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

arXiv: https://arxiv.org/abs/2402.09756 · HTML: https://mlsanity.com/paper/2402.09756/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2024.

## Built on by (most cited first, 7 of 7)

- [A Systematic Survey on Large Language Models for Algorithm Design](https://mlsanity.com/paper/2410.14716/) (2024)
- [Automated Consistency Analysis of LLMs](https://mlsanity.com/paper/2502.07036/) (2025)
- [Video-VoT-R1: An efficient video inference model integrating image packing and AoE architecture](https://mlsanity.com/paper/2503.15807/) (2025)
- [Just aware enough: Evaluating awareness across artificial systems](https://mlsanity.com/paper/2601.14901/) (2026)
- [Agentic AI-Based Joint Computing and Networking via Mixture of Experts and Large Language Models](https://mlsanity.com/paper/2605.02911/) (2026)
- [OmniPlan: An Adaptive Framework for Timely and Near-Optimal Network Planning Optimization](https://mlsanity.com/paper/2606.18105/) (2026)
- [Large Language Model for Operations Research Formulation Selection in Multi-Warehouse Inventory Allocation](https://mlsanity.com/paper/2607.25956/) (2026)

## Abstract (arXiv)

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.