Wireless Power Control Based on Large Language Models

Jiacheng Wang; Yucheng Sheng; Le Liang; Hao Ye; Shi Jin

cs.ITFeb 28, 2026

Wireless Power Control Based on Large Language Models

Authors: Jiacheng Wang, Yucheng Sheng, Le Liang, Hao Ye, Shi Jin

Organizations: School of Information Science and Engineering, Southeast University, Nanjing 210096, China · Department of Electrical and Computer Engineering, University of California, Santa Cruz, CA 95064, USA

Abstract

This paper investigates the power control problem in wireless networks by repurposing pre-trained large language models (LLMs) as relational reasoning backbones. In hyper-connected interference environments, traditional optimization methods face high computational cost, while standard message passing neural networks suffer from aggregation bottlenecks that can obscure critical high-interference structures. In response, we propose PC-LLM, a physics-informed framework that augments a pre-trained LLM with an interference-aware attention bias. The proposed bias tuning mechanism injects the physical channel gain matrix directly into the self-attention scores, enabling explicit fusion of wireless topology with pre-trained relational priors without retraining the backbone from scratch. Extensive experiments demonstrate that PC-LLM consistently outperforms both traditional optimization methods and state-of-the-art graph neural network baselines, while exhibiting exceptional zero-shot generalization to unseen environments. We further observe that topology-relevant relational reasoning is concentrated in shallow layers, whereas deeper layers encode task-irrelevant semantic noise. Motivated by this finding, we develop a lightweight adaptation strategy that reduces model depth by 50%, significantly lowering inference cost while preserving state-of-the-art spectral efficiency.

Figures & tables

Fig. 1: Graph representation of the wireless interference network. Communication links within transmitter-receiver pairs are modeled as nodes, while the mutual interference between pairs is represented by directed edges, constituting a fully connected interference graph $\mathcal{G}$ .

Fig. 2: Overall architecture of the proposed PC-LLM. The framework aligns physical channel features with the latent space of the model through an input projection layer, a bias-modulated Transformer backbone, and a power inference head.

Fig. 3: Performance comparison of the proposed PC-LLM framework against baseline algorithms across varying network densities (number of D2D pairs

K

) and interference environments (distance ranges

[d_{\min},d_{\max}]

). The results are organized by optimization objectives: (a)–(c) sum-rate maximization, (d)–(f) proportional fairness, and (g)–(i) harmonic maximization. The performance is normalized with respect to the WMMSE-Best algorithm for the max sum rate and proportional fairness tasks, whereas the harmonic mean rate is normalized relative to the optimal PCGNN-Large baseline. For learning-based methods, only the best performance achieved under their respective optimal training configurations is reported.

Panel of Figure 4 — Fig. 4: Performance comparison of the proposed PC-LLM and baseline algorithms under multi-dataset (darker bars) and single-dataset (lighter bars) training configurations. The performance is normalized with respect to the WMMSE-Best algorithm for the max sum rate and proportional fairness tasks, whereas the harmonic mean rate is normalized relative to the multi-dataset trained PCGNN-Large baseline. The bar heights represent the average performance across 15 distinct scenarios with varying user densities and channel distributions. The error bars denote the standard deviation, quantifying the performance variance and validating the universal robustness of each method against extreme macro-level topological heterogeneity.

Optimization Objective	Algorithm	Number of D2D Pairs $K$
Optimization Objective	Algorithm	20	35	50	65	80
Max Sum Rate	PC-LLM (zero-shot)	4.7741	3.8514	3.3893	3.0993	2.9070
	PC-LLM (full-shot)	4.7731	3.8183	3.3760	3.0914	2.9011
	WMMSE-Best	4.7452	3.8152	3.3561	3.0701	2.8811
Prop. Fairness	PC-LLM (zero-shot)	2.1553	1.3818	1.0601	0.8838	0.7776
	PC-LLM (full-shot)	2.1536	1.3511	1.0482	0.8830	0.7702

TABLE I: Generalization Performance Across Varying Network Densities with

[d_{\min},d_{\max}]=[1,100]

Fig. 5: Performance comparison with varying numbers of LLM layers

L

under the setup of

K=50

and

[d_{\min},d_{\max}]=[2,65]

m. The performance is normalized with respect to the WMMSE-Best algorithm for the max sum rate and proportional fairness tasks, whereas the harmonic mean rate is normalized relative to PCGNN-Large.

Fig. 6: Ablation results of the proposed PC-LLM framework with varying numbers of D2D pairs

K

under the

[d_{\min},d_{\max}]=[2,65]

m scenario. The performance is normalized with respect to the WMMSE-Best algorithm.

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

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

Wireless Resource Allocation Large Language Model-Guided Optimization Natural Language to Optimization Modeling

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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.

Abstract

Wireless Resource Allocation

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