Massive MIMO Uplink Channel Estimation using Compressive Sensing

Over the past decades, wireless communication systems have made a real revolution. Huge amounts of information circulate on the networks every second. To meet the growing demand for data throughput, massive multiple-input multiple-output (MIMO) has been proposed as a key technology for future wireless communications by offering substantial energy gain and spectral capacity. However, the advantages of such a system generally requires that the channel state information should be available on the transmitter (CSIT). For this purpose, this work presents the implementation of a new channel estimation technique based on training sequence in Time Division Duplex (TDD) mode. As it is known, wireless communication tends to be sparse, that is why, in this paper, we will focus on this property and propose a new approach based on compressed sensing technique to estimate the Channel Impulse response (CIR) of the massive MIMO system. As a simulation results, it has been shown that the proposed algorithm Block Orthogonal Matching Pursuit (B-OMP) outperforms the old approach known as Adaptive Orthogonal Matching Pursuit (AOMP) especially in terms of Bit Error Rate (BER) where it makes a gain of 1 dB over AOMP algorithm. However, in terms of Normalised Mean Square Error (NMSE), both of them have shown the same performance, where both of curves are superimposed. On the other hand and in order to underline the performance of the proposed approach, a comparison in terms of computational complexity of the two algorithms has been done and it has proved that the complexity of B-OMP is in the order of only O(KNpNtL) while the complexity of AOMP is equal to O(KNpNtUL) so the AOMP requires Nt more iteration to recover the estimated signal.

[1]  Thomas L. Marzetta,et al.  Massive MIMO: An Introduction , 2015, Bell Labs Technical Journal.

[2]  Kien T. Truong,et al.  Compressive Channel Estimation in FDD Multi-Cell Massive MIMO Systems with Arbitrary Arrays , 2016, 2016 IEEE Globecom Workshops (GC Wkshps).

[3]  Olgica Milenkovic,et al.  Subspace Pursuit for Compressive Sensing Signal Reconstruction , 2008, IEEE Transactions on Information Theory.

[4]  Robert W. Heath,et al.  Blind Channel Estimation for MIMO-OFDM Systems , 2007, IEEE Transactions on Vehicular Technology.

[5]  Fumiyuki Adachi,et al.  Structured Matching Pursuit for Reconstruction of Dynamic Sparse Channels , 2014, 2015 IEEE Global Communications Conference (GLOBECOM).

[6]  Shahid Mumtaz,et al.  Joint CSIT Acquisition Based on Low-Rank Matrix Completion for FDD Massive MIMO Systems , 2015, IEEE Communications Letters.

[7]  Yi Shi,et al.  Joint Channel Training and Feedback for FDD Massive MIMO Systems , 2015, IEEE Transactions on Vehicular Technology.

[8]  Khaled Ben Letaief,et al.  Compressed CSI acquisition in FDD massive MIMO with partial support information , 2015, 2015 IEEE International Conference on Communications (ICC).

[9]  Deanna Needell,et al.  CoSaMP: Iterative signal recovery from incomplete and inaccurate samples , 2008, ArXiv.

[10]  Jitendra K. Tugnait,et al.  Blind estimation and equalization of MIMO channels via multidelay whitening , 2001, IEEE J. Sel. Areas Commun..

[11]  Jean-Luc Starck,et al.  Sparse Solution of Underdetermined Systems of Linear Equations by Stagewise Orthogonal Matching Pursuit , 2012, IEEE Transactions on Information Theory.

[12]  Yonina C. Eldar Sampling Theory: Beyond Bandlimited Systems , 2015 .

[13]  David James Love,et al.  Downlink Training Techniques for FDD Massive MIMO Systems: Open-Loop and Closed-Loop Training With Memory , 2013, IEEE Journal of Selected Topics in Signal Processing.

[14]  Sheng Chen,et al.  Spatially Common Sparsity Based Adaptive Channel Estimation and Feedback for FDD Massive MIMO , 2015, IEEE Transactions on Signal Processing.

[15]  Jing Qin,et al.  Sparse Randomized Kaczmarz for Support Recovery of Jointly Sparse Corrupted Multiple Measurement Vectors , 2017, Association for Women in Mathematics Series.

[16]  Honglin Zhao,et al.  Compressed sensing-based structured joint channel estimation in a multi-user massive MIMO system , 2018, Frontiers of Information Technology & Electronic Engineering.

[17]  Seung Joon Lee,et al.  On the training of MIMO-OFDM channels with least square channel estimation and linear interpolation , 2008, IEEE Communications Letters.

[18]  J. Tropp,et al.  CoSaMP: Iterative signal recovery from incomplete and inaccurate samples , 2008, Commun. ACM.

[19]  Stéphane Mallat,et al.  Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..

[20]  Vincent K. N. Lau,et al.  Distributed Compressive CSIT Estimation and Feedback for FDD Multi-User Massive MIMO Systems , 2014, IEEE Transactions on Signal Processing.

[21]  Imran Khan,et al.  Efficient compressive sensing based sparse channel estimation for 5G massive MIMO systems , 2018 .