Multi-Channel Dynamic Spectrum Access Based on Multi-Agent Proximal Policy Optimization
To enhance communication efficiency and ensure user fairness in multi-user multi-channel communication scenarios,based on multi-agent proximal policy optimization (MAPPO) for the application of dynamic spectrum access (DSA) technology,this paper proposes the MAPPO-DSA algorithm. The algorithm addresses the issue of spectrum waste in single-channel access when multiple channels are simultaneously idle by using multi-channel access as a solution. However,multi-channel access leads to an exponential increase in the state and action spaces,resulting in high computational costs and learning difficulties. To tackle this,the paper introduces the MAPPO deep reinforcement learning (DRL) algorithm to efficiently learn and optimize access strategies in complex environments. The design of MAPPO incorporates reinforcement learning elements such as observation and reward,as well as shared network parameters to ensure user fairness. Experimen-tal results in different scenarios demonstrate that the proposed MAPPO-DSA algorithm can learn near-optimal access strate-gies,and approach the theoretical throughput limit in some scenarios,outperforming the existing algorithms significantly and effectively ensuring user fairness.
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