Research On Key Technologies Of Traffic Prediction And Delayed Offloading Incentive Mechanism In Heterogeneous Cellular Networks

With the development of 5G technology and the increase of smart terminal equipment,users' demand for wireless traffic is also increasing.How to effectively alleviate network congestion that may be caused by the surge in traffic is an urgent problem in the current communications field.Based on this,the current heterogeneous cellular network technology effectively improves network performance by increasing the density of base stations.In a heterogeneous cellular network,exploring the future trend of wireless traffic will help pre-allocation of resources,and encouraging users to delay offloading traffic can alleviate network congestion.However,the distribution of wireless traffic is temporal and spatial,and the delayed offloading environment is dynamic and information asymmetry.Therefore,exploring the future traffic trend in a certain area and motivating users to actively participate in delayed offloading becomes a challenging task.Based on this,this paper investigates wireless traffic prediction techniques and dynamic incentive mechanisms for delayed offloading of traffic to improve network performance.The contributions of this paper are mainly in two aspects:1.For the spatio-temporal correlation of city-level wireless traffic,this paper proposes a deep learning-based wireless traffic prediction method.Based on the data pre-analysis,three components are used to model the recently,daily,and weekly characteristics of wireless traffic data,and each component effectively captures the spatial and temporal correlation of wireless traffic data using both spatial and temporal dimensional convolution,to predict the wireless traffic in a certain region of the city at a certain time in the future.In this paper,experiments are conducted on a publicly available dataset of wireless traffic in the city of Milan,and simulation results show that the method has significant advantages in prediction performance compared to several other classical methods.2.Aiming at the dynamics and information asymmetry of the heterogeneous cellular network traffic delay offloading environment,this paper proposes a dynamic incentive mechanism for traffic delay offloading based on contract theory.First,establish a network service provider and user model.Then,combined with incentive compatibility constraints and participation constraints,a dynamic incentive mechanism for traffic delay offloading is designed.Finally,it is optimized to obtain the maximum expected utility of the network service provider.Simulation results show that the dynamic incentive mechanism can effectively improve the efficiency of traffic delay offloading in heterogeneous cellular networks,thereby effectively alleviating network congestion and improving network performance.