Resumen
Low-voltage power line communication (LVPLC) medium access control protocols significantly affect home area networks performance. This study addresses poor network performance issues caused by asymmetric channels and noise interference by proposing the following: (i) an improved Q learning method for optimizing the improved artificial LVPLC cobweb, wherein the learning-based hybrid time-division-multiple-access (TDMA)/carrier-sense-multiple-access (CSMA) protocol, the asymmetrical network system, is modeled as a discrete Markov decision process, associates the station information using online trial-and-error learning, builds a routing table, periodically studies stations to choose a better forward path, and optimizes the shortest backbone cluster tree between the central coordinator and the stations, guaranteeing network stability; and (ii) an improved adaptive p-persistent CSMA game optimization method is proposed to optimize the improved artificial cobweb saturation throughput and access delay performance. The current state of the game (e.g., the number of competitive stations) for each station is estimated by the hidden Markov model. The station changes its equilibrium strategy based on the estimated number of active stations, which reduces the collision probability of data packets, optimizes channel transmission status, and increases performance by dynamically adjusting the probability p. An optimal saturation performance is achieved by finitely repeating the game. We present numerical results to validate our proposed approach.