Distributed Unsupervised Learning for Interference Management in Integrated Sensing and Communication Systems

• Published in: IEEE transactions on wireless communications 2023-12, Vol.22 (12), p.1-1
• Main Authors: Liu, Xiangnan, Zhang, Haijun, Long, Keping, Nallanathan, Arumugam, Leung, Victor C. M.
• Format: Article
• Language: English
• Subjects: Algorithms; Array signal processing; Artificial neural networks; Base stations; Beamforming; Communications systems; Integrated sensing and communication; Interference; interference management; Machine learning; Radar; Radio equipment; Sensors; Transfer learning; Unsupervised learning
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/TWC.2023.3269815

Nowadays, the multi-access interference problem in the ISAC systems can not be ignored. The study on interference management in ISAC has been envisioned as one of key technologies to support ubiquitous sensing functions. Different from the current work, a communications-sensing-intelligence converged network architecture is proposed to coordinate interference in this paper. Each base station equips with the individual deep neural networks to allocate power and beamforming. On this basis, the interference management is transformed into a functional optimization with stochastic constraints. An unsupervised learning algorithm is proposed to allocate power for interference management. Furthermore, a transfer learning method is presented to obtain the interference management in terms of transmit beamforming. Finally, the distributed management is obtained from the local channel state information in the multi-cell scenario. Simulation results verify the effectiveness of the proposed unsupervised learning interference management method in the ISAC systems.