Multi-static ISAC based on network-assisted full-duplex cell-free networks: performance analysis and duplex mode optimization

• Published in: Science China. Information sciences Vol. 68; no. 5; p. 150303
• Main Authors: Zeng, Fan, Liu, Ruoyun, Sun, Xiaoyu, Yu, Jingxuan, Li, Jiamin, Zhu, Pengcheng, Wang, Dongming
• Format: Journal Article
• Language: English
• Published: Beijing Science China Press 01.05.2025; Springer Nature B.V
• Subjects: Algorithms; Closed form solutions; Communication; Computer Science; Exact solutions; Information Systems and Communication Service; Optimization; Optimization algorithms; Pareto optimum; Research Paper; interference suppression; multi-static integrated sensing and communication; access point duplex mode optimization; multi-objective optimization; network-assisted full-duplex system
• ISSN: 1674-733X; 1869-1919
• DOI: 10.1007/s11432-024-4381-8

Multi-static integrated sensing and communication (ISAC) technology, which can achieve a wider coverage range and avoid self-interference, is an important trend for the future development of ISAC. Existing multi-static ISAC designs are unable to support the asymmetric uplink (UL)/downlink (DL) communication requirements in the scenario while simultaneously achieving optimal sensing performance. This paper proposes a design for multi-static ISAC based on network-assisted full-duplex (NAFD) cell-free networks that can well solve the above problems. Under this design, closed-form expressions for the individual communication rate and localization error rate are derived under imperfect channel state information, which are respectively utilized to assess the communication and sensing performances. Then, we propose a deep Q -network-based access point (AP) duplex mode optimization algorithm to obtain the trade-off between communication and sensing from the UL and DL perspectives of the APs. Simulation results demonstrate that the NAFD-based ISAC system proposed in this paper can achieve significantly better communication performance than other ISAC systems while ensuring minimal impact on sensing performance. Then, we validate the accuracy of the derived closed-form expressions. Furthermore, the proposed optimization algorithm achieves performance comparable to that of the Pareto optimal solutions with low complexity.