Considering a low power wide area random access system (like LoRaWAN) where the individual transmitters are mobile, we 1) propose a way of quantifying the performance of tracking of the mobile devices, and 2) design a distributed algorithm to achieve a target tracking performance. The insights gained are then used to provide an analysis of a family of target-achieving reinforcement-learning algorithms used in the literature to learn the optimal (Nash Equilibrium) random access probabilities. By mapping the payoff function in the equivalent game to the performance metric, we establish that, under some general conditions of inter-node parameter separability, the algorithm convergence is independent of the payoff function used. The mapping from the desired performance metric to the success probability in random access can be used in the algorithm to achieve a target success probability.

中文翻译：

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LoRaWAN 类系统中的跟踪性能和一类分布式学习算法的等价性

考虑到单个发射机是移动的低功率广域随机接入系统（如 LoRaWAN），我们 1) 提出了一种量化移动设备跟踪性能的方法，以及 2) 设计一种分布式算法来实现目标跟踪性能. 然后将获得的见解用于分析文献中使用的一系列目标实现强化学习算法，以学习最佳（纳什均衡）随机访问概率。通过将等效游戏中的收益函数映射到性能度量，我们确定，在节点间参数可分离性的一些一般条件下，算法收敛与所使用的收益函数无关。