Research On Channel State Information Acquisition For Massive MIMO Systems

Massive Multiple-Input Multiple-output(Massive MIMO)technology can achieve unprecedented array gain and high spatial resolution through the deployment of large scale antenna array in the base station.It also improves the spectrum utilization,power efficiency and communication quality of the system significantly.Massive MIMO is consequently considered as an attractive technology to deal with the explosive growth of data service demand in the next generation mobile communication system.However,the potential advantage of massive MIMO technology depends on the accurate Channel State Information(CSI)obtained from the base station.Therefore,how to obtain CSI accurately and efficiently becomes the key to the application of massive MIMO technology.Therefore,exploring low overhead CSI acquisition methods is the main propose in this thesis.After studied current CSI acquisition methods,the spatial correlation,temporal correlation or sparseness in the virtual angular domain of the massive MIMO channel can be used to reduce the overhead for acquiring CSI.On this basis,this thesis further studies the common sparse characteristics of each subcarrier channel in a massive MIMO orthogonal frequency division multiplexing(OFDM)system and the temporal correlation of sparse support set.This means that it can obtain the sparse support set of all subcarrier channels only at the expense of a small resource overhead on a single subcarrier,so as to reduce the channel dimension.It thus greatly reduces the overhead of the base station for CSI acquisition.In addition,compressed sensing technology is also discussed in this thesis to reduce the pilot overhead required for acquisition of sparse support set information and improve the timeliness of information.Based on the study of the characteristics of massive MIMO-OFDM channel,a novel and efficient CSI acquisition method for massive MIMO-OFDM system is studied in this thesis.It not only reduces the required pilot overhead and the feedback overhead of the CSI to the same degree as the sparseness of the channel,but also reduces the computation load for channel estimation significantly.The CSI acquisition method studied in this thesis provides certain reference value for the research and implementation of massive MIMO system.