Optimizing various wireless user tasks poses a significant challenge for networking systems because of the expanding range of user requirements. Despite advancements in Deep Reinforcement Learning (DRL), the need for customized optimization tasks for individual users complicates developing and applying numerous DRL models, leading to substantial computation resource and energy consumption and can lead to inconsistent outcomes. To address this issue, we propose a novel approach utilizing a Mixture of Experts (MoE) framework, augmented with Large Language Models (LLMs), to analyze user objectives and constraints effectively, select specialized DRL experts, and weigh each decision from the participating experts. Specifically, we develop a gate network to oversee the expert models, allowing a collective of experts to tackle a wide array of new tasks. Furthermore, we innovatively substitute the traditional gate network with an LLM, leveraging its advanced reasoning capabilities to manage expert model selection for joint decisions. Our proposed method reduces the need to train new DRL models for each unique optimization problem, decreasing energy consumption and AI model implementation costs. The LLM-enabled MoE approach is validated through a general maze navigation task and a specific network service provider utility maximization task, demonstrating its effectiveness and practical applicability in optimizing complex networking systems.

翻译：优化各类无线用户任务对网络系统构成重大挑战，其根源在于用户需求的不断扩展。尽管深度强化学习取得了进展，但为单个用户定制优化任务的需求使得大量深度强化学习模型的开发和应用变得复杂，导致计算资源与能源消耗巨大，并可能引发不一致的结果。针对这一问题，本文提出了一种新颖方法，利用集成大语言模型的专家混合框架，有效分析用户目标与约束条件，选取专门化的深度强化学习专家，并对参与专家的每项决策进行加权。具体而言，我们开发了一个门控网络来监督专家模型，使专家集合能够应对广泛的新任务。此外，我们创新性地用大语言模型替代传统门控网络，借助其先进的推理能力来管理专家模型选择以实现联合决策。所提方法减少了对每个独特优化问题训练新深度强化学习模型的需求，从而降低了能耗与人工智能模型实施成本。通过通用迷宫导航任务与特定网络服务提供商效用最大化任务的验证，该大语言模型赋能的专家混合方法在优化复杂网络系统中的有效性与实际应用性得到了充分证明。