STARS Enabled Integrated Sensing and Communications

Abstract

A simultaneously transmitting and reflecting intelligent surface (STARS) enabled integrated sensing and communications (ISAC) framework is proposed, where the whole space is divided by STARS into a sensing space and a communication space. A novel sensing-at-STARS structure, where dedicated sensors are installed at the STARS, is proposed to address the significant path loss and clutter interference for sensing. The Cramér-Rao bound (CRB) of the 2-dimension (2D) direction-of-arrivals (DOAs) estimation of the sensing target is derived, which is then minimized subject to the minimum communication requirement. A novel approach is proposed to transform the complicated CRB minimization problem into a trackable modified Fisher information matrix (FIM) optimization problem. Both independent and coupled phase-shift models of STARS are investigated: 1) For the independent phase-shift model, to address the coupling of ISAC waveform and STARS coefficient in the modified FIM, an efficient double-loop iterative algorithm based on the penalty dual decomposition (PDD) framework is conceived;2) For the coupled phase-shift model, based on the PDD framework, a low complexity alternating optimization algorithm is proposed to tackle coupled phase-shift constants by alternatively optimizing amplitude and phase-shift coefficients in closed-form. Finally, the numerical results demonstrate that: 1) STARS significantly outperforms the conventional RIS in CRB under the communication constraints;2) The coupled phase-shift model achieves comparable performance to the independent one for low communication requirements or sufficient STARS elements; 3) It is more efficient to increase the number of passive elements of STARS rather than the active elements of the sensor; 4) High sensing accuracy can be achieved by STARS using the practical 2D maximum likelihood estimator compared with the conventional RIS.

A Dual-Function Radar-Communication System Empowered by Beyond Diagonal Reconfigurable Intelligent Surface

“…To address this limitation, a novel hybrid transmissive and reflective RIS, namely simultaneously transmitting and reflecting RIS (STAR-RIS) [28] or intelligent omni-surface [29], is proposed to support signal reflection and transmission and thus extend the coverage. The integration of STAR-RIS and DFRC is first studied in [30], where the system is designed by minimizing the Cramér-Rao bound (CRB) for radar target estimation subject to communication constraints. Then, a STAR-RIS is deployed at the vehicle to improve both sensing and communication performance [31].…”

“…Then, a STAR-RIS is deployed at the vehicle to improve both sensing and communication performance [31]. Nevertheless, the achievable performance of STAR-RIS aided DFRC in [30], [31] is limited by the simple architecture of STAR-RIS without fully exploiting the architecture of RIS.…”

Cramér–Rao Lower Bound Optimization for Hidden Moving Target Sensing via Multi-IRS-Aided Radar

“…Initial research on IRS was limited to wireless communication applications such as range extension to users with obstructed direct links [3], joint wireless information and power transmission [4], physical layer security [5], unmanned air vehicle (UAV) communications [6], and shaping the wireless channel through multi-beam design [7]. Recent works have also introduced IRS to integrated communications and sensing systems [5,[8][9][10]. In this paper, we focus on IRS-aided sensing, following the advances made in [8,11].…”

“…Recent works have also introduced IRS to integrated communications and sensing systems [5,[8][9][10]. In this paper, we focus on IRS-aided sensing, following the advances made in [8,11].…”

“…In contrast, IRSaided radar utilizes the signals received from the NLoS paths to compensate for the end-to-end transmitter-receiver or LoS path loss [11]. The potential of IRS in enhancing the estimation performance of radar systems has been recently investigated in [8,14,15]. Some recent studies such as [14], employ IRS to correctly estimate the direction-of-arrival (DoA) of a stationary target.…”