Abstract
Developing high data rate systems to meet the requirements of fifth generation mobile systems has become crucial. Hybrid radio frequency/visible light communication (RF/VLC) has appeared as a promising mechanism for achieving this objective. In hybrid RF/VLC, data rate maximization is subject to constraints on bandwidth, power and the user association. The joint optimization problem of bandwidth, power and user association to maximize the data rate is non-concave and obtaining an optimal solution is difficult with conventional optimization algorithms. The existing solutions are based on a presumption of at least one optimization variable. In this article, this issue has been overcome by solving the joint optimization problem in hybrid RF/VLC with a deep Q-network (DQN) learning based algorithm, which has been recognized as an efficient learning based mechanism for optimization. Our system model considers one RF and multiple VLC access points (APs). The idle APs are also incorporated in the system model. The application of DQN learning based algorithm is carried out by finding an optimal policy with the help of an action-value function. As the data sets for the considered system are large, a multi-layered network is used for approximating the action-value function estimator. Finally, a transfer learning based algorithm has been proposed for maximizing the total data rate of the system for the case of a newly entering user equipment (UE) that uses the information of the environment before the arrival of the new UE. Through simulations, it is found that our proposed algorithms can lead to an improvement of more than 10% and 54% in the achievable sum-rate and number of iterations for convergence respectively as compared to that obtained with existing conventional optimization algorithms.
Highlights
W ITH the growing population of mobile internet users, the requirement for data rate has seen an exponential growth in the recent years
To overcome the limitations of conventional optimization algorithms in solving such a problem, a centralized deep Q-network (DQN) based learning algorithm has been designed, which is based on learning from the hybrid radio frequency/visible light communication (RF/Visible Light Communication (VLC)) environment
The state vector for DQN is formulated with the constraint terms in the optimization problem, while the action vector for DQN is based on the choice of bandwidth, power, and association parameter
Summary
W ITH the growing population of mobile internet users, the requirement for data rate has seen an exponential growth in the recent years. A common issue faced in these research works when the joint optimization of the downlink bandwidth, transmission power of the APs, and the association parameter are involved, is the non-concavity of the downlink resource allocation problem. A deep Q-network (DQN) learning based algorithm is developed for jointly optimizing the downlink bandwidth allocation, power allocation for APs, and association parameter, which maximizes the achievable data rate in a downlink hybrid RF/VLC system. Our developed algorithm allows the CU to adaptively allocate the downlink bandwidth, power and the association parameter to the APs to maximize the achievable sum-rate of the system It is not dependent on interaction among the UEs. A DQN based learning. DQN learning can efficiently maximize the Q-value by approximating the action-value function from the current state It has still remained unexplored for finding optimal resource allocation in hybrid RF/VLC systems. When associated with a VLC AP, a UE receives data with LOS and reflected light ray components
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