SPMA protocol parameter optimization method and system based on enhanced learning and medium

Abstract

The invention provides an SPMA protocol parameter optimization method and system based on enhanced learning and a medium, and the method comprises the steps: a parameter selection and division step: selecting a parameter set of an SPMA protocol, dividing each parameter in the parameter set into different current parameter states according to a preset granularity, and obtaining a current parameterstate set; and a time delay and success rate obtaining step: according to the obtained current parameter state set and the preset scene, bringing the obtained current parameter state set into the preset scene, and obtaining the time delay and success rate of each priority service of the SPMA protocol. According to the invention, the SPMA protocol parameter optimization problem under different application scenarios is combined with the reinforcement learning algorithm; compared with the original parameter selection method of the SPMA communication system, the parameter calculation process is greatly simplified, the required performance index can be more easily achieved, the relevant setting of the SPMA protocol can be more efficiently completed, and the method has a wide application prospect.

Description

technical field [0001] The present invention relates to the technical field of communication protocols, in particular to a method, system and medium for optimizing SPMA protocol parameters based on reinforcement learning. Background technique [0002] The SPMA (Statistic Priority Multiple Access) protocol is mainly aimed at scenarios with high-priority time-sensitive services. In order to cope with high real-time business requirements of different priorities, such as TTNT's collaborative targeting information transmission, SPMA is adopted as the access protocol. The multiple access protocol based on priority probability statistics consists of several priority queues, priority competition back-off windows, priority thresholds, channel occupancy statistics, transceiver antennas and corresponding distributed control algorithms. Different priority services correspond to different MAC layer priority queues, and the channel occupancy statistics are obtained through the interactio...

AI Technical Summary

Problems solved by technology

Similarly, in the SPMA communication system, there are multiple sets of mutually restrictive parameters, which have varying degrees of influence on the performance indicators of the system. This relationship cannot be specifically represented by mathematical expressions

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