STARS Enabled Integrated Sensing and Communications

• Published in: IEEE transactions on wireless communications Vol. 22; no. 10; p. 1
• Main Authors: Wang, Zhaolin, Mu, Xidong, Liu, Yuanwei
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Array signal processing; Clutter; Communication; Constraint modelling; Cramer-Rao bounds; Cramér-Rao bound; Direction-of-arrival estimation; Estimation; Fisher information; integrated sensing and communication (ISAC); Iterative algorithms; Iterative methods; Maximum likelihood estimators; Optimization; Phase shift; Sensors; Signal to noise ratio; simultaneously transmitting and reflecting intelligent surface (STARS); Stars; Waveforms
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/TWC.2023.3245297

A simultaneously transmitting and reflecting surface (STARS) enabled integrated sensing and communications (ISAC) framework is proposed, where the entire space is partitioned by STARS into a sensing space and a communication space. A novel sensing-at-STARS structure is proposed, where dedicated sensors are mounted at STARS to address the significant path loss and clutter interference of sensing. The Cramér-Rao bound (CRB) of the two-dimensional (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 problem of ISAC waveform and STARS coefficient, 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 the coupled phase-shift constraint by alternately optimizing the amplitude and phase-shift coefficients of STARS with closed-form expressions. Finally, the numerical results demonstrate that: 1) STARS significantly outperforms conventional RIS in terms of 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 than the active elements of the sensor; 4) Higher sensing accuracy can be achieved by STARS using the practical 2D maximum likelihood estimator compared with the conventional RIS.