Low-complexity approximations for LMMSE channel estimation in OFDM/OQAM

Savaux, Vincent; Louët, Yves; Bader, Faouzi

doi:10.1109/ict.2016.7500464

Cited by 8 publications

(3 citation statements)

References 12 publications

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“…The main complexity of the MMSE channel estimator in (11) comes from a large dimension of a matrix inversion, which is scaled with the number of BS antennas

M

[23]. Thus, the total number of multiplication operations required to compute the MMSE channel estimation matrix is

M^{3} τ^{3}

.…”

Section: Estimator Complexitymentioning

confidence: 99%

Low‐complexity channel estimation for time division duplex massive multi‐user multi‐input multi‐output systems

Hawej

Shayan

2020

IET Communications

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This paper addresses the problem of minimum mean square error channel estimator for time division duplex massive multi-user multi-input multi-output systems. It is noteworthy that, the minimum mean square error has been previously proposed for multi-cell massive multi-user multi-input multi-output channel estimation. However, the minimum mean square error estimator suffers from high computational complexity due to the large dimension of the covariance matrix inversion. In this study, low-complexity channel estimator for time division duplex massive multi-user multi-input multi-output networks is designed by using the low-rank matrix approximation techniques. The proposed estimator is referred to as an approximate minimum mean square error estimator. Furthermore, the computational complexity of the proposed approximate minimum mean square error estimator is analysed and compared to the minimum mean square error and least square estimators. The normalised mean square error and the uplink achievable sum-rate performance criteria are used to evaluate the performance of the proposed estimator. Finally, the simulation results show the effectiveness of the proposed estimator under two different scenarios: noise-limited and pilot contamination. These simulation results are compared to the conventional minimum mean square error and least square estimators.

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“…The main complexity of the MMSE channel estimator in (11) comes from a large dimension of a matrix inversion, which is scaled with the number of BS antennas

M

[23]. Thus, the total number of multiplication operations required to compute the MMSE channel estimation matrix is

M^{3} τ^{3}

.…”

Section: Estimator Complexitymentioning

confidence: 99%

Low‐complexity channel estimation for time division duplex massive multi‐user multi‐input multi‐output systems

Hawej

Shayan

2020

IET Communications

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“…To reduce the complexity of LMMSE estimation, the low rank approximation-based singular value decomposition (SVD) approach is proposed in [11]. Based on the SVD method [11], the authors proposed two efficient channel estimation methods for OFDM/OQAM systems in [12]. However, these SVD-based estimation methods are still characterized by high computational complexity, as decomposing the R HH matrix using the SVD method itself requires cubic complexity [13].…”

Section: Introductionmentioning

confidence: 99%

A DFT-based Low Complexity LMMSE Channel Estimation Technique for OFDM Systems

Patra¹,

Pradhan²,

Singh³

2022

JTIT

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The linear minimum mean square error (LMMSE) channel estimation technique is often employed in orthogonal frequency division multiplexing (OFDM) systems because of its optimal performance in the mean square error (MSE) performance. However, the LMMSE method requires cubic complexity of order O(N 3 p ), where Np is the number of pilot subcarriers. To reduce the computational complexity, a discrete Fourier transform (DFT) based LMMSE method is proposed in this paper for OFDM systems in the frequency selective channel. To validate the proposed method, the closed form mean square error (MSE) expression is also derived. Finally, a computer simulation is carried out to compare the performance of the proposed LMMSE method with the classical LS and LMMSE methods in terms of bit error rate (BER) and computational complexity. Results of the simulation show that the proposed LMMSE method achieves exactly the same performance as the conventional LMMSE method, with much lower computational complexity.

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“…A low rank approximation based on singular value decomposition (SVD) is proposed in [6]. Based on [6], the authors proposed two efficient channel estimation methods for orthogonal frequency division multiplexing/offset quadrature amplitude modulation (OFDM/OQAM) system in [7]. However these SVD based estimation methods require still high computational complexity as decomposing the R HH matrix using SVD method itself requires cubic complexity [8].…”

Section: Introductionmentioning

confidence: 99%