Research On Joint Optimization Strategy Of Base Station Sleeping And Power Control In Heterogeneous Cellular Networks

With the gradual promotion of the 5th generation mobile communication technology,the massive increase of mobile devices will cause an explosive growth in communication traffic.Macro base stations can no longer meet traffic coverage.The emergence of heterogeneous cellular networks,the joint deployment of small base stations and macro base stations can effectively guarantees traffic coverage,but the heterogeneous cellular networks also brings huge energy consumption.However,issues,how to reduce the energy consumption of the network,and how to ensure the quality of service for users,remain waiting to be solved.According to the research of network energy consumption,the effective measures are base station sleeping and base station power control currently.The combination of the two measures will maximize the advantages of both sides.This thesis takes the heterogeneous cellular network composed of macro base stations and femto base stations as the basis research scenarios,and proposed joint optimization algorithms for femto base station sleeping and power control in single macro base station scenarios and multi-macro base station scenarios,in order to reduce network power consumption under the premise of ensuring traffic coverage.For the single macro base station scenario in a heterogeneous cellular network,this thesis studies the joint optimization problem of base station sleeping and power control in the downlink.The original problem can be divided into three parts: the base station's e traffic forecast stage,and the error optimization of the traffic forecast result and base station sleeping and power control stage.Radial basis function neural network is used for traffic forecast;according to Markov characteristics,the traffic forecast value is optimized for errors to obtain the revised predicted value;according to the revised predicted value,the sleeping proportion of the femto base station group is obtained.Through the proportional sleep,regard non-sleep base stations as particles,taking particle swarm optimization algorithm to iteratively obtain the optimal femto base station group transmit power.The simulation results show that the joint optimization strategy of base station sleep and power control in the single macro base station scenario proposed in this thesis can effectively adapt to the time-varying characteristicsof traffic.Compared with the network without this algorithm,the total power consumption of the network is reduced by 53%.For Multi-macro base station scenario in a heterogeneous cellular network,this thesis studies the joint optimization strategy of base station sleep and power control in full duplex mode.Based on the interference of the base station full duplex and network traffic coverage,the wavelet neural network is used for the network.Internal historical business data is decomposed,forecast,and reconstructed.On the one hand,the traffic volume is predicted,on the other hand,error optimization is implemented.The base station sleep algorithm considers the user's re-association,determines the base station sleep standard and the number of sleep according to the predicted traffic volume.After the base station power control algorithm clustered the femto base stations,the particle swarm optimization algorithm is executed in the cluster to obtain a local optimal solution,and the global optimal solution is obtained through inter-cluster communication.Finding the solution in the cluster,the target vector is obtained through the information exchanging between the clusters,and the optimal power adjustment strategy is obtained.Compared with the network without the joint optimization algorithm of base station sleep and power control in the Multi-macro base station scenario proposed in this thesis,the total power consumption of the network is reduced by 39% under the premise of ensuring traffic coverage in the scenario.