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The traditional MAC protocol in ad hoc networks includes carrier sensing to judge the busy/idle state of a channel. However, in airborne tactical networks, the results of carrier sensing are usually inaccurate due to the wide distribution of nodes, large communication distance, and high-dynamic network topology, and the carrier sensing causes a large transmission delay and low channel utilization ratio. In this paper, we propose a novel channel busy recognition mechanism combined with auto regressive (AR) forecasting, namely, L-steps-revise AR (LS-AR). LS-AR predicts the number of bursts in the current time frame based on the number of bursts received in previous time frames, and the predicted number of bursts determines the channel busy/idle state. The difference between the predicted value and the true value serves as the means for correcting the next prediction. This mechanism avoids the delay caused by carrier sensing, improves the channel utilization ratio and provides a more accurate judgment to enable different priority packets to access a channel. The simulation results show that the algorithm can accurately predict the channel load and meet the requirements of the MAC protocol in airborne tactical networks.

A Novel Statistical Multi-Channel Busy Recognition Mechanism in the MAC Layer for Airborne Tactical Networks