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Joint Transceiver Beamforming and Reflecting Design for Active RIS-Aided ISAC Systems
Qi Zhu, Ming Li, Rang Liu, Qian Liu
TL;DR
Passive RIS can be limited by multiplicative fading, motivating active-RIS assistance for ISAC. The paper jointly optimizes beamforming, active-RIS reflection, and radar reception using BCD, Dinkelbach’s transform, and MM, achieving up to 32dB radar SNR improvement over passive RIS-assisted ISAC.
Problem
Passive RIS suffers multiplicative fading that limits performance when direct links are unavailable, motivating active RIS for ISAC.
Method
The paper jointly designs the transmit beamformer, active-RIS reflection coefficients, and radar receive filter using BCD, Dinkelbach’s transform, and MM.
Results
Up to 32dB radar performance improvement is achieved over passive RIS-assisted ISAC when amax = 8 and PBS ≤42dBm.
Takeaways & Limitations
Active RIS can substantially improve radar performance in ISAC while maintaining high-quality communication performance.
Abstract
from arXiv · showhide
Integrated sensing and communication (ISAC) is recognized as a promising technology with great potential in saving hardware and spectrum resources, since it simultaneously realizes radar detection and user communication functions in the fully-shared platform. Employing reconfigurable intelligent surface (RIS) in ISAC systems is able to provide a virtual line-of-sight (LoS) path to conquer blockage problem as well as introduce new degrees of freedom (DoFs) to further enhance system performance. Nevertheless, the multiplicative fading effect of passive RIS limits its applications in the absence of direct links, which promotes the development of active RIS. In this paper, we consider an active RIS-assisted ISAC system and aim to jointly design the transmit beamformer, the active RIS reflection and the radar receive filter to maximize the radar output signal-to-noise ratio (SNR) while guaranteeing pre-defined signal-to-interference-plus-noise ratios (SINRs) for communication users. To solve for this non-convex problem, an efficient algorithm is developed by leveraging the techniques of block coordinate descent (BCD), Dinkelbach's transform and majorization-minimization (MM). Simulation results verify the significant advancement of deploying active RIS in ISAC systems, which can achieve up to 32dB radar SNR enhancement compared with the passive RIS-assisted ISAC systems.
I. INTRODUCTION
RIS can improve ISAC by providing virtual LoS paths and additional DoFs, but passive RIS suffers multiplicative fading. The paper therefore develops an active RIS-assisted ISAC design targeting radar and communication performance jointly.
- RIS deployment can provide virtual LoS paths that address blockage and additional DoFs for improving ISAC system performance.
- Passive RIS suffers multiplicative fading because reflected source-RIS-destination paths usually have much larger equivalent path loss than direct paths.
- Active RIS equips each reflecting element with a dedicated amplifier while retaining adjustable phase shifts to address passive-RIS limitations.
- The paper jointly designs the transmit beamformer, active RIS reflection coefficients, and radar receive filter to maximize radar output SNR under communication-user QoS requirements.
- BCD, Dinkelbach’s transform, and MM are combined to solve the resulting non-convex optimization problem.
II. SYSTEM MODEL AND PROBLEM FORMULATION
The system uses a full-duplex BS and an M-element active RIS to serve communication users and detect a blocked target without a direct BS-target link. The design maximizes radar SNR subject to communication, power, and reflection-amplitude constraints.
- The considered system simultaneously serves K communication users and detects a target blocked by obstacles with an M-element active RIS.
- The BS transmit signal combines communication symbols and radar signals through communication and radar beamforming matrices.
- Each active-RIS reflection coefficient has adjustable amplitude and phase, while the RIS also introduces dynamic noise.
- The radar echo is modeled through the BS-RIS-target-RIS-BS path because the direct BS-target link is unavailable in the considered setting.
- The optimization jointly designs W, φ, and u to maximize radar SNR subject to user QoS, BS and RIS power budgets, and the maximum reflection amplitude.
- The resulting problem is non-convex because of complicated multi-variable coupling, so it is decomposed into tractable subproblems solved alternately.
A. Receive Filter Design
With the transmit beamformer and active-RIS coefficients fixed, receive-filter design becomes a generalized Rayleigh-quotient problem. Its optimum is obtained from the dominant eigenvector of B^-1A.
- The receive-filter subproblem is formed with fixed transmit beamforming W and active-RIS reflection coefficients φ.
- The resulting optimization is a generalized Rayleigh quotient whose optimal receive filter is the eigenvector associated with the largest eigenvalue of B^-1A.
B. Transmit Beamforming Design
With the receive filter and RIS reflection coefficients fixed, transmit beamforming is optimized through an MM surrogate. First-order Taylor expansion produces a tractable convex subproblem for each iteration.
- The transmit-beamforming subproblem optimizes W while fixing the receive filter u and reflection coefficients φ.
- The objective w^HYw is convex because Y is positive-semidefinite Hermitian, making direct maximization non-convex.
- MM uses a first-order Taylor expansion at the current iterate to construct a tractable lower-bound surrogate for w^HYw.
- The resulting transmit-beamformer problem is convex and can be solved with efficient optimization algorithms.
C. Reflection Coefficients Design
The reflection-coefficient subproblem is transformed through Dinkelbach’s transform and MM approximations into a convex optimization problem that can be solved iteratively.
- Dinkelbach’s transform reformulates the fractional reflection-coefficient objective into an explicit form with respect to φ.
- MM constructs tractable surrogate functions for quartic terms in the reflection coefficients using second-order Taylor expansions and eigenvalue-based bounds.
- The reflection-power expression P(φ) is rewritten after extracting φ and applying transformations that expose its dependence on the reflection coefficients.
- The resulting optimization problem is convex and can be solved optimally using existing optimization algorithms or solvers such as CVX.
D. Summary
The proposed algorithm alternately updates the radar receive filter, transmit beamformer, and active RIS reflection coefficients until convergence.
- The algorithm iteratively updates u, W, and φ until convergence, with approximate complexity O([N(K + N)]^4.5 + M^4.5).
IV. SIMULATION RESULTS
Simulations evaluate radar SNR against transmit power, SINR requirements, and RIS size under active-RIS-assisted ISAC settings. Active RIS substantially improves radar performance over passive RIS, while communication requirements create a radar-sensing trade-off.
- The simulations use K = 4 users, M = 32 active-RIS elements, equal user SINR requirements, and PRIS = 20dBm.
- Fig. 2 compares radar SNR versus transmit power for active-RIS ISAC, passive-RIS ISAC, and active-RIS radar-only schemes.
- 32dB radar performance improvement is achieved by active-RIS ISAC over passive-RIS ISAC for amax = 8 when PBS ≤42dBm.
- The active-RIS ISAC scheme incurs about 3dB radar performance loss relative to the radar-only scheme when PBS ≤42dBm, while fixed PRIS limits gains as PBS increases.
- Higher SINR requirements degrade radar sensing performance, although this trade-off is not obvious for Γ between 8dB and 16dB.
- Radar SNR increases with the number of reflection elements M, and active RIS dramatically outperforms passive RIS across different M values.
V. CONCLUSIONS
The paper develops an efficient joint-design approach for active RIS-aided ISAC and reports its effectiveness in simulation studies. The design targets radar SNR while preserving communication performance.
- The proposed design jointly optimizes the transmit beamformer, active RIS reflection, and radar receive filter.
- An efficient algorithm combines block coordinate descent, Dinkelbach’s transform, and majorization-minimization methods.
- Radar SNR γr is evaluated against the number of RIS elements M for PBS = 35dBm and Γ = 12dB, with N = 16 and N = 8 configurations.
- Simulation studies demonstrate significant advantages from deploying active RIS in ISAC systems.