Research On Resource Schedule Strategy Of Cooperative Transmission In Cloud Radio Access Network

With the explosive growth of traffic,Cloud Radio Access Network(C-RAN)has been proposed as a promising network architecture for its low cost and high bandwidth.In CRAN,the dense deployment of remote radio head(RRH)leads to serious inter-cell interference.Benefit from the centralized signal processing,coordinated multipoint transmission(CoMP)is one of the most promising paradigms for mitigating interference in C-RAN.However,there is still some challenges to employ CoMP in C-RAN.On the one hand,CoMP may incur redundant data transmission over fronthaul network.Especially when the cooperation scale is large,the fronthaul network is under great pressure.On the other hand,large-scale collaborative signal processing will produce large computational load and additional power consumption,which requires green management.To solve the above problems,this thesis jointly optimizes the heterogeneous resources in C-RAN employing CoMP,including wireless resources,fronthaul resources and computing resources.The proposed strategy can alleviate interference,increase network throughout and reduce energy consumption.First,this thesis introduces the research background and network architecture of CRAN.Then the CoMP technology in C-RAN is analyzed,including the category and clustering strategy of CoMP.This thesis focuses on resources schedule strategy for CoMP in C-RAN,including wireless side optimization,joint scheduling of wireless resources and fronthaul resources,and joint scheduling of wireless resources and computing resources.Second,a joint scheduling strategy of optical and wireless resources for cooperative transmission is proposed.A dual timescale resource management framework is established to solve the fronthaul redundant transmission problem caused by cooperation in C-RAN.Further,the wavelength allocation strategy is obtained by reinforcement learning algorithm according to the network historical information.Then,the overlapping coalition game algorithm is used to dynamically adjust the users’ cooperative sets to improve the user throughput under the fronthaul bandwidth constraint.Then RB resource is allocated based on user cooperative sets.The results show that the proposed scheme can effectively improve the fronthaul transmission efficiency and system throughput.Thereafter,a resource scheduling strategy based on RRH clustering mapping is proposed.By analyzing the system power consumption in cooperative transmission scenarios,the dynamic power consumption model of virtualized Baseband Unit(v BBU)based on cluster is constructed.RRH clustering is formed according to the average channel quality and BBU power consumption.Furthermore,a power-aware joint beamforming and RRH selection strategy is proposed.In the cluster,the multi agent reinforcement learning algorithm is used to design the beamforming for users and activate RRH to optimize the transmission power consumption.The results show that the proposed strategy can effectively reduce network energy consumption while improving user signal quality.