In this paper, a machine learning-based decentralized time division multiple access (TDMA) algorithm for visible light communication (VLC) Internet of Things (IoT) networks is proposed. The proposed algorithm is based on Q-learning, a reinforcement learning algorithm. This paper considers a decentralized condition in which there is no coordinator node for sending synchronization frames and assigning transmission time slots to other nodes. The proposed algorithm uses a decentralized manner for synchronization, and each node uses the Q-learning algorithm to find the optimal transmission time slot for sending data without collisions. The proposed algorithm is implemented on a VLC hardware system, which had been designed and implemented in our laboratory. Average reward, convergence time, goodput, average delay, and data packet size are evaluated parameters. The results show that the proposed algorithm converges quickly and provides collision-free decentralized TDMA for the network. The proposed algorithm is compared with carrier-sense multiple access with collision avoidance (CSMA/CA) algorithm as a potential selection for decentralized VLC IoT networks. The results show that the proposed algorithm provides up to 61% more goodput and up to 49% less average delay than CSMA/CA.

翻译：本文提出了一种基于机器学习的可见光通信物联网网络去中心化时分多址接入算法。该算法基于强化学习中的Q-learning算法，考虑无协调节点发送同步帧及分配传输时隙的去中心化场景。各节点采用去中心化方式实现同步，并通过Q-learning算法自主寻找最优传输时隙以避免数据碰撞。所提算法已在实验室自主研发的可见光通信硬件系统上实现，评估参数包括平均奖励、收敛时间、有效吞吐量、平均时延及数据包大小。实验结果表明，该算法能快速收敛，为网络提供无碰撞的去中心化时分多址接入。与适用于去中心化可见光通信物联网网络的载波侦听多路访问/冲突避免算法相比，所提算法有效吞吐量最高提升61%，平均时延降低49%。