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Integrated Sensing and Communication with Reconfigurable Intelligent Surfaces: Opportunities, Applications, and Future Directions
Rang Liu, Ming Li, Honghao Luo, Qian Liu, A. Lee Swindlehurst
TL;DR
The paper addresses how to improve integrated sensing and communication amid spectrum congestion and demanding ubiquitous connectivity and sensing requirements. It surveys RIS fundamentals and RIS-assisted sensing and ISAC applications, then uses a DFRC case study to examine RIS-enabled performance gains and discusses practical research challenges. The study reports that RIS can improve sensing and communication by manipulating propagation, with sufficiently many elements strengthening virtual-line-of-sight paths while satisfying communication QoS requirements.
Problem
ISAC must reconcile conflicting sensing and communication requirements while supporting efficient, ubiquitous connectivity and sensing in future networks.
Method
The paper surveys RIS, sensing, and ISAC applications, presents RIS-assisted ISAC research, and evaluates an RIS-assisted DFRC system through simulation.
Results
The case study shows that increasing RIS elements makes performance improvements more pronounced, with virtual-line-of-sight paths eventually surpassing the direct path while satisfying communication QoS requirements.
Takeaways & Limitations
RIS-assisted ISAC can support high-accuracy, wide-coverage, and ultra-reliable sensing and communication functionalities for future 6G networks.
Abstract
from arXiv · showhide
Integrated sensing and communication (ISAC) is emerging as a key enabler to address the growing spectrum congestion problem and satisfy increasing demands for ubiquitous sensing and communication. By sharing various resources and information, ISAC achieves much higher spectral, energy, hardware, and economic efficiencies. Concurrently, reconfigurable intelligent surface (RIS) technology has been deemed as a promising approach due to its capability of intelligently manipulating the wireless propagation environment in an energy and hardware efficient manner. In this article, we analyze the potential of deploying RIS to improve communication and sensing performance in ISAC systems. We first describe the fundamentals of RIS and its applications in traditional communication and sensing systems, then introduce the principles of ISAC and overview existing explorations on RIS-assisted ISAC, followed by one case study to verify the advantages of deploying RIS in ISAC systems. Finally, open challenges and research directions are discussed to stimulate this line of research and pave the way for practical applications.
I. INTRODUCTION
ISAC integrates sensing and communication by sharing resources, while RIS reshapes wireless propagation with low energy and hardware requirements. The paper motivates RIS-assisted ISAC because uncontrolled environments and conflicting sensing-communication requirements limit performance.
- ISAC shares spectrum, hardware architecture, signal processing, and information between radar sensing and wireless communications.
- Integrating sensing and communication can improve spectral, energy, hardware, and cost efficiency.
- MIMO architectures exploit spatial degrees of freedom, but uncontrollable electromagnetic environments can still deteriorate integrated-system performance.
- The article examines RIS-assisted sensing and ISAC because RIS benefits are established in communications while radar and ISAC research remains recent.
- RIS elements provide tunable electromagnetic responses and cooperative passive beamforming with simple hardware, low energy consumption, and low cost.
B. RIS-Assisted Sensing
RIS-assisted sensing uses controllable propagation to strengthen target returns, suppress interference, and support sensing when direct links are blocked. Existing work evaluates these gains through detection, estimation, and echo-signal metrics.
- Target detection identifies target presence from reflected returns, while parameter estimation commonly concerns azimuth, distance, and velocity.
- Because direct optimization of detection and estimation metrics is difficult, studies often optimize illuminated power, beampatterns, SNR, or SINR.
- RIS can create effective line-of-sight links, add optimization degrees of freedom, boost target returns, and suppress interference.
- With a blocked direct link, an RIS enables direction-of-arrival estimation through virtual line-of-sight echoes by jointly optimizing transmit beamforming and RIS coefficients.
- RIS deployments near radar transmitters and receivers steer illumination and collect returns, with reflecting coefficients designed to maximize received-echo SNR.
3) DoA estimation:
RIS-assisted sensing extends beyond conventional radar by enabling multi-angle observation and improving localization and mapping. Within ISAC, RCC and DFRC share resources but require careful management of interference and conflicting functions.
- DoA estimation:: RIS-equipped UAV swarms can assist direction-of-arrival estimation while letting a central UAV observe targets from multiple angles.
- DoA estimation:: RIS can improve localization and mapping by enlarging signal differences across locations or targets.
- A. Principles of ISAC: Research on RIS-assisted ISAC includes initial investigations and case studies evaluating how RIS deployment benefits integrated systems.
- A. Principles of ISAC: RCC shares spectrum between separate radar and communication systems, whereas DFRC uses shared hardware and a unified waveform for both functions.
- A. Principles of ISAC: RCC requires interference management, cooperation, and side-information exchange, while DFRC reduces platform separation but faces conflicting waveform requirements.
B. RIS-assisted ISAC systems
RIS-assisted ISAC uses propagation control to improve sensing and communication under mutual interference. The surveyed designs combine RIS configuration with non-convex optimization methods and motivate broader models and algorithms.
- RIS-assisted ISAC may improve communication and sensing performance while enabling novel application scenarios.
- These applications require accurate models and associated algorithms for RIS-assisted ISAC system design.
- RIS can boost desired signals and suppress undesired signals in RCC by manipulating propagation, improving sensing and communication under mutual interference.
- An alternating algorithm using penalty dual decomposition and a concave-convex procedure was developed for the resulting non-convex optimization problem.
2) Deployment in DFRC systems:
RIS-assisted DFRC systems use reconfigurable propagation and waveform-design degrees of freedom across diverse deployment scenarios, but the resulting coupled optimization problems can be difficult. Applications range from partitioned sensing and communication functions to blocked-path operation, general LoS/NLoS propagation, and physical-layer security.
- DFRC deployment: RIS provides additional degrees of freedom for dual-functional waveform design, improving sensing–communication trade-offs across diverse DFRC applications.The associated optimization problems are non-convex because many variables are coupled.
- Partitioned functions: Adaptive RIS partitioning can separately support sensing and communication while a hierarchical codebook localizes the target and maintains a strong user link.
- Blocked paths: When direct BS–target paths are blocked, multiple RISs can jointly support virtual-LoS sensing and multi-user communication through frequency-dependent beamforming and phase shifts.The design maximizes weighted radar SINR and minimum communication SINR under a total transmit-power constraint.
- General propagation: With significant LoS and NLoS contributions, RIS coefficients affect received echoes nonlinearly, requiring advanced optimization and complicating standard SNR/SINR-based design.A studied simplified case maximizes radar SNR under communication-SNR and total-power constraints using MM and SDR methods.
- Physical-layer security: In RIS-assisted DFRC security scenarios, a target may also act as an eavesdropper, motivating RIS deployment and artificial noise for confidentiality.
C. Case Study
The case study evaluates an RIS-assisted DFRC system serving two users while detecting a target amid clutter. The results show focused illumination, low clutter-region power, and increasingly strong RIS benefits as the reflecting-element count grows.
- Simulation setup: The simulation considers a 16-antenna BS, an N-element RIS, two single-antenna users, one point-like target, and one point-like clutter source.All channels are LoS and follow a typical path-loss model.
- Beampattern: The beampattern directs BS beams toward the target, users, and RIS, whose passive beams further illuminate the target and users while keeping clutter-source power relatively low.
- RIS size: As N increases, RIS-enabled radar performance improvement becomes increasingly pronounced, with indirect-path echo signals eventually becoming stronger than the direct-path echoes.
- RIS size: For small N, the direct-path echo dominates, whereas for large N the RIS-created virtual-LoS link dominates the radar-return contribution.
- Implication: The results indicate that a sufficiently large RIS can improve target detection while satisfying certain communication quality-of-service requirements.
IV. OPEN CHALLENGES AND FUTURE DIRECTIONS
The paper identifies theoretical, modeling, and deployment challenges that must be addressed for practical RIS-assisted ISAC. Key needs include unified performance bounds, realistic echo models, and prototype-based validation of propagation and interference assumptions.
- Performance tradeoff: Unified upper bounds and tradeoff regions for sensing and communication are needed to evaluate RIS-assisted designs and guide radar–communication tradeoffs.
- Practical channel modeling: Different assumptions about echo propagation paths produce different radar signal models and algorithms, making practical channel-model selection essential.
- Practical channel modeling: Assuming all reflected returns contribute to detection can be overly strict because propagation delays may create ghost objects and ranging ambiguity.
- RIS deployments: Future deployments include active, multiple, UAV-mounted, and target-mounted RISs, offering signal amplification, spatial degrees of freedom, mobility, or self-assisted detection.
- Practical channel modeling: Potential interference directly reflected by RISs or RIS-mounted objects remains unclear and requires experimental measurements and prototype-based channel modeling.
3) Deployment and control of RISs:
RIS deployment and control must address practical choices about placement, size, signaling, and active amplification. Active RISs can reduce reflection loss but also amplify interference and increase power and hardware demands.
- Practical RIS deployment requires jointly deciding locations, element counts, and control signaling rather than assuming them fixed.
- Active RISs use reflection-type amplifiers to tune phase shifts and amplify incident signals, allowing smaller surfaces to mitigate severe reflection path loss.
- Active RISs also amplify undesired noise and interference, complicating satisfactory sensing and communication performance.
- Active-RIS design must jointly determine surface size and reflection coefficients under sensing or communication requirements to improve energy efficiency.
2) Multiple RISs:
Multiple and mobile RIS deployments provide additional spatial opportunities for communication and sensing, including coverage extension, interference control, and target observation. However, UAV-mounted RISs introduce interference and Doppler-related sensing ambiguities that require joint design.
- 2) Multiple RISs: Multiple RISs provide extra propagation-control degrees of freedom and geographic diversity for boosting desired signals and suppressing interference.
- 2) Multiple RISs: Multiple RISs can support high-quality communication and high-accuracy sensing in hotspots while improving performance in edge areas through passive beamforming gains.
- 3) UAV-mounted RIS: UAV-mounted RISs combine mobility and flexibility with lower RIS cost, weight, and power consumption to create paths around obstacles and cover shadowed areas.
- 3) UAV-mounted RIS: UAV reflections may interfere with target echoes, while maneuver-induced Doppler components can create velocity-measurement ambiguities.
- 2) Multiple RISs: RISs mounted on cooperative targets can boost backscatter toward a tracking radar, improving detection when the target has a small radar cross section.
C. Various RIS-assisted ISAC Scenarios
RIS-assisted ISAC extends beyond conventional RCC and DFRC to bi-static sensing, near-field operation, and wideband systems. These scenarios require models and designs that account for synchronization, spherical-wave propagation, frequency selectivity, and beam squint.
- C. Various RIS-assisted ISAC Scenarios: RIS-assisted ISAC research includes bi-static sensing, near-field communication and sensing, and wideband ISAC scenarios.
- Bi-static sensing: In bi-static ISAC, an RIS can establish a relatively stable virtual line-of-sight link and provide a stronger reference signal for synchronization.
- Near-field communication and sensing: Large, high-frequency RISs and near-transmitter or near-receiver deployment expand near-field operation, requiring electromagnetic rather than planar-wave modeling.
- Wideband ISAC: Wideband RIS-assisted ISAC must model frequency-selective reflections because ideal reflection assumptions can degrade communication QoS and cause Doppler-frequency ambiguity.
- Wideband ISAC: Large RISs also require attention to beam squint and frequency-dependent steering and reflection vectors in practical wideband designs.
D. Algorithm Designs
RIS-assisted ISAC algorithm design must handle high-dimensional radar data, discrete RIS phases, hardware imperfections, and difficult CSI acquisition. The article surveys these challenges while using a case study to demonstrate performance improvement from RIS deployment.
- D. Algorithm Designs: Radar-receiver processing is important because RIS-reflected returns enlarge the data dimension and can impose high computational loads.
- D. Algorithm Designs: Conventional optimization may be impractical for RIS-assisted ISAC because discrete phase shifts and hardware imperfections create complicated non-convex problems.
- D. Algorithm Designs: Large RISs make perfect channel-state-information acquisition difficult because many parameters must be estimated.
- D. Algorithm Designs: The article overviews RIS-assisted sensing and ISAC applications and presents a case study demonstrating performance improvement from RIS deployment.