Optimizing and Updating LoRa Communication Parameters: A Machine Learning Approach

摘要

LoRa is an extremely flexible low-power wide-area technology that enables each IoT node to individually adjust its transmission parameters. Consequently, the average per-node throughput of LoRa-based networks has been mathematically formulated and the optimal network-level configuration derived. For end nodes to update their transmission parameters, this centrally computed global configuration must then be disseminated by LoRa gateways. Unfortunately, the regional limitations imposed on the usage of ISM bands-especially those related to the maximum utilization of the band-pose a potential handicap to this parameter dissemination. To solve this problem, a set of tools from the machine learning field have been used. Precisely, the updating process has been formulated as a reinforcement learning (RL) problem whose solution prescribes optimal disseminating policies. The use of these policies together with the optimal network configuration has been extensively analyzed and compared to other well-established alternatives. Results show an increase of up to 147% in the accumulated per-node throughput when our RL-based approach is employed.