Antenna Selection and Beamforming in ELAA-based ISAC System: A Distributed Approach

• Published in: IEEE Global Communications Conference (Online) pp. 1979 - 1984
• Main Authors: Gao, Qian, Zhong, Ruikang, Liu, Yuanwei
• Format: Conference Proceeding
• Language: English
• Published: IEEE 08.12.2024
• Subjects: Antenna arrays; Antennas; Array signal processing; Arrays; Beamforming; Directive antennas; extremely large-scale antenna array (ELAA); Heuristic algorithms; integrated communication and sensing (ISAC); Integrated sensing and communication; multi-agent reinforcement learning (MARL); near-field communication (NFC); Optimization; Signal processing algorithms; Wireless communication
• ISSN: 2576-6813
• DOI: 10.1109/GLOBECOM52923.2024.10901546

An extremely large-scale antenna array (ELAA) based integrated sensing and communication (ISAC) system is investigated. In contrast to existing near-field ISAC systems, we consider the complexity of beamforming design and antenna selection for ELAA under a user-roaming environment. The formulated problem is maximizing long-term sum data rate by jointly optimizing antenna selection and beamforming, subject to the constraints of transmitting power and sensing targets' beam pattern gain. We propose a dimension reduction assisted multi-agent proximal policy optimization (DR-MAPPO) algorithm against the dynamic scenario: 1) an OrderConv layer is designed, which reduces the input dimension by removing the channel state information of unselected antenna while keeping the order information of selected ones, 2) multiple agents are employed to execute antenna selection and beamforming in a distributed manner. Numerical results demonstrate that the proposed DR-MAPPO algorithm improve the computational efficiency of joint antenna selection and beamforming optimization by reducing the size of the input dimension while obtaining comparable data rates and beampattern gain for sensing targets.