Research And Implementation Of Channel Estimation For PDSCH In 5G System

In 2019,the 5th generation(5G)is booming in China,and various 5G application innovations are springing up.Compared with previous generations of mobile communication systems,5G has higher data transmission rate,wider network coverage,higher network capacity,and also can accommodate more devices.Based on the national major project “Enhance the research and development of mobile broadband 5G terminal simulators”,the thesis focuses on the channel estimation of the 5G Physical Downlink Shared Channel(PDSCH)receiver.The main works are as follows:In this thesis,5G frame structure,time-frequency resource and PDSCH physical layer process are studied in turn based on 5G R15 specification.On this basis,the pilot-based channel estimation is studied.The Discrete Fourier Transformation(DFT)Least Square(LS)algorithm can achieve a relative balance of performance and complexity,whereas the energy leakage exists in the high signal-to-noise of non-sampled interval channel.Thus,an improved algorithm of mirror expansion and threshold decision is proposed.Simulation results show that when the bit error rate reaches410-,the proposed algorithm achieves at least 4d B performance gain compared with the original DFT,and has only about 0.8d B performance loss compared with LMMSE,alleviating the“floor effect” at high signal-to-noise ratio.In addition,this thesis makes use of the sparseness of multipath channels in the time domain,combining compressed sensing theory with channel estimation,and focusing on research and analysis of reconstruction algorithms in compressed sensing theory.Based on the original algorithm,a singular value assisted reconstruction algorithm is proposed.Simulation results show that the proposed algorithm has at least 2d B performance gain compared with the original Sparsity Adaptive Matching Pursuit(SAMP)algorithm under the premise of ensuring the reconstruction efficiency.At the same time,it also solves the problem of Compression Sampling Matching Pursuit(Co Sa MP)over-reliance on channel sparsity.Finally,on the basis of PDSCH link research and MATLAB simulation,this thesis comprehensively compares the algorithm performance,complexity and project requirements.Then,the DFT improved algorithm for multi-core digital signal processing(DSP)implementation is chosen.Based on the implementation results on the CodeComposer Studio(CCS)hardware platform,and analysis for its performance and operating cycle,the feasibility of the proposed scheme is verified.