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Hybrid RIS-Enhanced ISAC Secure Systems: Joint Optimization in the Presence of an Extended Target

Yu Yao, Junhao Zhang, Pu Miao, Long Zhang, Gaojie Chen, Feng Shu, Kai-Kit Wong

arXiv:2505.20012v1physics.ins-det

TL;DR

The paper addresses secure ISAC with multiple hybrid RISs when the extended target location is imperfectly known. It jointly optimizes transmission, reception, and RIS weights using GFP, PDD, and convex optimization to maximize worst-case sensing SINR under communication and security constraints. Simulations report improved extended-target detection and secure transmission over state-of-the-art RIS-aided ISAC approaches.

  • Problem

    Secure ISAC must simultaneously communicate confidential information and sense an extended target despite imperfect target-location knowledge and eavesdropping concerns.

  • Method

    The framework jointly designs BS transmit and receive processing, user receive beamformers, and multiple hybrid-RIS weights using GFP, PDD, PCCP, and convex subproblems.

  • Results

    Simulations show improved extended-target detection and secure transmission performance over state-of-the-art RIS-aided ISAC approaches.

  • Takeaways & Limitations

    Multiple hybrid RISs can enhance secure ISAC performance, with capability gains increasing as total power and the number of reflection units increase.

Abstract

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Unlike the conventional fully-passive and fully-active reconfigurable intelligent surfaces (RISs), a hybrid RIS consisting of active and passive reflection units has recently been concerned, which can exploit their integrated advantages to alleviate the RIS-induced path loss. In this paper, we investigate a novel security strategy where the multiple hybrid RIS-aided integrated sensing and communication (ISAC) system communicates with downlink users and senses an extended target synchronously. Assuming imperfectly known target location (TL), we consider the joint design of the transmit signal and receive filter bank of the base station (BS), the receive beamformers of all users and the weights of the hybrid RIS. An optimization problem is formulated for maximizing the worst-case sensing signal-to-interference-plus-noise-ratio (SINR) subject to secure communication and system power budget constraints. To address this non-convex problem, we leverage generalized fractional programming (GFP) and penalty-dual-decomposition (PDD), and propose a security solution that efficiently optimizes all variables by employing convex optimization approaches. Simulation results show that by incorporating the multiple hybrid RIS into the optimization design, the extended target detection and secure transmission performance of ISAC systems are improved over the state-of-the-art RIS-aided ISAC approaches.

I. INTRODUCTION

The paper develops a secure multiple hybrid RIS-aided ISAC framework for simultaneously serving users and sensing an extended target despite uncertain target location. It jointly optimizes transmit and receive processing and hybrid-RIS weights, using GFP, PDD, and convex subproblem methods to improve worst-case sensing and secure transmission performance.

  • ISAC security is challenged because wireless transmissions are broadcast and detected targets may eavesdrop on confidential user information.
  • Passive RISs suffer propagation attenuation, while hybrid RISs combine active amplification and passive reflection to mitigate this limitation.
  • The proposed system serves multi-antenna secure communication users and detects a single-antenna extended-target Eve simultaneously using multiple hybrid RISs.
  • With uncertain target location, the design makes multiuser interference constructive for secure users and destructive for the eavesdropping target.
  • The optimization maximizes worst-case sensing SINR while enforcing communication QoS, security, RIS-weight, and total-power constraints.
  • GFP handles variable coupling, while PDD and PCCP decompose the hybrid-RIS design into tractable quadratic or convex subproblems.
  • Numerical results show better performance than state-of-the-art benchmarks and increasing capability gains with total power and the number of hybrid-RIS reflection units.

B. Sensing Model

The sensing model represents an extended target with location uncertainty, target and clutter returns, receiver noise, and a space-time filter bank evaluated over possible target locations.

  • The target location is uncertain within angular intervals around estimated elevation and azimuth angles.
  • The model defines effective channels, steering vectors, and path losses for target and clutter returns through the BS and multiple RISs.
  • The extended target is modeled as multiple scattering points characterized by a target impulse response sensitive to target aspect angle.
  • The received radar observations include target echoes, clutter, and additive noise at active RIS units and the BS.
  • A bank of space-time linear receive filters processes observations for candidate target locations, and detection performance improves monotonically with received SINR at fixed false-alarm probability.

C. Problem Formulation

The formulation jointly designs transmit and receive processing and hybrid-RIS weights to maximize worst-case sensing SINR while enforcing communication, security, RIS, and power constraints.

  • The design variables include the BS transmit waveform, BS receive filters, SCU receive beamformers, and hybrid-RIS weights.
  • The objective maximizes sensing SINR over all possible target locations.
  • The optimization imposes CI-type communication QoS, DI-type security, RIS weight, and total power budget constraints.
  • The problem is challenging because its objective is quartic and its optimization variables are highly coupled.

III. HYBRID RIS-ENHANCED ISAC OPTIMIZATION

The proposed solution decomposes the coupled hybrid-RIS optimization into sequential subproblems and solves them using GFP, PDD, PCCP, and convex optimization techniques.

  • The receive-filter subproblem is formulated as an MVDR problem with a closed-form solution.
  • The SCU receive-beamformer subproblem is optimized with the other variables fixed.
  • Binary relaxation variables and a large factor reformulate the DI security constraint before optimizing transmit waveforms and hybrid-RIS weights.
  • GFP introduces auxiliary variables for the max-min-ratio problem and iteratively updates them with the design variables.
  • Each GFP iteration requires a convex problem, and the objective converges monotonically to the optimal value of the reformulated problem.

B. Solve for η and x

The η and waveform subproblems are solved iteratively using first-order approximation and convex optimization, with updates continued until a specified convergence threshold is met.

  • The η-subproblem uses a first-order Taylor expansion of the relevant function.
  • With other variables fixed, the η optimization is solved subject to relaxed bounds 0 ≤ η_n,p ≤ 1.
  • The η variables are updated until the change falls below the convergence threshold ε_η.
  • The waveform subproblem is convex because its relevant constraint is concave with respect to x, allowing solution by solvers such as CVX.

C. Solve for Θ

The hybrid RIS weight problem is transformed through continuous relaxation, PDD-based alternating optimization, and projection onto discrete reflection coefficients. GFP, PDD, and PCCP yield convex subproblems for the coupled non-convex design.

  • Discrete reflection optimization: The discrete hybrid RIS weight optimization is relaxed to continuous constraints before solving and projecting to a feasible discrete solution.The relaxed solution is projected to the nearest feasible point in the discrete constraint set, producing a suboptimal solution for the non-convex problem.
  • Problem transformation: The quartic sensing-SINR term is decomposed into a quadratic form to simplify hybrid RIS weight optimization.The high-order term originates from the sensing-signal transmission channel and is reduced to make the subproblem easier to solve.
  • PDD optimization: PDD introduces a copy variable and alternately updates the hybrid RIS variables within an inner block-coordinate-descent loop and an outer penalty-dual loop.The auxiliary problem is convex in the copy variable, while the penalty factor or dual variable is updated after inner-layer convergence.
  • Convex approximation: PCCP linearizes the non-convex unit-modulus constraint and solves the resulting SOCP with CVX while penalizing constraint violations.The penalty term is scaled by a regularization factor, and sufficiently small slack enforces the unit-modulus constraint.
  • Overall algorithm: The complete GFP-based algorithm iteratively optimizes the coupled variables and has complexity determined by outer iterations and the constituent subproblem solvers.Its complexity includes updates for the transmit design, receive filters, SCU beamformers, and the PDD procedure.

IV. NUMERICAL RESULTS

The numerical study evaluates convergence under target-location uncertainty and compares the proposed scheme with RIS benchmarks using specified system, noise, channel, and stopping parameters. Larger uncertainty sets reduce worst-case sensing SINR, while the algorithm converges monotonically.

  • Simulation setup: The simulations use a BS with Na = 8 antennas, K = 3 SCUs, Nr = 4 receive antennas, NI = 30 hybrid RIS units, and A = 6 active units.The default maximum BS and hybrid-RIS power budget is 20 dBm, with SCU and Eve SINR thresholds of 10 dB and −1 dB.
  • Simulation setup: The channel model uses a 35.3 + 37.6log10lab dB path-loss expression, 4 dB Rician coefficients, and specified 30 m and 3 m transmission distances.The BS-to-Eve, BS-to-RIS, and BS-to-SCU distances are 30 m, while RIS-to-Eve and RIS-to-SCU distances are 3 m.
  • Convergence and uncertainty: Worst-case sensing SINR increases monotonically with iterations, while larger target-location uncertainty sets produce lower minimum sensing SINR.The algorithm converges faster when the BS has perfect target-location knowledge.
  • Benchmark methods: The benchmarks include No-RIS, optimized passive RIS, random passive RIS, and optimized active RIS configurations.The proposed method is evaluated against designs that remove the RIS, optimize passive coefficients, randomize passive coefficients, or use fully active RIS units.

A. Single Hybrid RIS

Single-hybrid-RIS experiments examine sensing performance against RIS size, power budgets, active-unit count, communication QoS, and filter-array size. The results show gains from optimized hybrid RIS design, especially when adding initial active units or increasing RIS scale and system power.

  • RIS size: At NI = 20, Algorithm 3 achieves 76.7% higher SINR than optimized passive RIS, rising to 110.4% at NI = 50.The proposed method improves faster than the passive-RIS baseline as the number of hybrid RIS units increases, balancing hardware cost and performance.
  • Power budget: Increasing the BS power budget improves sensing SINR for all approaches, while the proposed method follows the ordering No-RIS, random passive RIS, optimized passive RIS, then Algorithm 3.The comparison considers hybrid-RIS power budgets of 5 dBm, 10 dBm, and 20 dBm.
  • Active-unit count: When A = 1, sensing SINR is 17.5% greater than at A = 0, whereas A = 10 is only 2.1% greater than A = 9.The largest gains occur when introducing a small number of active units into an otherwise passive RIS.
  • Communication QoS: Increasing Γk from 10 dB to 20 dB has almost no effect on target sensing capability within this practical QoS range.The study nevertheless observes a tradeoff between sensing capability and high communication QoS.
  • Filter array: Increasing the filter-array size improves worst-case sensing SINR when the target-location set is fixed.The result is reported as beneficial to target sensing capability.
  • Secure communication: Increasing Γk reduces the average SCU SER, while Eve’s SER remains about 0.8 under DI-type design.The results attribute Eve’s high SER to destructive-area transmission while maintaining SCU reception quality.

B. Multiple Hybrid RIS

The simulations show that optimized multiple hybrid RISs improve sensing and secure communication performance, while poorly designed phase shifts can introduce substantial interference. Performance gains persist under target-location uncertainty and increase with active-RIS power, whereas DI constraints strongly hinder eavesdropping.

  • Power-budget effects: Optimized multi-RIS phase shifts provide the strongest sensing performance across BS power budgets because they yield higher indirect channel gain.Random phase shifts can make multi-RIS sensing worse than single-RIS sensing because clutter interference may exceed useful target-signal gain.
  • Power-budget effects: Increasing active-RIS power noticeably enhances worst-case sensing SINR for optimized multi-RIS systems relative to non-RIS and single-RIS cases.With random phase shifts, increasing active-RIS power may instead deteriorate sensing SINR, underscoring the importance of phase-shift optimization.
  • Target uncertainty: Multiple hybrid RISs retain remarkable sensing improvements over multiple passive RISs and non-RIS systems despite performance loss from target-location CSI errors.The improvement is especially pronounced when the angle difference between the SCU and Eve is low, revealing a tradeoff involving sensing performance and location uncertainty.
  • Secure communication: DI constraints raise Eve’s SER noticeably, approaching 1 as the SCU–Eve angular difference increases, whereas CI-only decoding probability converges to 0.8.Designed multi-RIS phase shifts also outperform single-RIS designs in secure communication performance.
  • Discrete RIS control: Increasing DRC resolution slowly raises DRC sensing SINR toward CRC performance, while the proposed scheme exceeds Gaussian randomization and random-DRC benchmarks.The results attribute the proposed advantage to reducing communication–sensing mutual interference and indicate that careful discrete-RIS configuration is necessary.

V. CONCLUSIONS

The paper develops an efficient joint optimization method for securing multiple hybrid RIS-aided ISAC systems under sensing, communication, security, and power constraints. Simulations show improved performance over benchmark methods, with gains increasing as system power and the number of hybrid-RIS elements grow.

  • Joint design: The method jointly designs BS transmit signals and receive filters, SCU receive beamformers, and hybrid-RIS weights to maximize worst-case sensing SINR under CI and DI constraints.The optimization accounts for all possible target locations and system power budgets.
  • Optimization method: An alternating optimization procedure based on GFP, PDD, and PCCP addresses the non-convex joint design problem.
  • Performance: The proposed multiple hybrid RIS scheme achieves more excellent ISAC performance than state-of-the-art benchmark methods.
  • Performance: Capability enhancement grows with total system power consumption and the number of hybrid-RIS reflection units.

APPENDIX A

The appendix reformulates communication, security, and RIS power constraints into explicit expressions involving the hybrid-RIS variables. These transformations allow the original problem to be expressed in an equivalent optimization form suitable for the proposed solution.

  • Equivalent reformulation: The appendix uses Kronecker-product and reshaping transformations to express the relevant matrix-vector products in equivalent forms.The reshaped variables include X0, X1, and Wp, derived from the transformed transmit and beamforming quantities.
  • Constraint reformulation: CI-type communication QoS and DI-type security constraints are rewritten explicitly with respect to the hybrid-RIS variable ϑ.
  • Constraint reformulation: The RIS power constraint is also written explicitly with respect to ϑ, and the reformulated constraints produce an equivalent version of the optimization problem.
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