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

arXiv: https://arxiv.org/abs/2108.02551 · HTML: https://mlsanity.com/paper/2108.02551/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 48 references that are in the map.
- First posted on arXiv 5 Aug 2021.

## Builds on (most cited first, 12 of 12)

- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [Applications of Deep Reinforcement Learning in Communications and Networking: A Survey](https://mlsanity.com/paper/1810.07862/) (2018)
- [Deep Reinforcement Learning for Multi-objective Optimization](https://mlsanity.com/paper/1906.02386/) (2019)
- [Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning](https://mlsanity.com/paper/1804.00079/) (2018)
- [Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU](https://mlsanity.com/paper/1611.06256/) (2016)
- [Deep Reinforcement Learning for Dynamic Multichannel Access in Wireless Networks](https://mlsanity.com/paper/1802.06958/) (2018)
- [Artificial Intelligence-Enabled Intelligent 6G Networks](https://mlsanity.com/paper/1912.05744/) (2019)
- [Policy Gradient Methods for Reinforcement Learning with Function Approximation and Action-Dependent Baselines](https://mlsanity.com/paper/1706.06643/) (2017)
- [Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges](https://mlsanity.com/paper/1907.08965/) (2019)
- [Deep Reinforcement Learning Based Mode Selection and Resource Management for Green Fog Radio Access Networks](https://mlsanity.com/paper/1809.05629/) (2018)
- [Revisiting the Softmax Bellman Operator: New Benefits and New Perspective](https://mlsanity.com/paper/1812.00456/) (2018)

## Built on by (most cited first, 1 of 1)

- [Ensemble Reinforcement Learning: A Survey](https://mlsanity.com/paper/2303.02618/) (2023)

## Abstract (arXiv)

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.