Actor-Critic Deep Reinforcement Learning for Dynamic Multichannel Access
This work addresses channel access optimization in wireless networks, presenting an incremental improvement over existing methods.
The paper tackles the dynamic multichannel access problem by proposing an actor-critic deep reinforcement learning framework for sensing policies, achieving competitive performance in scenarios with varying channel switching patterns and time-varying environments compared to a DQN-based approach.
We consider the dynamic multichannel access problem, which can be formulated as a partially observable Markov decision process (POMDP). We first propose a model-free actor-critic deep reinforcement learning based framework to explore the sensing policy. To evaluate the performance of the proposed sensing policy and the framework's tolerance against uncertainty, we test the framework in scenarios with different channel switching patterns and consider different switching probabilities. Then, we consider a time-varying environment to identify the adaptive ability of the proposed framework. Additionally, we provide comparisons with the Deep-Q network (DQN) based framework proposed in [1], in terms of both average reward and the time efficiency.