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Task Offloading and Resource Scheduling in Full-Duplex Cell-Free Massive MIMO-Enabled Edge Computing Networks
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DOI：10.1109/TMC.2025.3640285.png)
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Abstract
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Edge computing brings computational resources to network edge, enabling mobile devices (MDs) to offload computing-intensive tasks to nearby edge servers. This significantly reduces the energy consumption of MDs and supports latency-sensitive applications. Meanwhile, the advancement of full-duplex (FD) cell-free massive multiple-input multiple-output (MIMO) technology provides a promising opportunity to enhance end-edge communication efficiency, particularly in scenarios with coexisting uplink (UL) and downlink (DL) users. In this paper, we investigate the joint task offloading and resource scheduling problem in FD cell-free massive MIMO-enabled edge computing networks. The problem is formulated as a two-stage optimization framework. In the first stage, we develop a hybrid simulated annealing–particle swarm optimization (SA-PSO) algorithm, which incorporates the Metropolis criterion to enhance global search capability, aiming to maximize spectral efficiency. In the second stage, we propose a diffusion-augmented prioritized deep deterministic policy gradient (DAP-DDPG) algorithm. This algorithm integrates prioritized experience replay with diffusion models to minimize the total energy consumption of MDs while satisfying stringent latency constraints. Simulation results demonstrate that, compared with benchmark schemes, the proposed SA-PSO algorithm achieves a 13.4% to 146% improvement in spectral efficiency, while the DAP-DDPG algorithm reduces the energy consumption of MDs by 12.5% to 33.1%.
Keywords:
Cell-free massive MIMO
edge computing
full-duplex communication
deep reinforcement learning
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