The invention discloses a self-adaptive
channel access method for an ad hoc network of an unmanned aerial vehicle, relates to the technical field of
wireless communication, and realizes channel selection and dynamic adjustment of contention windows by combining a multi-channel multi-contention window mechanism with deep
reinforcement learning. Specifically, each unmanned aerial vehicle node initializes contention window values of a plurality of channels, and dynamically selects an optimal channel in a
data transmission stage according to a channel busy / idle rate and contention window popularity. In a data
retransmission stage, a contention window value is adjusted through a dual-depth Q network to minimize conflicts and optimize
network performance. The
state space comprises a self contention window, a local channel busy / idle rate, a neighbor average channel busy / idle rate and contention window popularity, and the award function comprehensively considers
throughput, fairness and the average channel busy / idle rate. Experimental results show that compared with a traditional protocol, the method has the advantages that key performance indexes such as
throughput, time
delay and fairness are remarkably improved, and the method is particularly suitable for high-dynamic communication scenes such as unmanned aerial vehicle cluster cooperation and
the Internet of Vehicles.