LLM-Assisted Optimisation of Multi-RIS Placement and Beamforming in Smart Warehouses

Abstract

In this paper, we propose an optimisation framework for deployment of multiple reconfigurable intelligent surfaces (RISs) to meet the wireless coverage demands for smart warehouses. Specifically, we are the first to formulate a unified network optimisation task that jointly considers RIS placement and beamforming to maximize overall network coverage with a deterministic channel model to accurately describe the multipath effect for the warehouse. To address this problem, we design a hybrid optimisation framework composed of three synergistic modules. (1) A Large Language Model (LLM) acts as a semantic planner that generates physically feasible multi-RIS configurations, jointly determining the placement and beamforming directions guided by structured prompts and environment-aware embeddings. (2) A Genetic Algorithm (GA) module performs local numerical refinements to enhance the precision of LLMgenerated solutions under physical constraints. (3) A Diversity Reflection and Correction (DiRect) module evaluates structural similarity among candidate configurations and triggers additional semantic regeneration to maintain exploration diversity. These three modules form an alternating iterative process in which LLM reasoning, GA-based evolution, and DiRect-driven regeneration collectively guide the optimisation toward high-coverage configurations. Extensive simulations validate the effectiveness and robustness of the proposed framework. Compared with traditional heuristics, reinforcement learning methods, and LLMguided baselines, our hybrid framework achieves 10%-15% higher coverage within 10-20 iterations. The performance consistently scales with the number of RISs and element sizes, and remains stable under varying transmitter positions, demonstrating strong adaptability to complex smart warehouse layouts. Overall, the proposed hybrid optimisation framework provides a scalable and physically grounded solution for RIS-assisted network deployment optimisation in realistic in.