Joint User Association and Beamforming Design for ISAC Networks With Large Language Models

Integrated sensing and communication (ISAC) has been envisioned to play a more important role in future wireless networks. However, the design of ISAC networks is challenging, especially when there are multiple communication and sensing (C&S) nodes and multiple sensing targets. We investigate a multi-base station (BS) ISAC network in which multiple BSs equipped with multiple antennas simultaneously provide C&S services for multiple ground communication users (CUs) and targets. To enhance the overall performance of C&S, we formulate a joint user association (UA) and multi-BS transmit beamforming optimization problem with the objective of maximizing the total sum rate of all CUs while ensuring both the minimum target detection and parameter estimation requirements in terms of the radar signal-to-noise ratio (SNR) and the Cramér-Rao bound (CRB), respectively. To efficiently solve the highly non-convex mixed integer nonlinear programming (MINLP) optimization problem, we propose an alternating optimization (AO)-based algorithm that decomposes the problem into two sub-problems, i.e., UA optimization and multi-BS transmit beamforming optimization. Inspired by the huge potential of large language models (LLMs) for prediction and inference, we propose a unified framework integrating LLMs with convex-based optimization methods to benefit from the theoretical rigor and convergence guarantees of convex-based methods, and the adaptability and flexibility of LLMs. First, we propose a comprehensive design of prompt engineering based on in-context, few-shot, chain of thought, and self-reflection techniques to guide LLMs in solving the binary integer programming UA optimization problem. Second, we utilize convex-based optimization methods to handle the non-convex beamforming optimization problem based on fractional programming (FP), majorization minimization (MM), and the alternating direction method of multipliers (ADMM) with an optimized UA from LLMs. Numerical results demonstrate that our proposed LLM-enabled AO-based algorithm achieves fast convergence and near upper-bound performance with the GPT-o1 model, outperforming various benchmark schemes, which shows the advantages of integrating LLMs into convex-based optimization for wireless networks.