STARS Enabled Integrated Sensing and Communications

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 sens...

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Vydáno v:IEEE transactions on wireless communications Ročník 22; číslo 10; s. 1
Hlavní autoři: Wang, Zhaolin, Mu, Xidong, Liu, Yuanwei
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.10.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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
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Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
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ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2023.3245297