Sniffer Channel Selection Based on Value Decomposition Networks in CRNs

In Cognitive Radio Networks (CRNs), network fault analysis, traffic tracing, and resource optimization are challenging tasks. With the increasing number of wireless applications and the conflict with limited wireless spectrum resources, the Sniffers Channel Assignment problem in CRNs has become particularly important. To address this issue, we propose a Value Decomposition Networks-based channel selection (CSVDN) algorithm. During centralized training, the Monitoring Quality Network (MQN) is trained based on observed data, using global information to calculate the Quality of Monitoring (QoM), which is then used as a reward to guide sniffers in selecting the optimal channels. During decentralized execution, sniffers share model parameters and independently run the MQN, sequentially selecting the optimal channels. This process ensures that sniffers collectively maximize network coverage while maintaining distributed control, thereby improving efficiency and scalability in dynamic environments. The results from NS-3 simulations show that CSVDN provides a distributed and implementable channel selection solution with high scalability and practicality, making it particularly suitable for large-scale CRNs.