Ensemble Consensus-based Representation Deep Reinforcement Learning for Hybrid FSO/RF Communication Systems

TL;DR

This paper introduces a novel ensemble consensus-based deep reinforcement learning method for efficient link switching in hybrid FSO/RF communication systems, improving performance and reducing switching costs under atmospheric disturbances.

Contribution

It proposes a new DQNEnsemble-FSO/RF approach that leverages ensemble consensus learning to enhance link switching in hybrid FSO/RF systems, addressing high switching costs and environmental challenges.

Findings

DQNEnsemble-FSO/RF outperforms existing methods in link switching performance.

The proposed method significantly reduces switching costs.

Experimental results validate the effectiveness of ensemble consensus learning in dynamic environments.

Abstract

Hybrid FSO/RF system requires an efficient FSO and RF link switching mechanism to improve the system capacity by realizing the complementary benefits of both the links. The dynamics of network conditions, such as fog, dust, and sand storms compound the link switching problem and control complexity. To address this problem, we initiate the study of deep reinforcement learning (DRL) for link switching of hybrid FSO/RF systems. Specifically, in this work, we focus on actor-critic called Actor/Critic-FSO/RF and Deep-Q network (DQN) called DQN-FSO/RF for FSO/RF link switching under atmospheric turbulences. To formulate the problem, we define the state, action, and reward function of a hybrid FSO/RF system. DQN-FSO/RF frequently updates the deployed policy that interacts with the environment in a hybrid FSO/RF system, resulting in high switching costs. To overcome this, we lift this problem to ensemble consensus-based representation learning for deep reinforcement called DQNEnsemble-FSO/RF. The proposed novel DQNEnsemble-FSO/RF DRL approach uses consensus learned features representations based on an ensemble of asynchronous threads to update the deployed policy. Experimental results corroborate that the proposed DQNEnsemble-FSO/RF's consensus-learned features switching achieves better performance than Actor/Critic-FSO/RF, DQN-FSO/RF, and MyOpic for FSO/RF link switching while keeping the switching cost significantly low.