Saved in:
| Main Author: | |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.21316 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911617133838336 |
|---|---|
| author | Wadayama, Tadashi |
| author_facet | Wadayama, Tadashi |
| contents | Large language models (LLMs) are increasingly being explored as high-level decision modules in closed-loop systems, but their stochastic nature makes safe integration challenging. In this paper, we propose LLM-Steered Power Allocation, a dual-process architecture for parallel QPSK channels inspired by Kahneman's System 1/System 2 framework. A fast numerical optimizer (System 1) continuously performs projected gradient ascent on a weighted mutual-information objective, while an LLM navigator (System 2) periodically interprets natural-language policies and updates only the channel weights and the operational power budget. The LLM never manipulates the power-allocation variables directly, and constraint satisfaction is enforced structurally by the optimizer. To mitigate LLM unreliability, we further incorporate multi-layer guardrails including normalization, exponential moving-average smoothing, and fallback mechanisms. Numerical experiments on an 8-channel system show that, with a fixed optimization core and unchanged system prompt, different natural-language policies induce qualitatively different operating points, including throughput-oriented allocation, channel prioritization, power-aware operation, and channel shutdown. In addition, under an abrupt channel-gain reversal, the proposed system autonomously reconfigures its steering signals and reduces the final mutual-information spread by 60% compared with the optimizer alone. These results suggest that LLMs can serve as policy interpreters for safe, flexible reconfiguration of communication-system optimizers without controller reimplementation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_21316 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | LLM-Steered Power Allocation for Parallel QPSK-AWGN Channels Wadayama, Tadashi Information Theory Large language models (LLMs) are increasingly being explored as high-level decision modules in closed-loop systems, but their stochastic nature makes safe integration challenging. In this paper, we propose LLM-Steered Power Allocation, a dual-process architecture for parallel QPSK channels inspired by Kahneman's System 1/System 2 framework. A fast numerical optimizer (System 1) continuously performs projected gradient ascent on a weighted mutual-information objective, while an LLM navigator (System 2) periodically interprets natural-language policies and updates only the channel weights and the operational power budget. The LLM never manipulates the power-allocation variables directly, and constraint satisfaction is enforced structurally by the optimizer. To mitigate LLM unreliability, we further incorporate multi-layer guardrails including normalization, exponential moving-average smoothing, and fallback mechanisms. Numerical experiments on an 8-channel system show that, with a fixed optimization core and unchanged system prompt, different natural-language policies induce qualitatively different operating points, including throughput-oriented allocation, channel prioritization, power-aware operation, and channel shutdown. In addition, under an abrupt channel-gain reversal, the proposed system autonomously reconfigures its steering signals and reduces the final mutual-information spread by 60% compared with the optimizer alone. These results suggest that LLMs can serve as policy interpreters for safe, flexible reconfiguration of communication-system optimizers without controller reimplementation. |
| title | LLM-Steered Power Allocation for Parallel QPSK-AWGN Channels |
| topic | Information Theory |
| url | https://arxiv.org/abs/2604.21316 |