaked transmit signals while retaining their echoes for sensing.
  • Using Full-Duplex Radios for Downlink Sensing: Full-duplex JCAS is an ideal future technology, but handling many antenna-pair leakage signals makes it immature and impractical for near-term MIMO deployment.
  • Receiving-only BSs or dedicated receiving nodes provide near-term downlink-sensing alternatives, with clock synchronization needed to remove delay ambiguity between transmitters and receivers.TDD systems can separate downlink and uplink sensing signals largely in time at the receiver.
  • A spatially separated receive antenna can reduce leakage and support downlink active sensing on existing TDD hardware, although it requires installation space and may increase cost.
  • Using one sensing receive antenna with multiple transmitted spatial streams preserves estimation of all sensing parameters except AoA, while mainly reducing received signal energy.Estimated AoD and delay can still determine target locations.
  • Summary and Insights: Among three near-term suboptimal deployment solutions, a single spatially separated receive antenna is identified as the most cost-effective downlink-sensing option.Hardware calibration can mitigate the impact of imperfect receivers and antenna arrays on high-resolution parameter estimation.

V. MAJOR RESEARCH CHALLENGES FOR PMN

PMN research must reconcile communication and sensing requirements while extracting sensing parameters from complex mobile signals and operating across cellular networks. The survey organizes these challenges around performance bounds, joint optimization, mutual benefits, and networked sensing.

  • Sensing parameter estimation: PMNs require sensing-specific parameter-estimation techniques because mobile signals are randomly modulated, multiuser, spatially multiplexed, and fragmented across time, frequency, and space.Most existing radar, passive-radar, channel-estimation, localization, MUSIC, and ESPRIT methods are not directly applicable.
  • Joint design and optimization: Jointly designing signals and systems is a key problem because communication and sensing have different waveform, beamforming, antenna, and network-level requirements.The survey notes that system- and network-level optimization can matter more than waveform and basic-parameter optimization alone.
  • Mutual benefits: The mutual benefits of integrating communication and sensing remain poorly understood, with existing work concentrated mainly on propagation-path optimization and secure communications.The survey identifies this limited understanding as an important research issue.
  • Networked sensing: Cellular sensing could increase sensing capacity through frequency reuse, but performance bounds for sensing networks under cellular topology are largely unavailable.The cellular structure was designed for communication spectrum efficiency and capacity, while analogous sensing limits remain insufficiently characterized.
  • Performance bounds: PMN performance can be characterized using mutual information or estimation-accuracy measures such as Cramer-Rao lower bounds.For PMNs, mutual-information formulations differ between uplink and downlink sensing, and CRLB expressions may lack closed forms for MIMO-OFDM signals.
  • Performance bounds: Mutual information provides communication and sensing objectives that can guide joint optimization of transmitted signals or precoders.Communication uses channel-conditioned mutual information, while sensing uses conditional mutual information involving sensing channels and sensing signals.
  • Performance bounds: Existing JCAS mutual-information results provide a basis for PMN analysis, but practical formulations must account for uplink/downlink differences, frame structure, estimation errors, and scheduling.The survey reports that optimal solutions for communication and sensing are generally different, creating tradeoffs when requirements diverge.

B. Waveform Optimization

JCAS waveform optimization must reconcile a single shared signal with different communication and sensing requirements. PMN approaches optimize spatial precoding, combine communication and sensing waveforms, or adapt signal and resource parameters.

  • Waveform optimization: JCAS waveform optimization is difficult because one transmitted signal serves communication and sensing despite their different waveform requirements.Communication signals are typically random, multicarrier, and multiuser, whereas traditional radar signals are often orthogonal and unmodulated.
  • Optimization approaches: PMN waveform optimization is classified into spatial, time-domain, and frequency-domain techniques with different infrastructure-modification requirements.Spatial precoding can often be realized without changing existing signals, while time- and frequency-domain methods generally require slight signal changes.
  • Spatial optimization: Spatial precoding optimization alters transmitted-signal statistics to optimize joint communication-and-sensing objectives under constraints.The objective may target communication, sensing, or a weighted joint function.
  • Spatial optimization: Pre-designed communication and sensing precoders can be combined flexibly, enabling rapid adaptation when requirements change despite potentially suboptimal results.Examples include weighted sums or concatenations of communication- and sensing-oriented precoders.
  • Spatial optimization: Multibeam methods generate communication and sensing sub-beams with analogue antenna arrays, with efficiency depending on environmental and system factors.The approach is particularly relevant to directional mmWave systems.
  • Multiuser access: Uplink waveform optimization still needs to address multiuser access, whereas downlink sensing can treat multiuser signals as known to the sensing receiver.For downlink sensing, multiuser interference primarily affects communications because the transmitted signals are available to the sensing receiver.

2) Optimization in Time and Frequency Domains:

Time- and frequency-domain optimization modifies frame structure, pilots, preambles, subcarrier occupation, or power allocation to balance communication and sensing. Antenna-array designs similarly address conflicting beamforming, aperture, processing-gain, and resolution requirements.

  • Optimization in Time and Frequency Domains: Time- and frequency-domain optimization jointly adjusts frame structure, subcarrier occupation, power allocation, and pilot design, usually requiring slight modifications to mobile signals.These choices allow communication and sensing requirements to be considered across resources.
  • Optimization in Time and Frequency Domains: Communication preambles contain unmodulated and orthogonal signals that can be used directly for sensing, and their format can be designed to resemble radar waveforms.Preamble optimization can target both signal format and resource allocation.
  • Optimization in Time and Frequency Domains: Preamble length and power allocation have a much larger impact on communications than on sensing in the reported spatio-temporal optimization.A weighted sum of communication and sensing mutual informations yields a closed-form optimal power allocation.
  • Optimization in Time and Frequency Domains: Non-uniformly placed preambles or pilots provide a better communication–radar performance trade-off than equally spaced placements, particularly at large radar distances.The result is reported for a single-carrier 802.11ad-based JCR system, with possible extension to MIMO-OFDM PMNs.
  • Future signal formats: OTFS is identified as a possible next-generation JCAS waveform because its delay-Doppler representation supports sparse parameters involving delay, angle, and Doppler frequency.The survey presents OTFS as an extension beyond single-carrier and OFDM(A)-based JCAS waveform design.
  • Antenna array design: Antenna-array design faces conflicting requirements: sensing favors varying narrow beams and special antenna spacing, while communications favor fixed pointed beams and multibeam SDMA.The survey classifies PMN array techniques into virtual, sparse, and spatially modulated arrays.
  • Virtual MIMO and antenna grouping: Virtual subarrays divide antennas into groups, possibly with overlap, to assign communication and sensing tasks while retaining beamforming and orthogonal signaling across groups.Fig. 10 illustrates two overlapping three-antenna virtual subarrays.
  • Virtual MIMO and antenna grouping: Antenna grouping creates a trade-off between communication processing gain and sensing resolution related to the number of independent spatial streams.Hybrid antenna arrays are suggested as a potentially low-cost option, especially for mmWave systems, but hybrid-array JCAS research remains early-stage.

2) Sparse Array Design:

Sparse array design uses antenna placement and selection to expand spatial degrees of freedom for JCAS, supporting communication modulation and improved sensing resolution. RIS extends this design space through controllable propagation, but practical PMN applications remain open.

  • Sparse array design: Sparse arrays place limited antennas across a larger aperture to achieve high spatial resolution and low sidelobes.They are often formulated as selecting antenna locations from a large uniform grid.
  • Sparse array design: Sparse array design suits massive MIMO systems with many antennas but limited RF chains, potentially reducing cost while enhancing JCAS performance.It can add index modulation for communications and improve radar detection resolution.
  • Spatial modulation: A prototype using message-dependent antenna allocation increases communication rate while improving angular resolution and reducing transmit-beam sidelobes.The improvement is reported relative to fixed antenna allocations.
  • RIS-assisted JCAS: RIS adds controllable phase shifts that can shape propagation, improve communication performance, and generate location-dependent sensing fingerprints.It provides additional spatial degrees of freedom for JCAS design.
  • RIS-assisted JCAS: Jointly optimizing JCAS waveforms and RIS phase shifts can improve communication throughput without distorting the desired sensing beampattern.The approach also investigates the communication–sensing performance trade-off and multiuser interference.
  • Clutter suppression: PMN clutter suppression can occur before or after sensing-parameter estimation, with the two choices trading reduced parameter burden against avoiding sensing distortion.The two alternatives are summarized as distinct processing locations in the sensing pipeline.
  • Clutter suppression: Conventional radar clutter-suppression methods are not directly transferable because PMNs involve different signals, environments, and clutter characteristics.PMN multipath clutter is typically additive and may include near-zero-Doppler components.

1) Recursive Moving Averaging (RMA):

RMA estimates largely static multipath clutter by recursively averaging received signals over a suitably chosen window, then removing the estimate. Its effectiveness depends on signal compensation and parameter choices.

  • RMA principle: RMA estimates clutter under the assumption that fixed sensing parameters make low-Doppler path signals nearly identical across time.The estimated clutter is then removed from the received signal.
  • RMA limitations: RMA requires compensation for practical timing and frequency offsets before it can work effectively.These imperfections can otherwise make the method inefficient.
  • RMA principle: RMA recursively averages received signals over a window long enough to suppress time-varying paths but shorter than the channel coherence time.The forgetting factor controls the recursive update.
  • RMA equation: The RMA output is recursively updated from the previous output and the current channel estimate using a forgetting factor.The forgetting factor is also described as a learning rate.
  • RMA tuning: Window timing, window length, and forgetting factor determine how strongly signals with different Doppler frequencies are suppressed.Input spacing affects whether components combine constructively or destructively.
  • GMM comparison: GMM can achieve a given clutter-estimation accuracy with fewer samples than matched filtering and RMA, but commonly requires high-complexity estimation.Reducing GMM estimation complexity remains an open research problem.
  • Alternative estimators: 2D DFT offers coarse sensing-parameter estimates but has low resolution and typically requires continuous measurements, limiting use with discontinuous PMN samples.Windowing only slightly improves its resolution.

2) Subspace-Based Spectrum Analysis Techniques:

Subspace and compressive-sensing methods address nonlinear, multidomain parameter estimation in PMNs, but sampling, complexity, and continuous-parameter mismatch constrain their use. Structured and off-grid methods seek better fidelity at higher computational cost.

  • Subspace methods: MUSIC and ESPRIT estimate continuous sensing parameters with high resolution, but require favorable sampling, search granularity, or enough samples to separate signal and noise subspaces.Nonuniform-sampling variants can have very high computational complexity.
  • Compressive sensing: CS formulates sensing-parameter estimation as sparse signal recovery across delay, AoA, and Doppler domains.These domains can be represented using multidimensional Kronecker or tensor structures.
  • Compressive sensing: High-dimensional CS may be impractical for Doppler and AoA because available samples are limited, whereas mobile signals usually provide many subcarrier samples for delay estimation.MMV-CS formulations can combine spatial or Doppler signals with frequency-domain measurements.
  • Compressive sensing: Kronecker CS complexity is typically proportional to the cube of the number of samples, creating a major multidimensional-computation burden.This complexity is a key consideration when selecting CS dimensionality.
  • On-grid CS: On-grid CS is promising for PMN estimation, but dictionary mismatch between continuous parameters and finite grids can significantly degrade performance.The degradation worsens as the number of unknown variables increases.
  • Off-grid estimation: Grid densification and off-grid CS reduce quantization error, but both have higher complexity than conventional on-grid CS.Off-grid approaches include perturbation, maximum-likelihood, and atomic-norm methods.
  • Structured sparsity: Multipath channels often exhibit cluster sparsity, motivating CS priors and structured recovery methods that model paths arriving in parameter-similar groups.Candidate approaches include model-based CS, variational Bayes, and block Bayesian methods.

F. Resolution of Sensing Ambiguity

Sensing ambiguity in PMNs arises mainly from asynchronous timing and carrier offsets, motivating methods that remove or suppress these effects. CACC and CSI-ratio approaches offer partial solutions but retain practical limitations and unresolved ambiguities.

  • Sources of ambiguity: PMN sensing lacks typical clock-level synchronization, so time-varying timing and carrier-frequency offsets can create ambiguity in range and speed estimation.These offsets are easier to handle in communications than in radar sensing.
  • Cross-antenna cross-correlation: CACC removes common timing and frequency offsets across receive antennas by cross-correlating their signals, but produces relative sensing parameters and additional unknown terms.The method assumes shared oscillator impairments across antennas.
  • Cross-antenna cross-correlation: CACC output contains symmetric image components in relative delays and Doppler frequencies, causing sensing ambiguity and degrading estimation performance.The proposed image-suppression method is sensitive to the number and power distribution of static and dynamic propagation paths.
  • Cross-antenna cross-correlation: CACC has been used for WiFi range, velocity, and AoA estimation, but more advanced techniques are needed for robust operation under varied propagation paths.Its practical setup can use fixed nodes with line-of-sight links, including some mmWave deployments.
  • CSI-ratio methods: CSI ratios across antennas can suppress random clock-induced phase shifts while largely reducing measurement noise and preserving more information than cross-correlation.The approach establishes a relationship between CSI ratios and target movement.

I. Sensing-Assisted Communications

Sensing-assisted communications use radio sensing to improve beamforming, tracking, and security within PMNs. The surveyed approaches demonstrate feasibility, while practical deployment still requires resolving object-to-antenna mapping and multipath effects.

  • Applications: Radio sensing can support communications through sensing-assisted beamforming and secure communications.These applications exploit environmental and channel information obtained from integrated sensing.
  • Beamforming: Sensing can reduce the time needed for narrow-beam mmWave alignment by providing propagation information for initial and updated beamforming.JCAS uses a common signal and can construct a one-hop radio propagation map from downlink and uplink sensing.
  • Beamforming: Radar-assisted beam alignment has been shown feasible, but simulation results still exhibit a relatively large performance gap to the target.The approach estimates the covariance at a hybrid receiver from radar-derived signal covariance.
  • Beamforming: A multibeam design reserves fixed sub-beams for communication and packet-varying sub-beams for environmental scanning while transmitting the same data signals.The sensing module can combine echoes from both sub-beam types to construct a propagation map.
  • Beamforming limitations: Practical sensing-assisted beamforming must address offsets between sensed objects and their communication antennas, as well as phase estimation under multiple reflections.Combining downlink coarse sensing with uplink more detailed sensing is proposed as one potential solution.
  • Secure communications: JCAS physical-layer security uses detailed channel composition information, including artificial-noise-aided secure beamforming to combat eavesdroppers.Research on exploiting radio sensing for secure communications remains at an early stage.

APPENDIX

The appendix formulates channel and received-signal models for multi-antenna, multi-RRU mobile networks. These models cover downlink and uplink sensing and expose how propagation, Doppler, array geometry, clock offsets, and noise enter the observations.

  • System model: The CRAN model contains Q cooperating RRUs with uniform linear arrays serving K users through multiuser-MIMO OFDMA.Signals are spatially precoded in frequency before inverse FFT processing and assignment to RRUs.
  • General channel model: The general channel model represents each multipath through amplitude, propagation delay, Doppler frequency, AoD, and AoA across subcarriers and OFDM blocks.The model applies to both communication and sensing, but sensing must resolve the individual path parameters rather than only composite channel coefficients.
  • General channel model: Asynchronous timing and carrier-frequency offsets can vary with time and create range and speed ambiguity for sensing, despite being manageable in communications.This difference motivates explicit impairment handling in sensing algorithms.
  • Downlink sensing: In downlink sensing, each RRU receives reflections from itself and other RRUs, with received signals modeled using transmitted precoded signals and additive noise.A common clock within and between RRUs typically makes the downlink timing offset zero.
  • Downlink sensing: Jointly packing signals from multiple RRUs increases observation length but also increases unknown parameters, so joint processing does not directly improve sensing without exploiting parameter correlation.Channel reciprocity can provide similar inter-RRU propagation parameters for joint processing.
  • Uplink sensing: Uplink sensing uses received UE signals that are identical to the communication signals, and many processing techniques apply to both uplink and downlink because their signal expressions are similar.For a single UE occupying a subcarrier, the uplink model reduces to K=1.
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