Channel Estimation Algorithm For 5G Communication System Under Joint Sparse Channel Model

Signal will be affected by multipath delay and fading in the propagation process.In order to achieve correct detection at the receiving end,channel estimation technology is very critical in the communication system.A large number of studies have proved that the wireless multipath channel is sparse,and channel estimation technology based on the compressed sensing theory can effectively utilize the sparseness of the channel,improve the estimation performance and spectrum utilization.This paper mainly studies the channel estimation algorithm under the joint sparse model,and the details are as follows:The characteristics of the wireless sparse channel in the 5G communication system are analyzed.The two conclusions are that the 5G discrete channel response is more sparse compared to 4G and the channel response of multiple consecutive symbols has strong correlation.OFDM system and channel estimation model are introduced.In the sparse channel environment,the traditional channel estimation algorithms of LS and DFT and the channel estimation algorithms based on compressed sensing of OMP and CoSaMP are simulated.Because the channel estimation algorithms based on compressed sensing can effectively use the sparse characteristics of the channel,the estimation performance of the algorithm is better than traditional channel estimation.The simulation results show that compared with the traditional channel estimation algorithm based on compressed sensing,the channel estimation algorithm based on compressed sensing has better performance in estimating mean square error and bit error rate under different signal-to-noise ratios and multipath numbers.A joint sparse channel model more in line with the 5G communication system channel is introduced.Under this model,the channel response support set of consecutive multiple OFDM symbols is the same or partly the same.Based on this model,an improved SOMP algorithm is studied.The algorithm first estimates the number of public support set for multiple consecutive symbols,SOMP algorithm is used in the public support set to improve the accuracy of the estimation of the support set elements,and OMP algorithm is used in the non-public support set to avoid incorrect estimation.The simulation results show that sparse channel estimation where the support set is not exactly the same,the performance of traditional SOMP algorithm decreases significantly.The improved SOMP algorithm can still maintain the estimation performance when the number of common support set is unknown at the receiver,and under the joint sparse model,the improved SOMP algorithm estimation performance is always better than OMP algorithm.According to the 3GPP protocol,5G PDSCH link communication system is built.The output data of each module is simulated under the given parameter configuration.After the output baseband signal goes through the wireless channel,different channel estimation algorithms are used.Analyze the performance of different channel estimation algorithms with channel estimation mean square error and system bit error rate,simulation results show that the performance of this algorithm is better than traditional algorithms in 5G PDSCH link communication systems.