Dynamic Spectrum Sharing Based on Federated Learning and Multi-Agent Actor-Critic Reinforcement Learning

In order to improve spectrum efficiency in emergency communications, a dynamic spectrum sharing (DSS) scheme based on federated learning (FL) and deep reinforcement learning (DRL) is proposed. The operation model follows the paradigm of cognitive radio networks (CRNs), in which multiple secondary users (SUs) with different bandwidth requirements, spectrum sensing and access capabilities randomly access idle frequency bands that primary users (PUs) do not occupy. Different users in emergency communications are considered as SUs or PUs according to their communication priorities. A maximum entropy based multi-agent actor-critic (ME-MAAC) algorithm is used to realize an optimal spectrum sharing strategy by updating varying rewards to SUs. During the learning process, the FL algorithm is used to assign appropriate weights to SUs. Simulation results show that the performance of proposed scheme is better in terms of reward value, access rate, and convergence speed.