Research Of Channel Estimation Based On Compressive Sensing In Access Cloud Architecture

Compressed Sensing(CS)theory can successfully reconstruct a sparse signal from fewer samples than those obtained by Nyquist rate in the field of mathematics and signal processing.Then at the receiver,the optimal reconstruction algorithm is used to reconstruct the sparse signal from the limited sampling values.CS theory can be applied to sparse channel estimation in orthogonal frequency division multiplexing(OFDM)system and Cloud radio access network(C-RAN)system to reduce the number of pilot symbols,and obtain good channel estimation performance.Besides,the optimal pilot allocation in CS-based channel estimation should be investigated exclusively because the equidistant pilot placement is not the best.In view of this,the CS-based channel estimation including the corresponding pilot optimization and signal reconstruction algorithm in OFDM and C-RAN systems is studied in the dissertation.A new scheme based on hybrid genetic algorithm is investigated for the pilot design which is achieved minimizing the coherence of the dictionary matrix in CS channel estimation model.This thesis investigates a scheme of secondary optimization for the sub-optimal pilot selected by genetic algorithm which reduces the number of subsequent iterations and performs better than the traditional pilot scheme.And then,in order to improve spectrum utilization,the pilot sequences of different channels of the same user are inserted into the same time-frequency resource for transmission in C-RAN coordinated multi-point transmission/reception(CoMP)system,then the receiver obtains the different channel independent pilot and estimates each channel independently by CS-based sparse channel estimationWith the conventional approach in C-RAN,the remote radio head(RRH)quantizes and compresses the received pilot and data signals,and forwards the compressed signals to the baseband unit(BBU)on the fronthaul link.Then the BBU estimates the channel state information(CSI)on the basis of the received signals.Due to the centralized data processing of the BBU,the pressure of the fronthaul link transmission is added and the calculation of BBU is complicated.Based on the purpose of reducing the transmission pressure and calculation complexity analyzed above,the channel estimation of C-RAN system is moved to RRH in this thesis so as to acquire the CSI at RRH.How to estimate channel at the RRH? An adaptive weighting and matching pursuit(AWMP)algorithm is proposed,in which adaptive weighting and optimal sparseness searching after iteration takes place according to the estimated signal to noise ratio(SNR)and the matching principle.Then the channel information was estimated accurately without the known SNR and sparse degree.Compared to the traditional channel reconstruction algorithm,the method proposed here can obtain CSI more accurately with fewer pilots.