Research On Green Network Based On Cell Dynamic Sleeping

With the increasingly serious climate and energy problems,energy efficiency issues in communication networks have received unprecedented attention,and green networks have also continued to develop.Among them,the cell dynamic sleeping technology as a key technology in the green network has been widely researched and applied in the fourth generation(4G)mobile network.However,with the advent of the fifth-generation(5G)mobile network era,cell dynamic sleeping technology is facing more new challenges.Heterogeneous Ultra-Dense Networks(H-UDNs)have been applied to fifth-generation(5G)mobile networks.In H-UDNs,the large number of base stations(BS)have increased the communication rate and coverage rate,but it brought more energy consumption,and the network resources will be more difficult to fully utilize.However,some emerging technologies in 5G also bring many new ideas for the development of green networks at the same time.This thesis focuses on the characteristics of 5G networks and researches green networks based on cell sleeping technology.This thesis uses Q-learning to combine cell sleeping technology with load balancing technology to achieve dynamic load adjustment of cell.At the same time,cell sleeping technology is combined with D2D communication in the 5G network.The combination further improves the energy efficiency and coverage of the cell.The main work of this paper includes:This thesis proposes a small cell dynamic load adjustment algorithm in ultra-dense networks.The algorithm applies Q-learning to learn the effective offloading strategies of small cells.These strategies could combine cell sleeping technology with load balancing technology to improve the energy efficiency and throughput of the communication network.Based on the proposed algorithm,the heterogeneous ultra-dense network could adjust the traffic load of small cells to turn off some redundant small cells or balance the load of heavily loaded small cells and lightly loaded small cells.Simulation results show that the proposed algorithm in this paper could turn off redundant BSs to reduce network energy consumption when the network load is light and balance the network load when the network load is heavy to improve the network throughput.In this thesis,we also propose a cell sleeping strategy based on D2D cluster communication.First,this thesis derives the optimal D2D cluster head density by solving the problem of maximizing the number of users which could communicate with the D2D cluster head.Then,this thesis further selects the optimal D2D cluster heads by considering two factors,the received signal strength and the user's signal to interference plus noise ratio.After selecting the optimal D2D cluster heads,this thesis combines the cell sleeping algorithm with D2D cluster communication,and improves energy efficiency by adjusting lightly loaded BSs and switching them to sleeping mode.The simulation results show that the proposed cell sleeping strategy in this thesis not only reduces network energy consumption,but also further improves network user coverage.In summary,this thesis combines cell sleeping technology with machine learning algorithms and D2D communication technology to research the application of cell sleeping technology in 5G networks.The proposed algorithms adjust the load of the network and optimize energy efficiency.Finally,the thesis evaluates the proposed algorithms through simulation.The simulation results show that the algorithms not only reduce the energy consumption but also improve the user coverage probability and the throughput of the network.