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Resource Allocation in Cell-Free MEC Networks: A Hierarchical MADRL-Based Algorithm
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DOI：10.1109/TCCN.2025.3645433.png)
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Abstract
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This paper investigates a cell-free massive multiple-input multiple-output enabled multi-access edge computing (termed CF-MEC) system, where multiple users are served by multiple central processing units (CPUs) and their connected access points (APs), both of which are equipped with computation resources. For this system, a dynamic user-centric task offloading scheme is designed to provide seamless and efficient computation services for users. Based on this scheme, the joint optimization of user-centric AP clustering, edge server selection, communication and computation resources is formulated as a long-term problem to minimize the average energy consumption. The formulated problem is complicated non-convex due to the highly coupled time-varying discrete and continuous variables, resulting in high complexity and non-real-time to obtain the optimal solution. To tackle this challenging problem, we propose a multi-layer hierarchical multi-agent deep reinforcement learning (ML-HMADRL) based resource allocation algorithm. Specifically, the proposed algorithm incorporates a hierarchical structure with high, middle, and low-level agents that iteratively train the actor-critic networks to obtain discrete and continuous variables of the formulated problem. To further enhance the training effectiveness by leveraging the CF-MEC system, we design distinct actor-critic networks for the agents at different levels to facilitate centralized training and distributed execution. Simulation results validate the training stability of the proposed algorithm at each level, and demonstrate the superiority of the proposed algorithm over benchmark schemes in terms of the average energy consumption, providing a stable distributed framework for practical implementation in dynamic environments.
Keywords:
Multi-access edge computing
user association
resource allocation
deep reinforcement learning
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