Multi-Channel Scheduling for Short-Range Wireless Communication Networks Using a Q-Learning Feedback Mechanism

The traditional channel scheduling methods in short-range wireless communication networks are often constrained by fixed rules, resulting in inefficient channel resource utilization and unstable data communication.To address these limitations, a novel multi-channel scheduling approach, based on a Q-learning feedback mechanism, was proposed.The architecture of short-range wireless communication networks was analyzed, focusing on the core network system and wireless access network structures.The network channel nodes were optimized by deploying Dijkstra's algorithm in conjunction with an undirected graph representation of the communication nodes within the network.Multi-channel state characteristic parameters were computed, and a channel state prediction model was constructed to forecast the state of the network channels.The Q-learning feedback mechanism was employed to implement multi-channel scheduling, leveraging the algorithm's reinforcement learning capabilities and framing the scheduling process as a Markov decision-making problem.Experimental results demonstrate that this method achieved a maximum average packet loss rate of 0.03 and a network throughput of up to 4.5 Mbps, indicating high channel resource utilization efficiency.Moreover, in low-traffic conditions, communication delay remained below 0.4 s, and in high-traffic scenarios, it varied between 0.26 and 0.4 s.These outcomes suggest that the proposed approach enables efficient and stable transmission of communication data, maintaining both low packet loss and high throughput.