STARS-assisted Near-field ISAC: Sensor Deployment and Beamforming Design

• Published in: IEEE transactions on wireless communications p. 1
• Main Authors: Xue, Na, Mu, Xidong, Chen, Yue, Liu, Yuanwei
• Format: Journal Article
• Language: English
• Published: IEEE 06.10.2025
• Subjects: Approximation algorithms; beamformer design; Costs; Estimation; Integrated sensing and communication; Integrated sensing and communication (ISAC); Near-field (NF); Noise measurement; Reconfigurable intelligent surfaces; Reconfigurable intelligent surfaces (RISs); sensor deployment; Stars; Vectors; Wireless communication; Wireless sensor networks
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/TWC.2025.3616074

A simultaneously transmitting and reflecting surface (STARS) assisted near-field (NF) integrated sensing and communication (ISAC) framework is proposed, where the radio sensors are installed on the STARS to directly conduct the distance-domain sensing by exploiting the spherical wavefront. A new squared position error bound (SPEB) expression is derived to reveal the dependence on beamforming (BF) design and sensor deployment. To balance the trade-off between the SPEB and the sensor deployment cost, a cost function minimization problem is formulated to jointly optimize the sensor deployment, the active and passive BF, subject to communication and power consumption constraints. For the sensor deployment optimization, a joint sensor deployment algorithm is proposed by invoking the successive convex approximation. Under a specific relationship between the sensor numbers and BF design, we derive the optimal sensor interval in a closed-form expression. For the joint BF optimization, a penalty-based method is invoked. Simulation results validated that the derived SPEB expression is close to the exact SPEB, which reveals the Fisher Information Matrix of position estimation in NF can be approximated as a diagonal matrix. Furthermore, the proposed algorithms achieve the best SPEB performance compared to the benchmark schemes accompanying the lowest deployment cost.