The invention is applicable to the technical field of
wireless networks, and provides a large-range
wireless network dynamic deployment
system and a resource optimization method, and the
system comprises a central
control unit, a plurality of intelligent access points, a
user density prediction module, a staged ad hoc network protocol module and a
reinforcement learning resource scheduling module. Through a staged ad hoc network protocol, a
wireless Mesh backhaul topology is quickly constructed and access parameter initialization is completed under the conditions that
deployment time is limited and backhaul link resources are limited; a
user density prediction module is used for predicting future
user density levels of areas covered by all access points, and prediction results, channel utilization rates, return loads and other indexes jointly form a
state space of multi-agent
reinforcement learning; through an improved MADDPG model, the working channel, the transmitting power, the
beam direction and the
beacon frame sending interval of each access point are jointly optimized. According to the method, the total
throughput of the whole network can be improved, and the congestion occurrence rate of the hotspot area is reduced.