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

Xiucheng Wang; Yue Zhang; Nan Cheng

cs.ITMar 19, 2026

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

Authors: Xiucheng Wang, Yue Zhang, Nan Cheng

Organizations: State Key Laboratory of ISN and School of Telecommunications Engineering, Xidian University, Xi’an 710071, China

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 & tables

Method	LLM Role	Optimization Layer	NL Interface	Fine-Tuning	Solver
ComAgent [ 14 ]	Code generator	Task-dependent	No	No	LLM-generated code
WirelessAgent [ 15 ]	Decision engine	Network (slicing)	Partial	No	LLM reasoning loop
PE-RTFV [ 17 ]	Black-box solver	Physical (constellation)	No	No	LLM output
NetLLM [ 19 ]	End-to-end model	Network (scheduling)	No	Yes	Fine-tuned LLM
TelecomGPT [ 24 ]	Domain-adapted LLM	Telecom tasks	No	Yes	Fine-tuned LLM
TelePlanNet [ 37 ]	Intent parser + RL	Site planning	Yes	Yes (RL)	RL policy

TABLE I : Comparison of BeamAgent with representative existing methods.

Fig. 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.

Method	$\bar{P}_{\mathcal{B}}$ (dB)	$\max P_{\mathcal{D}}$ (dB)	Scale (dB)	Contrast (dB)
Random	75.9	30.0 †	19.9	43.2
Exh+MRT	77.8	30.0 †	20.3	48.8
Exh+MMSE	78.6	30.0 †	1.0	57.7
Exh+ZF	76.9	14.5	—	70.1
SLNR	76.4	14.3	—	80.9
BeamAgent	84.0	30.0 †	0.9	54.2

TABLE II : Comparison of constraint-aware precoding methods in a 4-Site scenario with dark-zone threshold

T=30

dB. Methods marked with † are post-processed by scaling

\|\mathbf{W}\|

to enforce

\max_{s\in\mathcal{D}}P_{s,\text{dB}}\leq T

. The Scale column indicates the dB reduction applied during post-processing.

Fig. 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.

Fig. 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.

Fig. 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.

Method	Contrast (dB)	Std (dB)	Note
Expert+Optimizer	55.3	0.0	Expert constraints
BeamAgent	52.0	3.6	LLM parsing + optimizer
LLM Direct W	49.4	0.0	LLM outputs $\mathbf{W}$ directly
LLM Gen Code	80.9 ‡	0.0	LLM generates SLNR solver
‡ $\bar{P}_{\mathcal{B}}=76.4$ dB, lower than BeamAgent (84.0 dB); no site selection.

TABLE III : Comparison of four LLM integration modes using Claude Sonnet 4.6. Expert+Optimizer serves as the upper bound with manually specified constraints. BeamAgent uses LLM-parsed constraints with the same optimizer. LLM Direct W and LLM Gen Code represent alternative paradigms where the LLM directly participates in numerical computation.

Fig. 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.

Fig. 6 : Effect of dark-zone threshold

T

on bright-zone power

\bar{P}_{\mathcal{B}}

(green squares) and actual dark-zone power

\max P_{\mathcal{D}}

(red triangles). The dashed line shows the threshold value. As

T

increases, the dark-zone power tracks the threshold and bright-zone power rises accordingly, confirming that relaxing the constraint releases optimization degrees of freedom.

Configuration	$\bar{P}_{\mathcal{B}}$ (dB)	$\max P_{\mathcal{D}}$ (dB)	Contrast (dB)
Full (Ours)	85.8	30.6	55.1
No Cosine Annealing	85.9	30.7	55.2
No Gradient Clipping	85.8	30.6	55.2
No Cosine + No Clip	85.9	30.7	55.3
$R=1$ (No Alternating)	77.6	30.9	46.7
Tradeoff (no penalty)	86.0	25.2	60.7

TABLE IV : Ablation study on optimization components in the 4-Site scenario (

T=30

dB). Each row removes one component from the full configuration. Disabling alternating optimization (

R=1

) causes the only significant performance degradation (

-8.2

dB in

\bar{P}_{\mathcal{B}}

Stage	Time (s)
Constraint preparation (tensor stacking)	1.350
Phase A: Tx scan (per round)	0.006
Phase B: 400 gradient steps	0.308
Full optimization (5 rounds)	1.668
Exhaustive MRT baseline (all Tx)	0.124

TABLE V : Wall-clock time for each optimization stage measured on an Apple M2 MacBook. LLM API latency is excluded. The full pipeline completes in under 2 s, with vectorized Tx scanning taking 6 ms per round.

Wireless Resource Allocation

Wireless resource allocation assigns spectrum, power, beams, and computing capacity to meet throughput, latency, fairness, and energy goals.

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In near-field extremely large-scale multiple-input multiple-output (XL-MIMO) systems, spherical wavefront propagation expands the traditional beam codebook into the joint angular-distance domain, rendering conventional beam training prohibitively inefficient, especially in complex 3-dimensional (3D) low-altitude environments. Furthermore, since near-field beam variations are deeply coupled not only with user positions but also with the physical surroundings, precise beam alignment demands profound environmental understanding capabilities. To address this, we propose a large language model (LLM)-driven multimodal framework that fuses historical GPS data, RGB image, LiDAR data, and strategically designed task-specific textual prompts. By utilizing the powerful emergent reasoning and generalization capabilities of the LLM, our approach learns complex spatial dynamics to achieve superior environmental comprehension...

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