Compressive sensing-based channel estimation for massive multiuser MIMO systems

Nguyen, Sinh Le Hong; Ghrayeb, Ali

doi:10.1109/wcnc.2013.6555020

Cited by 154 publications

(105 citation statements)

References 15 publications

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“…Currently, pilot contamination reduction schemes in massive MIMO systems can be classified into three groups: channel estimation [6][7][8][9][10][11], precoding [12], and pilot scheduling [13][14][15][16][17][18][19][20]. In [6], pilot contamination was tackled by a multi-cell precoding method based on the minimum mean square error channel estimation.…”

Section: Introductionmentioning

confidence: 99%

“…The sparse Bayesian learning was employed to estimate not only the CSI but also the interference from neighboring cells [10]. A compressive sensing-based low-rank approximation scheme for channel estimation was proposed in [11], which exploits the fact that the degree of freedom of physical channels is much smaller than the number of independent parameters. Specifically, this manuscript focuses on the pilot scheduling because in essence, pilot contamination is incurred by the assignment of the same pilot sequences to users in neighboring cells [13].…”

Section: Introductionmentioning

confidence: 99%

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Pilot contamination reduction in massive MIMO systems based on pilot scheduling

Liu

Cao

et al. 2018

J Wireless Com Network

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The pilot contamination caused by sharing the non-orthogonal pilots among users is considered to be a bottleneck of the massive multi-input multi-output (MIMO) systems. This paper proposes a pilot scheduling scheme based on the degradation to address this problem. Through computing the degradation of the users, the proposed scheduling assigns the optimal pilot sequence to the user who suffers from the greatest degradation in a greedy way. Moreover, the proposed scheme is further optimized with an extra set of orthogonal pilot sequences, which is called pilot scheduling scheme based on user grouping. Simulation results show that the target cell's achievable sum rate of the proposed scheme is much higher than the random pilot scheduling (RPS) and the smart pilot assignment (SPA) schemes; also, our scheme can reduce the impact of shadowing fading on the target cell's achievable sum rate effectively.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Pilot contamination reduction in massive MIMO systems based on pilot scheduling

Liu

Cao

et al. 2018

J Wireless Com Network

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“…In order to estimate CIR in massive-MIMO systems, linear minimum mean-squared error (LMMSE) estimation techniques are usually used. Several CS based techniques have also been proposed to estimate the CIR in massive-MIMO (see e.g., [39,40]). In [41], authors have proposed a low complexity polynomial channel estimation based on Bayesian channel estimators to estimate CIR in massive-MIMO.…”

Section: Introductionmentioning

confidence: 99%

Massive-MIMO Sparse Uplink Channel Estimation Using Implicit Training and Compressed Sensing

Mansoor¹,

Nawaz²,

Gulfam³

2017

Applied Sciences

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Massive multiple-input multiple-output (massive-MIMO) is foreseen as a potential technology for future 5G cellular communication networks due to its substantial benefits in terms of increased spectral and energy efficiency. These advantages of massive-MIMO are a consequence of equipping the base station (BS) with quite a large number of antenna elements, thus resulting in an aggressive spatial multiplexing. In order to effectively reap the benefits of massive-MIMO, an adequate estimate of the channel impulse response (CIR) between each transmit-receive link is of utmost importance. It has been established in the literature that certain specific multipath propagation environments lead to a sparse structured CIR in spatial and/or delay domains. In this paper, implicit training and compressed sensing based CIR estimation techniques are proposed for the case of massive-MIMO sparse uplink channels. In the proposed superimposed training (SiT) based techniques, a periodic and low power training sequence is superimposed (arithmetically added) over the information sequence, thus avoiding any dedicated time/frequency slots for the training sequence. For the estimation of such massive-MIMO sparse uplink channels, two greedy pursuits based compressed sensing approaches are proposed, viz: SiT based stage-wise orthogonal matching pursuit (SiT-StOMP) and gradient pursuit (SiT-GP). In order to demonstrate the validity of proposed techniques, a performance comparison in terms of normalized mean square error (NCMSE) and bit error rate (BER) is performed with a notable SiT based least squares (SiT-LS) channel estimation technique. The effect of channels' sparsity, training-to-information power ratio (TIR) and signal-to-noise ratio (SNR) on BER and NCMSE performance of proposed schemes is thoroughly studied. For a simulation scenario of: 4 × 64 massive-MIMO with a channel sparsity level of 80% and signal-to-noise ratio (SNR) of 10 dB, a performance gain of 18 dB and 13 dB in terms of NCMSE over SiT-LS is observed for the proposed SiT-StOMP and SiT-GP techniques, respectively. Moreover, a performance gain of about 3 dB and 2.5 dB in SNR is achieved by the proposed SiT-StOMP and SiT-GP, respectively, for a BER of 10 −2 , as compared to SiT-LS. This performance gain NCME and BER is observed to further increase with an increase in channels' sparsity.

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“…The reliable high-speed broadband wireless links are expected to be on a huge development prospective due to the foreseen rapidly increases in the number of users, amount of data traffic and number of applications [1]. To meet these demands, it is expected that future communication systems, i.e., beyond 4G 5 or 5G systems, will reach a faster data rate at gigabit-scale over the next few years.…”

Section: Introductionmentioning

confidence: 99%

“…However, neither of these algorithms can attain accurate estimation performance and low complexity at the same time. More recently, one quadratic semi-definite programming (SDP) method has been discussed in [1], where the author demonstrated that SDP solver can be stable and provide accurate channel estimation, as long as 50 the degree of freedom (DoF) of the channel matrix is much smaller than the size of channel matrix (i.e., total number of elements in the channel matrix). However, experimental results have shown that the convex optimization solver runs slowly in the large-scale applications since it requires explicit operations on the large matrix [13].…”

Section: Introductionmentioning

confidence: 99%

Joint channel estimation algorithm via weighted Homotopy for massive MIMO OFDM system

Nan

Sun

Zhang

2016

Digital Signal Processing

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Massive (or large-scale) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system is widely acknowledged as a key technology for future communication. One main challenge to implement this system in practice is the high dimensional channel estimation, where the large number of channel matrix entries requires prohibitively high computational complexity. To solve this problem efficiently, a channel estimation approach using few number of pilots is necessary. In this paper, we propose a weighted Homotopy based channel estimation approach which utilizes the sparse nature in MIMO channels to achieve a decent channel estimation performance with much less pilot overhead. Moreover, inspired by the fact that MIMO channels are observed to have approximately common support in a neighborhood, an information exchange strategy based on the proposed approach is developed to further improve the estimation accuracy and reduce the required number of pilots through joint channel estimation. Compared with the traditional sparse channel estimation methods, the proposed approach can achieve more than 2dB gain in terms of mean square error (MSE) with the same number of pilots, or * Corresponding author

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Compressive sensing-based channel estimation for massive multiuser MIMO systems

Cited by 154 publications

References 15 publications

Pilot contamination reduction in massive MIMO systems based on pilot scheduling

Pilot contamination reduction in massive MIMO systems based on pilot scheduling

Massive-MIMO Sparse Uplink Channel Estimation Using Implicit Training and Compressed Sensing

Joint channel estimation algorithm via weighted Homotopy for massive MIMO OFDM system

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