An LLM-Based Framework for Beamforming Optimization

Beamforming optimization is fundamental to maximizing performance in wireless communication systems. However, traditional optimization algorithms are computationally intensive, and conventional deep learning models often struggle with high-dimensional solution spaces. This article introduces a novel large language model (LLM)- based framework for beamforming optimization in multiuser multiple-input single-output (MU-MISO) systems. The proposed approach integrates multi-head attention, low-rank adaptation (LoRA), and structural priors of optimal beamforming to enable efficient and scalable learning. We present three representative use cases. First, the framework is applied to conventional MU-MISO beamforming optimization to maximize the downlink sum rate under transmit power constraints. Second, to address CSI aging, we extend the model to jointly perform channel prediction and beamforming. Finally, we incorporate a fluid antenna system (FAS) and develop a joint port selection and beamforming strategy using a differentiable relaxation technique. In all scenarios, the proposed LLM-based approach consistently outperforms conventional neural network baselines in sum rate performance while reducing computational overhead. These results demonstrate the potential of LLMs as a powerful tool for physical- layer optimization in future wireless networks.