 Research
 Open Access
Efficient Bayesian compressed sensingbased channel estimation techniques for massive MIMOOFDM systems
 Hayder ALSalihi^{1}Email author and
 Mohammad Reza Nakhai^{1}
https://doi.org/10.1186/s1363801607969
© The Author(s) 2017
 Received: 3 October 2016
 Accepted: 12 December 2016
 Published: 23 February 2017
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, CSbased 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 multitask (MT) version, so the developed MTBCS 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.
Keywords
 Massive multiple input multiple output (MIMO)
 Channel estimation
 Bayesian compressed sensing (BCS)
 Pilot contamination
 Channel state information (CSI)
 Multitask Bayesian compressed sensing (MTBCS)
1 Introduction
The main activity of recent research has identified that the major targets for the next generation of mobile communications, the socalled 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]. From a highlevel perspective, there is a promising technology that enables reaching higher fifth generation targets, called a massive multiple input multiple output (MIMO). A massive MIMO can be defined as a system using a large number of antennas at the base station; accordingly, a significant beamforming can be achieved and the system capacity can serve a large number of users [2].
When comparing massive MIMO to the conventional MIMO systems, massive MIMO shows several advantageous aspects. Firstly, as the number of the antennas at the base station goes to high values, the simplest coherent combiner and linear precoder turn out to be optimal. Secondly, by exploiting the features of the channel reciprocity, additional antennas increase the network capacity significantly without the need for additional feedback overhead. Thirdly, enabling the power reduction in the uplink and in the downlink can provide the potential for smallcell size shrinking [3].
The major limiting factor in massive MIMO is the availability of accurate, instantaneous channel state information (CSI) at the base station. The CSI is typically acquired by transmitting predefined pilot signals and estimating the channel coefficients from the received signals by applying an appropriate estimation algorithm [1–3].
Channel estimation accuracy depends on having perfect orthogonal pilots allocated to the users; however, to achieve high spectral efficiency, the same carrier frequency should be used in the neighbouring cells by following a specific reuse pattern. This leads to the creation of a spatially correlated intercell interference, known as pilot contamination, which reduces the estimation performance and spectral efficiency [1–3].
The pilot contamination problem was analyzed in [4] and it has shown that the precoding downlink signal of the base station in the serving cell contaminated the received signal of the users roaming in other cells. The authors of [5] analyzed the pilot contamination problems in multicell massive MIMO systems relying on a large antennas at the base station, and demonstrated that the pilot contamination problem persisted in largescale MIMO [6].
However, pilot contamination could be reduced by reducing the number of pilots. A multiuser scenario therefore needs to reduce the number of pilots without affecting the channel impulse response (CIR) quality. Hence, the development of efficient channel estimation techniques for massive MIMO that are computationally less complex and require a fewer number of pilots is a challenge that should be thoroughly addressed [7].
Recently, compressed sensing (CS) techniques have received attention since they can recover the unknown signals from only a small number of measurements, thus using significantly far fewer samples than is possible via the conventional Nyquist rate, which is the signal recovery scheme developed for CS to exploit the sparse nature of signals (that is, only a small number of components in a signal vector are nonzero). CS allows for accurate system parameter estimation with fewer pilots; thereby, addressing the pilot contamination problem and improving the bandwidth efficiency [8, 9]. However, classical CS algorithms require prior knowledge of channel sparsity, which is usually unknown in practical scenarios. In addition, to apply CS algorithms, the sampling matrix must satisfy the restricted isometry property (RIP) for guaranteeing reliable estimators. Such a condition cannot be easily verified because it results computational demanding [10, 11].
To overcome the scarcity of CSbased 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. Also, the sampling matrix condition is efficiently overcome based on probabilistic formulation [12–14].
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.
Furthermore, by exploiting the common statistical sparsity inherent in different multipath signals, we extend the BCS algorithms to a multitask version for simultaneously reconstructing multiple signals, thus leading to MTBCS [15, 16].

The BCSbased channel estimation algorithm has been proposed for massive MIMO to address the pilot contamination problem.

We have also proposed to enhance the performance of the BCSbased estimator through the principle of thresholding to select the most significant taps to improve the channel estimation accuracy.

In addition, we have exploited the common statistical sparsity distribution to enhance the estimation accuracy performance through the proposed MTBCSbased estimator.

To provide the benchmark for the minimum performance error of the BSC and MTBCS, the Cramer Rao bound (CRB) has been drawn for BCS and it has been derived and drawn for MTBCS.
The remainder of this paper is organized as follows. The multicell massive MIMO system model is presented in Section 2. The BSCbased and the MTBSC based channel estimation details are reviewed in Sections 3 and 4, respectively. In section 5, we provide the CramerRao bound analysis. Section 6 presents the simulation results. Finally, the final conclusions are drawn in Section 7.
The following notation is adopted throughout the paper: \(\mathbb {C}\) denotes the complex number field. For \({A} \in \mathbb {C}\), we have A=A _{ R }+j A _{ I }, where \(j=\sqrt {1}\), while A _{ R } and A _{ I } are the real and imaginary parts of A, respectively. For any matrix A, A _{ i,j } denotes the (i,j)th element. The transpose, inverse and Hermitian transpose operators are denoted by (.)^{ T }, (.)^{−1}, and (.)^{ H }, respectively. Upper bold font are used to denote matrices while lower light font are used to denote vectors, lower and upper case represents the time domain and frequency domain, respectively. The I denotes an identity matrix, \(diag\{\underline {\mathbf {X}}\}\) denotes the diagonal matrix with the diagonal entries equal to the elements of X and \(\hat {X}\) represents the estimate of \(\hat {X}\). The Frobenius and spectral norms of a matrix x are denoted by ∥x∥_{ F } and ∥x∥_{2} respectively. E{.} has been employed to denote expectation with regard to all random variables within the brackets. A Gaussian stochastic variable o is the denoted by o∼N(r,q), where r is the mean and q is the variance. Also, a random vector x having the prober complex Gaussian distribution of mean μ and covariance Σ is indicated by x∼C N(x;μ,Σ), where, \( N(\mathbf {x};\boldsymbol {\mu },\boldsymbol {\Sigma })=\frac {1}{det(\pi \boldsymbol {\Sigma })} e^{(\mathbf {x}\boldsymbol {\mu })\boldsymbol {\Sigma }^{1}(\mathbf {x}\boldsymbol {\mu })}\), for simplicity we refer to C N(x;μ,Σ) as x∼C N(μ,Σ).
2 Massive MIMO system model
At the beginning of the transmission, all mobile stations in all cells synchronously transmit OFDM pilot symbols to their serving base stations. Let the OFDM pilot symbol of user n in the cth cell be denoted by \(\mathbf {x}^{n}_{c}=[{X}^{n}_{c}[1]\ {X}^{n}_{c}[2] \cdots {X}^{n}_{c}[K]]^{T}\), where K is the number of subcarriers. The OFDM transmission partition the multipath channel between the user and each antenna of the BS into K parallel independent additive white Gaussian noise (AWGN) subchannels in the frequency domain. Each subchannel is associated with a subcarrier. Let \({H}^{n}_{c^{*},c,i}[k]\) denote the kth subchannel coefficient between the nth user in the cth cell and the ith antenna of the BS of cell c ^{∗} in the uplink.
where \(\mathbf {X}^{n}_{c^{*}}=\text {diag}\{\mathbf {x}^{n}_{c^{*}}\}\), \(\mathbf {h}^{n}_{c^{*},c,i}=[{H}^{n}_{c^{*},c,i}[1]\cdots {H}^{n}_{c^{*},c,i}[K]]^{T}\) and \(\mathbf {v}_{c^{*},i}=[{V}_{c^{*},i}[1]\cdots {V}_{c^{*},i}[K]]^{T} \sim CN(0,{\sigma }_{v}^{2})\). Let \(\mathbf {g}^{n}_{c^{*},c,i}=[g^{n}_{c^{*},c,i}[1] \cdots g^{n}_{c^{*},c,i}[\ell ] \cdots g^{n}_{c^{*},c,i}[L]]^{T}\) collect the samples of the sampled multipath CIR between the nth user of the cth cell and the ith antenna of the BS in cell c ^{∗}, where L is the number of the channel taps and \(g^{n}_{c^{*},c,i}[\ell ]\) corresponds to the ℓth channel tap. The K frequency domain channel coefficients, i.e., \(\mathbf {h}^{n}_{c^{*},c,i}\), can be calculated as the Kpoint DFT of the CIR samples, i.e., \(\mathbf {g}^{n}_{c^{*},c,i} \in \mathbb {C}^{L \times 1}\), e.g., [18].
The channel coefficient is modelled as \(g^{n}_{c^{*},c,i}[\ell ]=\sqrt {{\phi }_{c^{*},c,i}}[\ell ] {\psi }_{c^{*},c,i}[\ell ]\) for 1≤ℓ≤L, where \({\phi }_{c^{*},c,i}\phantom {\dot {i}\!}\) model the pathloss and shadowing (largescale fading), while the term \(\phantom {\dot {i}\!}{\psi }_{c^{*},c,i}\) is assumed to be independent identical distribution (i.i.d) of unknown random variables with C N(0,1) (smallscale fading) [3].
3 BCSbased channel estimation
In common literature, channel estimation methods are classified into parametric and Bayesian approaches. A standard parametric approach is the best linear unbiased estimator, which is often referred to as least squares channel estimation. In contrast to parametric methods, the Bayesian approach treats the desired parameters as random variable with apriori known statistics. Clearly, the a priori probability density function (PDF) of the channel is assumed to be perfectly known at the receiver [21, 22]. Based on the Bayesian channel estimation philosophy, the estimation of unknown parameters is the expectation of the posterior probabilistic distribution that is proportional to the prior probability and the likelihood of the unknown parameters.
where β represents the hyperparameters that control the sparsity of the channel while σ ^{2} is the net sum of the noise variance and interference variance.
where ζ=d i a g{β _{1},β _{2},…,β _{ K }}.
Now, to obtain the estimated channel \(\hat {\mathbf {g}}^{\prime n}_{c^{*},c^{*},i}\), we need to find the heyparmarpater σ ^{2} and β that can be obtained from the second term on the righthand side of (10) by applying a type −I I maximum likelihood procedure by operating a RVM.
The β _{ k } and \(\sigma _{k}^{2}\) which maximize the log marginal likelihood are then found iteratively by setting β and σ ^{2} to initial values and then finding values for \(\boldsymbol {\mu }^{n}_{c^{*},c^{*},i}\) and \(\boldsymbol {\Sigma }^{n}_{c^{*},c^{*},i}\) from (12) and (13). These values are then repeatedly used to calculate a new estimate for β _{ k } and σ ^{2} and until a convergence criteria is met.
Further details of the BCS algorithm can be found in [23, 24]. The procedure for implementation of the proposed technique is summarized in Algorithm 1.
In contrast to the conventional BCSbased estimator, it can also improve the performance of the BCS estimator based on the principle of thresholding, which can be applied to keep the most significant taps. The proposed algorithm applies a threshold approach by retaining the channel taps that have energy above a threshold value of ϱ and set the other taps to zero. The value of ϱ is the energy of the channel impulse response.
4 Multitask BCS based channel estimation
With a high probability of user movements, the massive MIMO system channel may vary. Consequently, the channels at different time instants/locations are different but share the same common statistical property. As a result, to estimate the current channel, we can exploit the previous compressive vectors in addition to the current compressive vector [15].
for j=1,2,…J where J is the number of the task, \(\mathbf {A}^{n}_{c^{*},j}, \mathbf {g}^{\prime n}_{c^{*},c^{*},i,j}\phantom {\dot {i}\!}\) and \(\phantom {\dot {i}\!}\mathbf {z}_{c^{*},i,j}\) represents the jth measurement matrices,channel vector and the noise vector, respectively [15].
where ψ=d i a g(ψ _{0},ψ _{1},ψ _{2},…,ψ _{ K }).
Further information on MTBCS can be found in [16].
5 CRB for BCSbased estimator
where l(θ,f) is the likelihood function corresponding to the observation f, parameterized by θ [25].
Theorem 1
Proof
See Appendix 1. □
6 Simulation results
To verify the accuracy of our analytical results, the simulation parameters can be summarized as follows: the number of antennas is 100, the number of users is 100, the number of the channel taps is 500, the number of subcarrier K is 4096 and the convergence δ is 10^{−6}. The simulation results are obtained by averaging over 1000 realizations.
Complexity analysis
Estimators  Computation complexity 

RLS  O(L ^{2}) 
BCS  O(K L ^{2}) 
MTBCS  O(K L ^{3}) 
OMP  O(L log(K)) 
BiAMP  O(L K+K) 
7 Conclusions
To address the pilot contamination problem in massive MIMO systems, we proposed a BCSbased channel estimation algorithm for the multicell multiuser massive MIMO. The simulation results have revealed that the BCSbased channel estimation algorithm has tremendous improvement over conventionalbased channel estimation algorithms and can address the pilot contamination problem. Furthermore, the proposed technique can be enhanced by thresholding the CIR to a certain value and also by exploiting the common sparsity feature inherent in the system channel. In addition, the number of antennas and the compression ratio should be selected wisely to achieve optimum estimation accuracy.
8 Appendix 1: Proof of Theorem 1
Declarations
Acknowledgements
This work is supported by the Iraqi Higher Committee of Educational Development (HCED). The authors would like to acknowledge its financial support.
Competing interests
The authors declare that they have no competing interests.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Authors’ Affiliations
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