Efficient Bayesian compressed sensing-based channel estimation techniques for massive MIMO-OFDM systems

Hayder Al-Salihi,Mohammad Reza Nakhai

doi:10.1186/s13638-016-0796-9

Hayder Al-Salihi, Mohammad Reza Nakhai

Open Access

https://doi.org/10.1186/s13638-016-0796-9

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Abstract

Efficient and highly accurate channel state information (CSI) at the base station (BS) is essential to achieve the potential benefits of massive multiple input multiple output (MIMO) systems. However, the achievable accuracy that is attainable is limited in practice due to the problem of pilot contamination. It has recently been shown that compressed sensing (CS) techniques can address the pilot contamination problem. However, CS-based channel estimation requires prior knowledge of channel sparsity to achieve optimum performance, also the conventional CS techniques show poor recovery performance for low signal to noise ratio (SNR). To overcome these shortages, in this paper, an efficient channel estimation approach is proposed for massive MIMO systems using Bayesian compressed sensing (BCS) based on prior knowledge of statistical information regarding channel sparsity. Furthermore, by utilizing the common sparsity feature inherent in the massive MIMO system channel, we extend the proposed Bayesian algorithm to a multi-task (MT) version, so the developed MT-BCS can obtain better performance results than the single task version. Several computer simulation based experiments are performed to confirm that the proposed methods can reconstruct the original channel coefficient more effectively when compared to the conventional channel estimator in terms of estimation accuracy.

Highlights

The main activity of recent research has identified that the major targets for the generation of mobile communications, the so-called fifth generation of mobile communications, are to achieve 1000 times the system capacity and 10 times the spectral efficiency, energy efficiency and data rate, and 25 times the average cell throughput [1]
A massive multiple input multiple output (MIMO) can be defined as a system using a large number of antennas at the base station; a significant beamforming can be achieved and the system capacity can serve a large number of users [2]
To overcome the scarcity of compressed sensing (CS)-based channel estimation in massive MIMO systems, in this paper, we propose an improved channel estimation scheme based on the theory of Bayesian CS (BCS) that introduces relevance vector machines (RVM) and statistical learning information (SLI) into standard CS; whereby, probabilistic a priori information regarding the channel sparsity can be exploited for more reliable channel recovery to mitigate the pilot contamination problem

Summary

Introduction

The main activity of recent research has identified that the major targets for the generation of mobile communications, the so-called fifth generation of mobile communications, are to achieve 1000 times the system capacity and 10 times the spectral efficiency, energy efficiency and data rate, and 25 times the average cell throughput [1]. Channel estimation accuracy depends on having perfect orthogonal pilots allocated to the users; AL-Salihi and Nakhai EURASIP Journal on Wireless Communications and Networking (2017) 2017:38 to achieve high spectral efficiency, the same carrier frequency should be used in the neighbouring cells by following a specific reuse pattern. To overcome the scarcity of CS-based channel estimation in massive MIMO systems, in this paper, we propose an improved channel estimation scheme based on the theory of Bayesian CS (BCS) that introduces relevance vector machines (RVM) and statistical learning information (SLI) into standard CS; whereby, probabilistic a priori information regarding the channel sparsity can be exploited for more reliable channel recovery to mitigate the pilot contamination problem. Compared with the classical based scheme, our simulation results indicate that the proposed channel estimation methods provide improved estimation accuracy and can address the pilot contamination problem. −1(x−μ), for simplicity we refer to CN(x; μ, ) as x ∼ CN(μ, )

Massive MIMO system model

Conclusions