Sensing-Aided Simultaneous Multi-BS Beam Alignment Strategy for Cooperative Scenarios

Beam alignment (BA), a core component of beamforming, is essential for achieving optimal directional gain in high-frequency communications. However, in single base station (BS) scenarios, BA may fail when the line-of-sight (LoS) path is obstructed. Multi-BS cooperation can effectively address this issue by leveraging the LoS paths from other BSs. However, applying traditional single-BS BA schemes to multi-BS cooperation scenarios leads to a substantial training overhead. To address this, we propose a sensing-aided simultaneous multi-BS BA (SSMBA) strategy in this paper. In the first phase, compressed sensing (CS) is utilized to enable simultaneous multi-beam training across multiple BSs, significantly reducing training overhead. In the second phase, leveraging integrated sensing and communication (ISAC) capabilities, a sensing-aided beam refinement technique is introduced to further increase BA accuracy and beamforming gain. The simulation results demonstrate that the proposed SSMBA strategy has advantages in both reducing the training overhead and increasing beamforming gain.