Map-aided ISAC Beam Design via Active Sensing

Artikel i vetenskaplig tidskrift, 2026

Initial beam alignment for analog arrays often relies on exhaustively sweeping beams over predefined codebooks when no prior information is available, leading to substantial pilot overhead before reliable data transmission and accurate localization can be achieved. This letter proposes a map-aided active beam-training framework for joint communication and localization in a single-user uplink system. During beam training, the BS exploits environmental geometry to adaptively design probing beams, thereby improving the communication-and-localization performance under a fixed pilot budget. The measurement policy is implemented by a long short-term memory (LSTM) network with a deep neural network (DNN) head that maps the history of observations to a beamforming vector, optimized under a tunable tradeoff between the localization Cramér–Rao bound (CRB) and the communication objective function. Simulation results show that the learned policy steers early beams to explore both direct and reflected paths and then concentrates energy on two paths simultaneously, achieving a higher rate and a lower CRB for the UE than baseline methods.