The invention discloses a self-organizing network medium
access control method based on
reinforcement learning, is applied to the field of
wireless networks, and aims at solving the problem that in the prior art, distributed network node multi-time-slot selection in a dynamic TDMA medium
access control method is generally not considered. According to the invention, a time slot
interleaving schemeis adopted in the frame structure design, so that the requirement of the
media access control method on the packet
response delay performance of hardware equipment is looser; meanwhile, a multi-time-slot reservation mechanism is adopted, so that the network node only needs to send a control frame once and reserve information time slots of a plurality of subsequent periods in each period, the interaction process is simple and easy to implement, and the control overhead is low; and the selection probability of each section of time slot is adaptively adjusted based on a multi-time slot selectionalgorithm of
reinforcement learning, and a more optimized time slot
selection strategy is generated, so that the time slot competition conflict is reduced, the time slot allocation efficiency is improved, and the competition success rate, the
transmission bandwidth, the
transmission time delay, the
packet loss rate and other performances of the medium
access control method are further optimized.