Joint Beamforming and Position Optimization for Fluid RIS-aided ISAC Systems

TL;DR

This work studies joint beamforming and position optimization in a fluid RIS ($fRIS$) aided ISAC system, where movable RIS elements add extra spatial degrees of freedom to simultaneously improve sensing and communication. It formulates a non-convex objective that combines sensing beampattern mismatch $\\varepsilon_r$ and symbol estimation error $\\varepsilon_c$, and optimizes the transmit waveform $\\mathbf{x}$, fRIS phase shifts $\\boldsymbol{\\Theta}$, element positions $\\mathbf{p}$, the symbol estimator $\\omega$, and sensing scale $\\beta$. An alternating minimization framework is developed, integrating augmented Lagrangian methods, semidefinite relaxation, and majorization-minimization to transform high-order terms into tractable forms and solve subproblems efficiently. Numerical results demonstrate that the proposed $fRIS$-based design outperforms conventional RIS-aided ISAC benchmarks in both sensing accuracy and communication BER, and can compensate for reduced RIS element counts by leveraging additional spatial DoFs. This approach offers practical gains in deployment flexibility and computational efficiency for integrated sensing and communication systems.

Abstract

A fluid reconfigurable intelligent surface (fRIS)-aided integrated sensing and communication (ISAC) system is proposed to enhance multi-target sensing and multi-user communication. Unlike the conventional RIS, the fRIS employs movable elements with adjustable positions, offering additional spatial degrees of freedom. In this system, a joint optimization problem is formulated to minimize sensing beampattern mismatch and symbol estimation error. An algorithm based on alternating minimization is devised to handle the resultant non-convex problem, where the subproblems are solved via augmented Lagrangian method, quadratic programming, semidefinite relaxation, and majorization-minimization. A key challenge is that the element positions affect both incident and reflective channels, leading to the high-order composite objective functions. As a remedy, the high-order terms are transformed into linear and linear-difference forms by exploiting the structural characteristics of fRIS and the channels. Numerical results demonstrate the superiority of the proposed scheme over conventional RIS-aided ISAC and other benchmarks.

Figures (6)

• Figure 1: A scenario of a fRIS-aided ISAC system that performs multi-target sensing and multi-user communication.
• Figure 2: Convergence behaviors: (a) The proposed algorithm and the benchmarks: the objective function value $\varepsilon$ versus the iteration step. (b) Fluctuations with a fixed channel realization and random initialization parameters.
• Figure 3: Beampattern at the fRIS: beam gain (dB) versus spatial angle ($^\circ$). (a) The beampattern when biased to the communication ($\alpha=0.1$). (b) The beampattern when no bias ($\alpha=0.5$). (c) The beampattern when biased to the sensing ($\alpha=0.9$). (d) The beampattern of different approaches when $\alpha=0.5$.
• Figure 4: Beampatterns (dB) of different system configurations: (a) Angles Separation: $5^\circ$, Element number: $16$, Size: $8\lambda$. (b) Angles Separation: $10^\circ$, Element number: $16$, Size: $8\lambda$. (c) Angles Separation: $5^\circ$, Element number: $16$, Size: $12\lambda$. (d) Angles Separation: $5^\circ$, Element number: $64$, Size: $8\lambda$. (e) Angles Separation: $10^\circ$, Element number: $64$, Size: $8\lambda$. (f) Angles Separation: $5^\circ$, Element number: $64$, Size: $12\lambda$.
• Figure 5: (a) Average BER (dB) versus different receive noise power (dBm) under different modulations and weighting coefficients. (b) Average BER (dB) versus different receive noise power (dBm) with different approaches.