Mixture of Experts for Network Optimization: A Large Language Model-enabled Approach

TL;DR

This paper introduces a novel Large Language Model-enabled Mixture of Experts framework to optimize wireless network tasks efficiently, reducing the need for multiple specialized DRL models and lowering resource consumption.

Contribution

It presents a new LLM-augmented MoE approach that effectively manages expert selection and decision-making in network optimization, enhancing flexibility and efficiency.

Findings

Reduces training needs for new DRL models

Demonstrates effectiveness in maze navigation and network utility tasks

Lowers energy consumption and implementation costs

Abstract

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.