Resource Allocation And Power Control Optimization In Cloud Radio Access Network

As the concept of the Internet of Everything is put forward,the number and types of devices in the future network will increase significantly,which could bring more challenges to the spectrum utility and energy efficiency of the next generation of the network.The existing cellular network architecture turns out to be difficult to meet the enormous traffic demand and service performance requirements.To tackle the bottomneck,cloud radio access network(C-RAN),a new network architecture,is designed.The C-RAN network is proved that it can achieve flexible network capacity and higher network by concentrating rich BBU(baseband unit)resources in the BBU pool and deploying low-power remote wireless nodes throughout the network to achieve flexible network resources.Despite the above advantages,when the C-RAN network is densely distributed,it would also meet some challenges: 1)the capacity limitation of the frounthaul restricts further enlargment of the network capacity;2)high density distributed RRHs(Remote Radio Heads)cause the energy consumption problem and the interference problem;3)the non-ideal channel state problem caused by the incomplete network feedback information,which leading to the consequent loss of transmission quality.To overcome the above challenges,and to futher reduce network energy consumption,improve network throughput and enhance user experience,the following work has been done:1)Energy-efficient Resource Allocation with Cache Placement and Task Offloading in Cloud Radio Access NetworkTo further analyze the capacity limitation of the forward backhaul,our work designed a transformation based on the characteristic of resource conversion,that the storage resources and computing resources of the RRHs can be transformed into the communication resources of the frounthaul.A content caching strategy and task offloading strategy based on content popularity is established,which summarized into a joint optimization problem.The optimal target is to achieve higher data service rate and larger network capacity with the lowest energy consumption and the largest throughput.By applying the Lyapunov optimization method,the problem is decomposed into several sub-problems: the content caching placement problem and task offloading problem.An online beamforming design algorithm is proposed,and theoretical analysis and numerical analysis are given to prove the performace of our work.2)NOMA-based Power Control Optimization with Constraint of Outage Probability in Cloud Radio Access NetworkAs for the capacity of the forward backhaul,our work considers the power domain-based NOMA transmission mechanism and establishs the outage probability expression based on the beamforming vector and the channel estimation.In this work,we study the power-domain non-orthogonal multiple access(PD-NOMA)scheme and apply a channel estimation method to characterize the impact caused by imperfect CSI(Channel State Infromation).Outage probability constraint is applied as the network performance index,which tradeoffs between QoS and energy consumption of the network.We studied the beamforming design with the lowest energy consumption and the joint problem of the beam at the transmitting terminal.The network control schedual is decided by calculating the outage probability and the average network energy consumption.To solve the optimization problem,the non-convex outage probability function is approximated based on the worst case of the network,by applying minimum mean square error(MMSE)method and probability theory.To reduce computational complexity,the time-coupled decision variables are solved separately by funding a two-stage iterative algorithm.Finally,simulation results verify the control effect of the proposed algorithm.