Integrated Sensing and Communication with Reconfigurable Distributed Antenna and Reflecting Surface: Joint Beamforming and Mode Selection

TL;DR

This paper introduces a novel integrated sensing and communication framework using reconfigurable distributed antenna and reflecting surfaces, optimizing joint beamforming and mode selection to improve performance and reduce costs.

Contribution

It proposes a new RDARS-aided ISAC framework that mitigates fading effects and reduces costs compared to existing RIS-based systems, with an efficient optimization algorithm.

Findings

Superior performance demonstrated through simulations

Effective mitigation of multiplicative fading effects

Significant gains from proper RDARS configuration

Abstract

This paper presents a new integrated sensing and communication (ISAC) framework, leveraging the recent advancements of reconfigurable distributed antenna and reflecting surface (RDARS). RDARS is a programmable surface structure comprising numerous elements, each of which can be flexibly configured to operate either in a reflection mode, resembling a passive reconfigurable intelligent surface (RIS), or in a connected mode, functioning as a remote transmit or receive antenna. Our RDARS-aided ISAC framework effectively mitigates the adverse impact of multiplicative fading when compared to the passive RIS-aided ISAC, and reduces cost and energy consumption when compared to the active RIS-aided ISAC. Within our RDARS-aided ISAC framework, we consider a radar output signal-to-noise ratio (SNR) maximization problem under communication constraints to jointly optimize the active transmit beamforming matrix of the base station (BS), the reflection and mode selection matrices of RDARS, and the receive filter. To tackle the inherent non-convexity and the binary integer optimization introduced by the mode selection in this optimization challenge, we propose an efficient iterative algorithm with proved convergence based on majorization minimization (MM) and penalty-based methods.Numerical and simulation results demonstrate the superior performance of our new framework, and clearly verify substantial distribution, reflection as well as selection gains obtained by properly configuring the RDARS.