Ensemble consensus representation deep reinforcement learning for hybrid FSO/RF communication systems

Hybrid FSO/RF system requires an efficient FSO and RF link switching 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, 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 representation learning-based DRL called DQNEnsemble-FSO/RF. The proposed DQNEnsemble-FSO/RF DRL approach uses consensus learned features based on an ensemble of asynchronous threads to update the deployed policy. Experimental results corroborate that DQNEnsemble-FSO/RF's consensus-learned features demonstrate better performance than Actor/Critic-FSO/RF, DQN-FSO/RF, and MyOpic while keeping the switching cost low. The results provide interesting insights into the prediction of received signal strength indicator (RSSI) for FSO/RF link switching.