Deep Q-Learning Based Optimization of VLC Systems With Dynamic Time-Division Multiplexing

Deep Q-Learning Based Optimization of VLC Systems With Dynamic Time-Division Multiplexing

The traditional method to solve nondeterministic-polynomial-time (NP)-hard optimization problems is to apply meta-heuristic algorithms. In contrast, Deep Q Learning (DQL) uses memory of experience and deep neural network (DNN) to choose steps and progress towards solving the problem. The dynamic tim...

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Bibliographic Details
Main Authors: Umair F. Siddiqi, Sadiq M. Sait, Murat Uysal
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Deep Q learning
deep reinforcement learning
dynamic time division multiple access
visible light communications
optimization
non-deterministic algorithms
Online Access:https://ieeexplore.ieee.org/document/9130159/

Internet

https://ieeexplore.ieee.org/document/9130159/

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