面向网络优化的专家混合：一种基于大语言模型的方法
Mixture of Experts for Network Optimization: A Large Language Model-enabled Approach
AI总结：
针对网络系统中定制化DRL模型开发成本高、能耗大的问题，提出结合LLM的MoE框架，利用LLM替代传统门控网络进行专家选择与决策权衡，有效降低了模型部署成本并在网络优化任务中验证了其有效性。
摘要：
由于用户需求范围不断扩大，优化各种无线用户任务给网络系统带来了重大挑战。尽管深度强化学习（DRL）取得了进展，但为单个用户定制优化任务的需求使得开发和部署大量DRL模型变得复杂，导致计算资源和能耗大幅增加，并可能产生不一致的结果。为解决此问题，我们提出了一种利用专家混合（MoE）框架并结合大语言模型（LLMs）的新方法，以有效分析用户目标和约束，选择专门的DRL专家，并权衡参与专家的各项决策。具体而言，我们开发了一个门控网络来管理专家模型，使专家集合能够应对各种新任务。此外，我们创新性地用LLM替代传统门控网络，利用其高级推理能力来管理专家模型选择以进行联合决策。所提方法减少了对每个独特优化问题训练新DRL模型的需求，降低了能耗和AI模型部署成本。这种基于LLM的MoE方法通过通用迷宫导航任务和特定的网络服务提供商效用最大化任务得到了验证，证明了其在优化复杂网络系统方面的有效性与实用价值。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
