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Integrated Sensing and Communications: Recent Advances and Ten Open Challenges

Shihang Lu, Fan Liu, Yunxin Li, Kecheng Zhang, Hongjia Huang, Jiaqi Zou, Xinyu Li, Yuxiang Dong, Fuwang Dong, Jia Zhu, Yifeng Xiong, Weijie Yuan, Yuanhao Cui, Lajos Hanzo

arXiv:2305.00179v2cs.ITeess.SP

TL;DR

ISAC addresses the need to support sensing-dependent applications and shared wireless resources in B5G/6G networks. The paper critically reviews advances across theory, physical-layer design, networking, and applications, and formulates ten open challenges. It highlights tradeoffs in ISAC objectives, synchronization constraints, network redesign, and wireless-sensing security and privacy.

  • Problem

    ISAC must unify radar and communications theories despite their different information objectives, while existing networks face unresolved design, synchronization, architecture, and security challenges.

  • Method

    The paper critically appraises recent ISAC advances across foundations, system design, networking, and applications, organizing the discussion into ten open challenges.

  • Results

    The review identifies information-theoretic tradeoffs, synchronization and phase-offset issues, Pareto-optimal signaling, super-resolution design, network transformation, and wireless-sensing security and privacy concerns.

  • Takeaways & Limitations

    ISAC research spans a progression from fundamental theory to practical engineering, requiring coordinated advances in signaling, synchronization, cellular architecture, protocols, and protection of sensing information.

Abstract

from arXiv · show

It is anticipated that integrated sensing and communications (ISAC) would be one of the key enablers of next-generation wireless networks (such as beyond 5G (B5G) and 6G) for supporting a variety of emerging applications. In this paper, we provide a comprehensive review of the recent advances in ISAC systems, with a particular focus on their foundations, system design, networking aspects and ISAC applications. Furthermore, we discuss the corresponding open questions of the above that emerged in each issue. Hence, we commence with the information theory of sensing and communications (S$\&$C), followed by the information-theoretic limits of ISAC systems by shedding light on the fundamental performance metrics. Next, we discuss their clock synchronization and phase offset problems, the associated Pareto-optimal signaling strategies, as well as the associated super-resolution ISAC system design. Moreover, we envision that ISAC ushers in a paradigm shift for the future cellular networks relying on network sensing, transforming the classic cellular architecture, cross-layer resource management methods, and transmission protocols. In ISAC applications, we further highlight the security and privacy issues of wireless sensing. Finally, we close by studying the recent advances in a representative ISAC use case, namely the multi-object multi-task (MOMT) recognition problem using wireless signals.

I. INTRODUCTION

ISAC is presented as an evolution beyond communication-only networks, motivated by emerging sensing-dependent applications and shared resource constraints. The paper reviews advances from theory through applications while framing ten open challenges spanning system design, networking, and sensing security.

  • Sensing functionality is envisioned as a basic service for B5G/6G applications including autonomous systems, driving, activity sensing, smart homes, and UAV networks.
  • The paper organizes ten open challenges across information-theoretic limits, system design, cellular architecture, resource management, security and privacy, and MOMT recognition.
  • ISAC jointly exploits limited hardware, spectrum, and energy resources while enabling sensing-assisted communications.
  • ISAC-enabled IoT devices combine environmental sensing with connectivity, reducing data-exchange delays and processing costs through sensing-with-communication.
  • Fundamental ISAC research must reconcile radar’s environment uncertainty with communications’ transmitted-signal uncertainty, while addressing synchronization, phase offsets, and sensing-friendly network interference.
  • Wireless sensing introduces security and privacy risks because malicious entities may overhear confidential CSI, misuse localization, or infer target movements.

C. Existing Efforts and the Scope of This Paper

The paper reviews ISAC advances from information-theoretic foundations through system design, networking, and applications, while organizing ten open challenges across these areas. It develops sensing and communication metrics, their tradeoffs, and representative optimization and detection relationships.

  • Scope: The paper critically reviews recent ISAC advances and formulates ten open challenges spanning theory, system design, networking, and applications.Its organizing narrative is “Theory-System-Network-Application.”
  • Scope: Unlike earlier overviews focused on narrower ISAC topics or three scenario categories, this paper adopts a holistic perspective from fundamental theory to applications.It compares existing contributions and this paper using the ten open questions.
  • Information-theoretic foundations: Information-theoretic analysis treats communication and sensing through distinct conditional mutual-information objectives, producing an MI-based S&C tradeoff for ISAC signaling.Communication maximizes I(X; Yc|Hc), while sensing maximizes I(Hs; Ys|X), under practical constraints.
  • Information-theoretic foundations: A weighted MI formulation uses ρ ∈[0, 1] to control S&C priorities and identify the Pareto-optimal MI boundary.The weighting factor explicitly represents the assigned priorities of sensing and communications.
  • Metric limitations: The review notes that MI lacks an explicit operational interpretation for radar sensing, while multi-metric Pareto optimization can become computationally excessive as metrics increase.Improving metrics other than MMSE may require accepting MMSE degradation, and expanding the metric set enlarges the search space.
  • Sensing metrics: KLD provides an asymptotic detection-performance measure: as observations N increase, maximizing KLD becomes equivalent to maximizing detection probability.The paper also reports that increasing transmit SNR increases KLD and improves detection performance.

2) Existing Literature:

Information-theoretic ISAC models connect communication capacity, sensing-channel states, and estimation parameters, while framing sensing as delayed or state-feedback information. A pentagon inner bound characterizes part of the CRB-rate tradeoff, but achieving its full Pareto boundary remains open.

  • Capacity-distortion models represent target responses as delayed feedback channels for channel-state estimation.
  • The I-MMSE perspective changes in monostatic sensing because the transmitter already knows its own signal and estimates target parameters instead.
  • A pentagon inner bound of the CRB-rate region reveals information-theoretic connections among communication capacity, target-channel states, and parameters to be estimated.

3) Future Directions and Potential Solutions:

The paper identifies unresolved information-theoretic questions for ISAC, including unified performance metrics, attainable limits, and methods for approaching sensing–communication trade-offs.

  • Techniques capable of approaching the Pareto-optimal boundary remain largely unexplored beyond communication- and sensing-optimal corner points.
  • Quantifying mutual-information requirements for delay, angle, and Doppler estimation remains an open research problem.
  • Future work must clarify relationships between KLD-based target detection measures and mutual information in wireless communication.
  • Unified metrics are needed to evaluate sensing and communications, although sensing estimation rate provides only one proposed unification.
  • Random communication signals require sensing metrics such as ergodic CRB or ergodic PCRB to be tailored to different use cases.
  • The information theory of ISAC remains open for attainable degrees of freedom, massive MIMO systems, and networking issues.

1) Background:

ISAC can reuse sensing information to assist communication, but the usefulness of inferred CSI depends on the correlation between sensing and communication channels. The paper distinguishes uncorrelated, moderately correlated, and strongly correlated environments.

  • Conventional CSI acquisition uses downlink pilots and uplink feedback, creating communication overhead that limits useful transmission rate.
  • The three S&C environment categories are summarized as uncorrelated, moderately correlated, and strongly correlated scenarios.
  • Uncorrelated: Uncorrelated S&C channels arise in different spatial environments, making useful channel information difficult to infer from sensory data.
  • Moderately correlated: Moderately correlated channels share partial coupling when a sensed object scatters the communication signal, enabling partial CSI inference from strong echoes.
  • Strongly correlated: Strongly correlated channels occur when sensing and communication target the same object, allowing echoes to support vehicular tracking, downlink assistance, and reduced pilot overhead.

2) Existing Literature:

The paper notes that sensory features can assist beam management in massive MIMO systems, reducing beam-training and communication feedback overheads to a certain extent.

  • Velocity, location, and angle features in sensory data can support beam tracking and prediction in massive MIMO systems.The approach addresses overhead from selecting beams across a predefined codebook.

3) Future Directions and Potential Solutions:

The paper models sensing–communication channel correlation through shared channel parameters and factor-graph relationships, using mutual information to quantify inferable CSI. It identifies integration-gain quantification as an open challenge requiring more realistic models and real-time algorithms.

  • Communication and sensing channels are modeled as functions of their respective channel parameters, ηc and ηs.
  • The shared parameter set ηcs represents correlation between sensing and communication channels, including a null set in uncorrelated scenarios.
  • The factor graph represents relationships among sensing and communication channel parameters for channel-information inference.
  • Conditional mutual information I(Hc; Hs|Ys, X) estimates how much communication-channel information can be inferred from sensory data.
  • Inference-oriented modeling must account for complex wireless environments, spectrum interference, clutter sources, and real-time sensing requirements.
  • Quantifying integration and coordination gains remains an identified challenge for ISAC systems.

1) Background:

ISAC progresses beyond spectrum sharing by jointly exploiting common hardware, waveforms, and resources for integration and coordination gains. Its achievable performance depends on how strongly sensing and communication channels overlap, while quantitative gain measures remain preliminary.

  • Integration and coordination gains: ISAC combines common platforms and waveforms to improve hardware, spectrum, and energy efficiency through integration and coordination gains.Sensing-assisted communication can also reduce pilot overhead by using radar echoes for vehicle-trajectory prediction and beam alignment.
  • Channel overlap: Orthogonal sensing and communication resources form an uncorrelated inner bound with no integration gain.This occurs when the functionalities occupy different spatial environments or are separated in time or frequency.
  • Channel overlap: Moderately correlated channels permit mutual benefits through jointly reused resources, producing an arc-shaped achievable boundary.A sensed vehicle can simultaneously act as a scatterer for the communication signal, allowing resources such as transmit power to be reused.
  • Channel overlap: Strongly correlated channels define an upper boundary where sensing- and communication-optimal points are achievable without performance erosion.This corresponds to fully aligned sensing and communication channels with jointly exploited wireless resources.
  • Quantifying gains: A larger sensing-communication subspace correlation coefficient may produce a larger performance region and higher performance gain.The illustrated gain relationship is intuitive rather than a rigorous mathematical description, and quantifying the relevant regions depends on specific conditions.

III. PHYSICAL-LAYER SYSTEM DESIGN

Physical-layer ISAC design addresses synchronization and phase offsets, Pareto-oriented signaling, and super-resolution sensing. Tight synchronization is especially difficult because sensing requires greater accuracy than conventional communications.

  • Physical-layer design: Physical-layer ISAC design focuses on clock synchronization, Pareto-optimal signaling, and super-resolution methods for Sub-6G sensing.The section covers bistatic and distributed deployments alongside potential research directions for super-resolution sensing.
  • Clock synchronization: ISAC synchronization is more demanding than communication synchronization because sensing receivers must compensate differently while preserving target information.Communication channel estimation can cancel propagation delay and clock offset together, whereas ISAC sensing requires function-specific compensation.
  • Clock synchronization: Asynchronous clocks create timing, frequency, and phase offsets that can substantially degrade sensing performance.Timing offset introduces range bias in TOA- and TDOA-based estimation by perturbing propagation-delay estimates.
  • Synchronization solutions: Common reference clocks and multi-antenna signal processing offer alternative synchronization solutions.Reference clocks can be broadcast by a dominant node or GPS, while similar phase shifts across antennas can be mathematically removed.
  • Open synchronization issues: Existing synchronization approaches remain difficult to adapt because GPS-based methods are slow for high mobility and signal-processing methods require multiple antennas.These approaches also present a tradeoff between synchronization accuracy and hardware or deployment requirements.

4) Future Directions and Potential Solutions:

The paper identifies open directions for synchronization, Pareto-optimal waveform design, and super-resolution sensing. Proposed solutions improve particular capabilities but retain practical constraints involving mobility, bandwidth assumptions, metrics, and search complexity.

  • Clock synchronization: GPS-based synchronization can be more accurate than signal-processing methods, but its long synchronization time limits high-mobility applications.Signal-processing approaches generally require multiple-antenna receivers and provide lower synchronization accuracy.
  • Clock synchronization: UWB can provide high-precision synchronization through high-resolution timestamps, but it conflicts with narrow-band algorithms and requires two signal-trip rounds.These round trips make UWB synchronization unsuitable for high-mobility scenarios.
  • Pareto-optimal signaling: Existing ISAC waveform designs lack a unified Pareto framework for determining the optimal sensing-communication tradeoff.Current research mainly optimizes radar metrics under communication constraints or communication metrics under radar constraints.
  • Pareto-optimal signaling: A weighted joint waveform objective balances downlink multi-user interference against proximity to an ideal sensing waveform.The weighting factor ρ ∈[0, 1] controls the priorities assigned to sensing and communications under practical constraints.
  • Super-resolution sensing: Sub-6G cellular networks generally provide only meter-level sensing accuracy, motivating super-resolution methods for demanding applications.Distance resolution depends on bandwidth, while angular resolution depends on array aperture; carrier aggregation and sparse arrays target these limitations.

2) Existing Literature:

Existing and proposed ISAC architectures use carrier aggregation, sparse arrays, and super-resolution algorithms to improve sensing while supporting networked cooperation. Future cellular designs may integrate sensing into dense or cell-free architectures, but resource management and protocols remain open.

  • Super-resolution platforms: Carrier aggregation harnesses multiple component carriers across spectrum bands and can improve communication throughput, peak data rate, and sensing bandwidth.It includes intra-band contiguous, intra-band non-contiguous, and inter-band non-contiguous configurations.
  • Super-resolution platforms: Sparse arrays enlarge virtual array aperture through difference coarrays, improving expected angular resolution with fewer physical sensors.The cited six-sensor nested and coprime examples illustrate increased virtual aperture; coprime arrays can also reduce mutual coupling relative to nested arrays.
  • Super-resolution algorithms: MUSIC and ESPRIT are representative subspace-based angle-estimation algorithms, and combining them with sparse arrays is a promising resolution-improvement strategy.The paper identifies sparse-array and super-resolution combinations as a direction for improving ISAC sensing.
  • Cellular architecture: Network sensing integrates dual-functional signals and hardware into dense cellular architectures so user equipment and networks can sense surroundings.The envisioned applications include environmental monitoring and target detection.
  • Cellular architecture: Multi-station cooperation can expand detection range, improve SNR and detection probability, and provide multiple viewing angles for target sensing.Cell-free massive MIMO is presented as promising because distributed access points support flexible load balancing and simultaneous cooperative service.
  • Open network design: ISAC-specific cellular layouts, resource management, and protocols remain open because communication-only design principles may not suit sensing functionalities and station locations.The section characterizes these topics as having relatively little focused research and emphasizes potential questions and future directions.

1) Background:

Future ISAC networks must manage sensing requests alongside communication services despite limited shared resources. This requires cross-layer resource management, redesigned protocols, and attention to security and privacy.

  • Sensing requests may arrive randomly and unexpectedly, requiring tailored frame structures and resource-scheduling algorithms.
  • Limited PHY resources can prevent an ISAC base station from serving all sensing requests during congestion, motivating cross-layer resource management.
  • Echo-wave interference has distinct effects on communication and sensing, making interference handling a new PHY-layer ISAC challenge.
  • MAC layer: High-priority requests, such as those from high-velocity vehicles, should receive faster responses, higher QoS, and more resources.
  • MAC layer: ISAC channel access must redesign control-frame sequencing so sensing feedback does not collide with communication control frames.
  • Cross-layer optimization must account for long-term interactions across the entire ISAC framework rather than optimizing layers independently.

2) Existing Literature:

Existing ISAC work addresses sensing security, privacy, wireless human sensing, and signal-processing representations. However, these approaches still face practical constraints in sensing multiple people and targets.

  • Potential protections include transmit-signal optimization, beamforming, artificial noise, authentication, access control, and channel-aware secret-key generation.
  • Wireless human sensing uses radar- and WiFi-based methods, with radar offering speed and range estimation advantages for near-field sensing.
  • Radar sensing can represent human motion through 3D time-range-Doppler data cubes and compressed time-Doppler or range-Doppler maps.
  • Narrow bandwidth limits direct target-range estimation in ISAC, so existing systems generally estimate relative range instead.
  • HAR pipelines combine feature extraction from signal amplitude, phase, and Doppler shifts with model-based or learning-based activity recognition.

3) Future Directions and Potential Solutions:

The paper’s future directions span practical wireless experiments, multi-person sensing, theoretical foundations, physical-layer design, network architecture, applications, and standardization. These directions emphasize integrated, cross-layer, and Pareto-aware ISAC development.

  • WiFi is more suitable for indoor human sensing, whereas cellular systems are more promising for outdoor sensing tasks.
  • Most existing wireless human-sensing work focuses on single-person scenarios, leaving multi-person sensing under restrictive separability assumptions.
  • Future systems should support difficult scenarios including integrated human sensing and communications, ultra-reliable low-latency sensing, and non-line-of-sight sensing.
  • ISAC design guidelines cover theoretical foundations, physical-layer system design, networks and cross-layer design, and applications.
  • Theoretical Foundations of ISAC: Theoretical work should clarify sensing mutual information, connect sensory data with communication CSI, and quantify integration and coordination gains.
  • Physical-Layer System Design: Physical-layer directions include reducing synchronization and phase errors, exploring Pareto-optimal solutions, and using super-resolution methods for high-precision sensing.
  • ISAC Networks & Cross-Layer Design: Future ISAC networks require cell-free sensing, joint resource management, and protocols adapted to bursty sensing services.
  • ISAC Applications: ISAC applications require security and privacy guarantees and improved sensing resolution for multi-object multi-task recognition.
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