BeamAgent: LLM-Aided MIMO Beamforming with Decoupled Intent Parsing and Alternating Optimization for Joint Site Selection and Precoding

Abstract

Integrating large language models (LLMs) into wireless communication optimization is a promising yet challenging direction. Existing approaches either use LLMs as black-box solvers or code generators, tightly coupling them with numerical computation. However, LLMs lack the precision required for physical-layer optimization, and the scarcity of wireless training data makes domain-specific fine-tuning impractical. We propose BeamAgent, an LLM-aided MIMO beamforming framework that explicitly decouples semantic intent parsing from numerical optimization. The LLM serves solely as a semantic translator that converts natural language descriptions into structured spatial constraints. A dedicated gradient-based optimizer then jointly solves the discrete base station site selection and continuous precoding design through an alternating optimization algorithm. A scene-aware prompt enables grounded spatial reasoning without fine-tuning, and a multi-round interaction mechanism with dual-layer intent classification ensures robust constraint verification. A penalty-based loss function enforces dark-zone power constraints while releasing optimization degrees of freedom for bright-zone gain maximization. Experiments on a ray-tracing-based urban MIMO scenario show that BeamAgent achieves a bright-zone power of 84.0\,dB, outperforming exhaustive zero-forcing by 7.1 dB under the same dark-zone constraint. The end-to-end system reaches within 3.3 dB of the expert upper bound, with the full optimization completing in under 2 s on a laptop.

Figures (6)

• Figure 1: Overview of the BeamAgent framework. The system consists of three functional stages separated into two domains by the structured constraint interface $\mathcal{C} = (\mathcal{B}, \mathcal{D}, \mathcal{R}_{\mathrm{tx}})$. In the semantic domain (left), an LLM parses natural language input into spatial constraints via a scene-aware prompt, followed by multi-round interaction with dual-layer intent classification for constraint verification. In the numerical domain (right), an alternating optimization algorithm jointly solves BS site selection (Phase A) and precoding design (Phase B), with a threshold-based penalty that releases optimization degrees of freedom once the dark-zone constraint is satisfied.
• Figure 2: Bright-zone power $\bar{P}_{\mathcal{B}}$ versus maximum dark-zone power $\max P_{\mathcal{D}}$ for all methods after post-processing. The dashed line marks the dark-zone threshold $T=30$ dB. Points below this line are feasible. BeamAgent achieves the highest $\bar{P}_{\mathcal{B}}$ in the feasible region while operating near the constraint boundary.
• Figure 3: Per-site received power (bars) and SVD bounds (triangles) at the optimized BS location. Black triangles ($\blacktriangledown$) denote $\sigma_{\max}^2$ and gray triangles ($\vartriangle$) denote $\sigma_{\min}^2$. The dashed line marks $T=30$ dB. Bright Sites approach their upper bounds; dark Sites are suppressed to the threshold.
• Figure 4: Spatial distribution of received power (dB) in the 224 m $\times$ 222 m urban scenario after joint optimization. Green circles indicate bright Sites (7, 8); red squares indicate dark Sites (6, 12); the star marks the optimized BS location. The beam pattern steers energy toward the north while suppressing the south and center.
• Figure 5: Bright-dark contrast (dB) under four LLM integration modes using Claude Sonnet 4.6. BeamAgent (decoupled parsing + dedicated optimizer) achieves within 3.3 dB of the expert upper bound. LLM Direct W, which outputs the precoding vector without channel information, yields the lowest contrast among LLM-involved modes.