A Novel Multi-channel Statistical Predicted Strategy for Airborne Networks

Abstract

In the contention-based MAC protocols of airborne networks, the busy degree of channels can be used as the threshold of different priority services. By limiting access of low priority traffic, we can guarantee the QoS demands of high priority services and solve the problem that the process of packets accessed channels blindly will worsen the protocol performance, so, a multi-channel statistical prediction strategy is proposed in this paper. According to the high reliability demand of the highest priority traffic and the ratio of different types of services, we establish the channel occupancy model and get the channel load interval of different services. Then we use the statistical prediction model to predict the busy degree of channels and channel loads for the next time. Simulation results show that the strategy has a prediction rate of over 95% for channel loads, can provide differential service for different priorities, and it also can significantly improve the performance of contention-based protocol under heavy loads.