Robust massive MIMO channel estimation for 5G networks using compressive sensing technique (original) (raw)
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Low-Complexity Channel Estimation in 5G Massive MIMO-OFDM Systems
Symmetry, 2019
Pilot contamination is the reuse of pilot signals, which is a bottleneck in massive multi-input multi-output (MIMO) systems as it varies directly with the numerous antennas, which are utilized by massive MIMO. This adversely impacts the channel state information (CSI) due to too large pilot overhead outdated feedback CSI. To solve this problem, a compressed sensing scheme is used. The existing algorithms based on compressed sensing require that the channel sparsity should be known, which in the real channel environment is not the case. To deal with the unknown channel sparsity of the massive MIMO channel, this paper proposes a structured sparse adaptive coding sampling matching pursuit (SSA-CoSaMP) algorithm that utilizes the space–time common sparsity specific to massive MIMO channels and improves the CoSaMP algorithm from the perspective of dynamic sparsity adaptive and structural sparsity aspects. It has a unique feature of threshold-based iteration control, which in turn depends...
Elektronika ir Elektrotechnika
Massive Multiple-Input Multiple-Output (MIMO) is envisioned to be a strong candidate technology for the upcoming 5th generation (5G) of wireless communication networks. This research work presents a novel Compressed Sensing (CS) and Superimposed Training (SiT) based technique for estimating the sparse uplink channels in massive MIMO systems. The proposed technique involves arithmetic addition of a periodic, but low powered training sequence with each user’s information sequence. Consequently, separately dedicated resources for the pilot symbols are not needed. Moreover, to attain the estimates of the Channel State Information (CSI) in the uplink, the sparsity exhibited by the MIMO channels is exploited by incorporating CS based Orthogonal Matching Pursuit (OMP) algorithm. For decoding the transmitted information symbols of each user, a Linear Minimum Mean Square Error (LMMSE) based equalizer is incorporated at the receiving Base Station (BS). Based on the obtained simulation results...
Pilot reduction techniques for sparse channel estimation in massive MIMO systems
2018 14th Annual Conference on Wireless On-demand Network Systems and Services (WONS), 2018
The current high gain frequency division duplex (FDD) Massive multiple-input, multiple-output (MIMO) systems pose several challenges to carry out the downlink beamforming. Specifically, downlink beamforming requires a channel estimation that usually needs long training and feedback overhead, scaling with the number of antennas at the base station (BS). We exploit compressive sensing (CS) techniques to accurately estimate the channel, while assuring overhead reduction which is proportional to the sparsity level of the channel. The sparse virtual channel representation is obtained through the proposed dictionary design, which is more flexible, robust and able to estimate the cell characteristics. We specifically focus on massive MIMO-Orthogonal Frequency-Division Multiplexing (OFDM) systems that show more robustness to multipath fading, and analyze several CS algorithms to select among them the best technique with the proposed dictionary design. Numerical results demonstrate that gree...
Compressive sensing based channel estimation for massive MIMO systems with planar arrays
2015
Filter-bank multicarrier with offset quadrature amplitude modulation (FBMC-OQAM) has been considered as an alternative scheme to orthogonal frequency division multiplexing (OFDM). However, the traditional channel estimation techniques of the OFDM cannot be directly applied to FBMC-OQAM system because of the real-field orthogonality of FBMC-OQAM signals. Although traditional channel estimation techniques, such as least square (LS) and minimum mean square error (MMSE) are widely applied to FBMC-OQAM system via canceling the intrinsic imaginary interference from adjacent data symbols, the LS algorithm is subject to noise enhancement and it results in large mean square error (MSE), while the MMSE algorithm needs to know the statistical information of channel in advance. Due to sparsity of the wireless channel, channel estimation is investigated as a compressive sensing (CS) problem. In this paper, we firstly introduce auxiliary pilot and coding methods to cancel intrinsic imaginary interference for FBMC-OQAM system. Then, a novel sparse adaptive subspace pursuit (SASP) method is proposed to improve the accuracy of LS channel estimation. Finally, we develop two different algorithms, namely auxiliary pilot (AP)-SASP and coding-SASP to estimate channel frequency respond (CFR) in FBMC-OQAM system. Simulation results show that the AP-SASP and coding-SASP algorithms can offer a lower complexity and less measurement than conventional orthogonal matching pursuit (OMP) and regularized orthogonal matching pursuit (ROMP) algorithms. Moreover, the proposed AP-SASP and coding-SASP algorithms have a better bit error ration (BER) performance than the conventional OMP and ROMP methods for FBMC system in doubly selective channels. Meanwhile, the proposed AP-SASP and coding-SASP algorithms have the same BER performance as CoSaMP, subspace pursuit (SP) algorithms while the complexity of proposed algorithms is 10.41% less than that of the CoSaMP and SP algorithms. INDEX TERMS FBMC-OQAM, channel estimation, compressive sensing (CS), coding-SASP, doubly selective channels.
Estimation of Very Large MIMO Channels Using Compressed Sensing
SBrT 2013, 2013
In this paper, we propose an efficient pilot-assisted technique for the estimation of very-large MIMO (multiple-input multiple-output) channels exploiting the inherent sparsity of the channel. We first obtain an appropriate sparse decomposition model from a virtual channel representation of the very-large MIMO channel. Based on this model, we capitalize on a fundamental result of the compressed sensing (CS) to show that the channel matrix can be accurately estimated from very short training sequences compared to the number of used transmit antennas. We compare the normalized mean square error (NMSE) obtained using the proposed CS-based channel estimator, the least-square (LS) estimator and the Cramer-Rao lower bound (CRLB). The simulation results show that the proposed estimator obtains good performance, being 5 dB from the CRLB.
Hierarchical Sparse Channel Estimation for Massive MIMO
2018
The problem of wideband massive MIMO channel estimation is considered. Targeting for low complexity algorithms as well as small training overhead, a compressive sensing (CS) approach is pursued. Unfortunately, due to the Kronecker-type sensing (measurement) matrix corresponding to this setup, application of standard CS algorithms and analysis methodology does not apply. By recognizing that the channel possesses a special structure, termed hierarchical sparsity, we propose an efficient algorithm that explicitly takes into account this property. In addition, by extending the standard CS analysis methodology to hierarchical sparse vectors, we provide a rigorous analysis of the algorithm performance in terms of estimation error as well as number of pilot subcarriers required to achieve it. Small training overhead, in turn, means higher number of supported users in a cell and potentially improved pilot decontamination. We believe, that this is the first paper that draws a rigorous connec...
Efficient Coordinated Recovery of Sparse Channels in Massive MIMO
IEEE Transactions on Signal Processing, 2015
This paper addresses the problem of estimating sparse channels in massive MIMO-OFDM systems. Most wireless channels are sparse in nature with large delay spread. In addition, these channels as observed by multiple antennas in a neighborhood have approximately common support. The sparsity and common support properties are attractive when it comes to the efficient estimation of large number of channels in massive MIMO systems. Moreover, to avoid pilot contamination and to achieve better spectral efficiency, it is important to use a small number of pilots. We present a novel channel estimation approach which utilizes the sparsity and common support properties to estimate sparse channels and require a small number of pilots. Two algorithms based on this approach have been developed which perform Bayesian estimates of sparse channels even when the prior is non-Gaussian or unknown. Neighboring antennas share among each other their beliefs about the locations of active channel taps to perform estimation. The coordinated approach improves channel estimates and also reduces the required number of pilots. Further improvement is achieved by the data-aided version of the algorithm. Extensive simulation results are provided to demonstrate the performance of the proposed algorithms.
Sparse Channel Estimation for MIMO-OFDM Systems using Compressed Sensing
IEEE International Conference On Recent Trends In Electronics Information Communication Technology, May 20-21, 2016, India , 2016
One of the major challenge for practical Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM)system is the accurate channel estimation which is veryessential to guarantee the system performance. In this paper,theSubspace Pursuit (SP), Orthogonal Matching Pursuit (OMP) and Compressed Sampling Matching Pursuit(CoSaMP) techniques combined with Minimum Mean Square Error(MMSE) and Least Mean Square(LMS)tools are used to estimate the channel coefficients for MIMO-OFDM system.These algorithms are used for the channel estimation in MIMO-OFDM system to develop the joint sparsity of the MIMO channel. Simulation results shows that SP, OMP and CoSaMP techniques combined with MMSE and LMS tools provides significant reduction in Normalized Mean Square Error (NMSE) vs Signal to Noise Ratio (SNR) when compared to SP,OMP and CoSaMP technique with Least Square (LS) tool and also the conventional channel estimation methods such as LS, MMSE and LMS. Moreover CoSaMP combined with LMS tool performs better than SP and OMP techniques with LMS tool with less computational timecomplexity.
Millimeter Wave MIMO channel estimation based on adaptive compressed sensing
2017 IEEE International Conference on Communications Workshops (ICC Workshops), 2017
Multiple-input multiple-output (MIMO) systems are well suited for millimeter-wave (mmWave) wireless communications where large antenna arrays can be integrated in small form factors due to tiny wavelengths, thereby providing high array gains while supporting spatial multiplexing, beamforming, or antenna diversity. It has been shown that mmWave channels exhibit sparsity due to the limited number of dominant propagation paths, thus compressed sensing techniques can be leveraged to conduct channel estimation at mmWave frequencies. This paper presents a novel approach of constructing beamforming dictionary matrices for sparse channel estimation using the continuous basis pursuit (CBP) concept, and proposes two novel low-complexity algorithms to exploit channel sparsity for adaptively estimating multipath channel parameters in mmWave channels. We verify the performance of the proposed CBP-based beamforming dictionary and the two algorithms using a simulator built upon a three-dimensional mmWave statistical spatial channel model, NYUSIM, that is based on real-world propagation measurements. Simulation results show that the CBPbased dictionary offers substantially higher estimation accuracy and greater spectral efficiency than the grid-based counterpart introduced by previous researchers, and the algorithms proposed here render better performance but require less computational effort compared with existing algorithms.