Deep Q-Network Based Power Allocation Meets Reservoir Computing in Distributed Dynamic Spectrum Access Networks

Dynamic spectrum access (DSA) is regarded as one of the key enabling technologies for future communication networks. In this paper, we introduce a power allocation strategy for distributed DSA networks using a powerful machine learning tool, namely deep reinforcement learning. The introduced power allocation strategy enables DSA users to conduct power allocation in a distributed fashion without relying on channel state information and cooperations among DSA users. Furthermore, to capture the temporal correlation of the underlying DSA network environments, the reservoir computing, a special class of recurrent neural network, is employed to realize the introduced deep reinforcement learning scheme. The combination of reservoir computing and deep reinforcement learning significantly improves the efficiency of the introduced resource allocation scheme. Simulation evaluations are conducted to demonstrate the effectiveness of the introduced power allocation strategy.