MAC protocols are important to ensure the quality of service of UAV ad-hoc networks, which are becoming more and more popular. Facing the dynamically changing network transmission environment, the adaptive MAC protocol becomes an effective solution. In this paper, a MAC protocol based on deep Q-learning is designed. The system integrates a contention-based MAC protocol and a scheduling-based MAC protocol. Using the deep Q-learning approach, it switches between CSMA and TDMA according to the current state of the UAV (e.g. throughput rate, latency, etc.). Two different network scenarios were designed and simulated to evaluate the performance of the designed MAC protocols. The results show that the performance of the designed adaptive MAC protocol outperforms that of a single protocol in terms of performance such as latency and throughput.
MAC protocols are important to ensure the quality of service of UAV ad-hoc networks, which are becoming more and more popular. Facing the dynamically changing network transmission environment, the adaptive MAC protocol becomes an effective solution. In this paper, a MAC protocol based on deep Q-learning is designed. The system integrates a contention-based MAC protocol and a scheduling-based MAC protocol. Using the deep Q-learning approach, it switches between CSMA and TDMA according to the current state of the UAV (e.g. throughput rate, latency, etc.). Two different network scenarios were designed and simulated to evaluate the performance of the designed MAC protocols. The results show that the performance of the designed adaptive MAC protocol outperforms that of a single protocol in terms of performance such as latency and throughput.
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