Large-Language-Model Based Beamforming Prediction for Sensing-Aided Communication

Integrated sensing and communication (ISAC) is expected to enable the fifth-generation (5G) networks to provide ubiquitous communication and sensing. However, some highdynamic scenarios hinder applications of conventional ISAC schemes owing to the high overhead. In this paper, we design a novel ISAC architecture and propose a large language model (LLM) based two-stage beamforming prediction scheme. Specifically, in the first stage, we develop an LLM-based approach to predict the future channel state information (CSI) according to the history echoes. In the second stage, according to the predicted/estimated CSI, we formulate a beamforming optimization problem to maximize the achievable sum rate. Then, we propose a Primary-dual network to handle it. Simulation results verify that, compared with the benchmarks, our proposed beamforming prediction scheme not only enjoys a higher channel prediction accuracy but also achieves a better balance between the performance and computational complexity.