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去中心化时分多址算法,用于可见光通信物联网网络。该算法基于强化学习中的Q-learning算法。本文考虑了无协调节点发送同步帧、分配传输时隙给其他节点的去中心化场景。所提算法采用去中心化方式实现同步,每个节点通过Q-learning算法自主学习最优传输时隙以避免数据碰撞。该算法已在实验室自主设计实现的可见光通信硬件系统上进行了验证。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,该算法具有快速收敛特性,可为网络提供无碰撞的去中心化时分多址通信。将其与作为去中心化可见光通信物联网潜在备选方案具有冲突避免的载波侦听多路访问算法进行对比,结果显示所提算法相比冲突避免的载波侦听多路访问算法可提升高达61%的有效吞吐量,并降低多达49%的平均时延。