Multi-agent Conditional Diffusion Model With Mean Field Communication As Wireless Resource Allocation Planner

Abstract

In wireless communication systems, efficient and adaptive resource allocation plays a crucial role in enhancing overall Quality of Service (QoS). Compared to the conventional Model-Free Reinforcement Learning (MFRL) scheme, Model-Based RL (MBRL) first learns a generative world model for subsequent planning. The reuse of historical experience in MBRL promises more stable training behavior, yet its deployment in large-scale wireless networks remains challenging due to high-dimensional stochastic dynamics, strong inter-agent cooperation, and communication constraints. To overcome these challenges, we propose the Multi-Agent Conditional Diffusion Model Planner (MA-CDMP) for decentralized communication resource management. Built upon the Distributed Training with Decentralized Execution (DTDE) paradigm, MA-CDMP models each communication node as an autonomous agent and employs Diffusion Models (DMs) to capture and predict environment dynamics. Meanwhile, an inverse dynamics model guides acti

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keymeng2025multi

Related papers

• Resource Management In Wireless Networks Via Multi-agent Deep Reinforcement Learning (2020)16.43
• Madiff: Offline Multi-agent Learning With Diffusion Models (2023)2.26
• Dual-mind World Models: A General Framework For Learning In Dynamic Wireless Networks (2025)0.00
• Deep Reinforcement Learning For Distributed And Uncoordinated Cognitive Radios Resource Allocation (2022)0.00
• Small-scale-fading-aware Resource Allocation In Wireless Federated Learning (2025)0.00
• Diffusion Models For Offline Multi-agent Reinforcement Learning With Safety Constraints (2024)0.00
• Deep Reinforcement Learning For Distributed Uncoordinated Cognitive Radios Resource Allocation (2019)0.00
• Multi-agent Deep Reinforcement Learning (MADRL) Meets Multi-user MIMO Systems (2021)7.50