Dynamic Channel Allocation Scheme Based on Traffic Prediction in Dense Wireless Networks

If the future traffic of small base stations (SBSs) can be foreseen, we can systematically adjust the system resources to meet the quality of service (QoS) of users and realize the effective assignment of network resources. To this end, a dynamic channel allocation schemes based on traffic prediction is proposed. First, the machine learning is adopted to extract temporal and spatial features of the service traffic or load in a certain region, and then the prediction results and graph theory are both used to realize the division of a given frequency band and bandwidth allocation, in order to achieve the purpose of coordinating the interference between SBSs and improve the system throughput; Secondly, oriented to the fluctuation of service traffic in the region, a dynamic channel allocation method based on user satisfaction is proposed to fulfill the dynamic adjustment of the total system bandwidth and channel allocation, so as to utilize the frequency band resources more effectively. Simulations show that our proposed method improves the number of bandwidths allocated per SBS by a factor of 4.932 and 1.225 on average compared to OCA and CA-CM, and it realizes the data rate requirement of most users with less bandwidth.