基于 ReEvo 设计的模因算法提升流体天线系统物理层安全性能
Beyond LLM-GA: Secure Fluid Antenna Systems with ReEvo-Designed Memetic Algorithm
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研究者提出一种基于反思进化（ReEvo）的模因算法，用于解决流体天线系统（FAS）防窃听的 secure FAS port selection 问题。与仅用 LLM 设计交叉或变异算子的 LLM-GA 不同，该算法离线用 LLM 进化出专门的交叉、变异和局部搜索算子并嵌入模因搜索框架，执行时无需在线 LLM 查询。
正文 · 原文
Abstract:Fluid antenna systems (FASs) offer significant spatial flexibility, yet securing them against eavesdropping is critical for practical FAS deployment in military, satellite, and internet-of-things networks. Although large language model (LLM)-assisted genetic algorithms (LLM-GAs) can address this secure FAS port selection problem, whether further algorithmic improvement is possible warrants deeper investigation. To this end, we propose a memetic algorithm based on reflective evolution (ReEvo). Unlike the state-of-the-art LLM-GAs, which design only crossover or mutation operators with an LLM, our algorithm leverages an LLM to evolve dedicated crossover, mutation, and local-search operators offline. These operators are then embedded into a memetic search framework, thereby obviating any online LLM queries during execution. Simulation results at equal generation counts demonstrate that our proposed algorithm achieves a higher secure sum-rate than the conventional GA and the state-of-the-art LLM-GAs.
| Comments: | Accepted by WCSP 2026 |
| Subjects: | Information Theory (cs.IT); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.10235 [cs.IT] |
| (or arXiv:2610.10235v2 [cs.IT] for this version) | |
| https://doi.org/10.48550/arXiv.2610.10235 arXiv-issued DOI via DataCite |
Submission history
From: Tong Zhang [view email]
[v1]
Wed, 7 Oct 2026 15:20:52 UTC (250 KB)
[v2]
Mon, 26 Oct 2026 13:18:46 UTC (250 KB)
来源：arXiv cs.AI · arxiv.org