Compressive sensing based matching pursuit algorithm can estimate the channel state information of communication system with shorter pilot sequences. It has the advantages of lower computational complexity and less number of pilots. However
the estimation accuracy is relatively low. Assigning different weights to the estimated channel according to the estimation error can improve the estimation performance under low Signal Noise Ratio (SNR) conditions. Nevertheless
it will reduce the estimation accuracy under high SNR conditions. To address this issue
an improved weighted matching pursuit algorithm is proposed. The information of weight value is obtained by training the channel data. Adjusting the weight values adaptively with the iterations can further improve the estimation accuracy. The simulation results show that the proposed algorithm can improve the estimation performance under both low and high SNR conditions.
Chen Z, Sohrabi F, Yu W. Multibi估计精Sparse Activity Detection for Massive Random Access: Massive MIMO Versus Cooperative MIMO[J]. IEEE Transactions on Wireless Communications, 2019, 18(8): 4060-4074.
Liu Y, Zhao M, Xiao L, et al. Pilot Domain NOMA for Grantn应用信号Massive Random Access in Massive MIMO Marine Communication System[J]. China Communications, 2020, 17(6): 131-144.
Figueiredo F A P D, Cardoso F A C M, Moerman I, et al. Channel Estimation for Massive MIMO TDD Systems Assuming Pilot Contamination and Frequency Selective Fading[J]. IEEE Access, 2017, 5: 17733-17741.
Nguyen H. Low Complexity Maxplex Uplink Power Control in Massive MIMO System with Experiment on Testbed[C]//2019 26th International Conference on Telecommunications (ICT). Hanoi, Vietnam: IEEE, 2019: 351-355.
Albreem M A M, Elsaleh A A, Juntti M. Linear Massive MIMO Uplink Detector based on Joint Jacobi and Gaussiorunic Methods[C]//2020 16th International Conference on the Design of Reliable Communication Networks DRCN 2020. Milano, Italy: IEEE, 2020: 1570610672.
Mezghani A, Swindlehurst A L. Blind Estimation of Sparse Broadband Massive MIMO Channels with Ideal and Onealel ADCs[J]. IEEE Transactions on Signal Processing, 2018, 66(11): 2972-2983.
Ali A, Carvalho E D, Health R W. Linear Receivers in Noneiversgnsnt Massive MIMO Channels with Visibility Regions[J]. IEEE Wireless Communications Letters, 2019, 8(3): 885-888.
Narayanan S, Sahoo S K, Makur A. Greedy Pursuits based Gradual Weighting Strategy for Weighted l1-Minimization[C]//2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Calgary, AB, Canada: IEEE, 2018: 4664-4668.
Lu W, Wang Y, Hua X, et al. Compressive Downlink Channel Estimation for FDD Massive MIMO using Weighted lp Minimization[C]//2018 IEEE 4th International Conference on Computer and Communications (ICCC). Chengdu, China: IEEE, 2018: 8780953.
Gilabert P L, L lab-Bueno D, Montoro G. Spectral Weighting Orthogonal Matching Pursuit Algorithm for Enhanced Out-of-Band Digital Predistortion Linearization[J]. IEEE Transactions on Circuits and Systems II: Express Briefs, 2019, 66,(7): 1277-1281.
Papazafeiopoulos A, Kourtessis P, Renzo M D, et al. Performance Analysis of Cell-free Massive MIMO Systems: A Stochastic Geometry Approach[J]. IEEE Transactions on Vehicular Technology, 2020, 69(4): 3523-3537.