Performance Analysis of Compressive Sensing based LS and MMSE Channel Estimation Algorithm

Munshi, Ami; Unnikrishnan, Srija

doi:10.24138/jcomss.v17i1.1084

Cited by 11 publications

(7 citation statements)

References 28 publications

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“…This is the minimum mean square error (MMSE) algorithm [ 25 , 26 ]. However, the matrix inversion requires a large amount of computation and continuous recalculation, which greatly occupies computational resources and has poor real-time performance.…”

Section: Proposed Channel Estimation Schemementioning

confidence: 99%

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Channel Estimation for RIS-Assisted MIMO Systems in Millimeter Wave Communications

Liu

Deng

Peng

2023

Sensors

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The large number of estimated parameters in a reconfigurable intelligent surface (RIS) makes it difficult to achieve accurate channel estimation accuracy in 6G. Therefore, we suggest a novel two-phase channel estimation framework for uplink multiuser communication. In this context, we propose an orthogonal matching pursuit (OMP)-based linear minimum mean square error (LMMSE) channel estimation approach. The OMP algorithm is used in the proposed algorithm to update the support set and pick the columns of the sensing matrix that are most correlated with the residual signal, which successfully reduces pilot overhead by removing redundancy. Here, we use LMMSE’s advantages for handling noise to address the problem of inadequate channel estimation accuracy when the signal-to-noise ratio (SNR) is low. Simulation findings demonstrate that the proposed approach outperforms least-squares (LS), traditional OMP, and other OMP-based algorithms in terms of estimate accuracy.

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Section: Proposed Channel Estimation Schemementioning

confidence: 99%

“… Performance evaluation of the NMSE for the BS-UE channels (compared with LS [ 25 ], conventional OMP algorithm [ 14 ], and improved OMP algorithm [ 26 ]). …”

Section: Figurementioning

confidence: 99%

Channel Estimation for RIS-Assisted MIMO Systems in Millimeter Wave Communications

Liu

Deng

Peng

2023

Sensors

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“…In the original DDFTN, the calculation of the estimated symbol depends on the tap coefficients of the postfilter. The least-squares (LS) channel estimation process was added after the postfilter in prior studies and was shown Photonics 2023, 10, 1222 2 of 10 to perform better [14][15][16]. The MLSE is superior in solving pattern-dependent ISI.…”

Section: Introductionmentioning

confidence: 99%

Look-Up-Table-Based Direct-Detection-Faster-Than-Nyquist-Algorithm-Enabled IM/DD Transmission with Severe Bandwidth Limitation

Zhang,

Huo,

Liu

et al. 2023

Photonics

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The emergence of new applications is driving a dramatic growth in the capacity of data center interconnects. Intensity modulation and direct detection (IM/DD) has the characteristics of low cost, low power consumption and a small footprint. Industry and academia have conducted much research on IM/DD systems as a cost-effective solution. However, optical/electronic bandwidth and fiber dispersion are the restricting factors for the improvement of transmission capacity. Pattern-dependent distortion is an important aspect that affects system performance. In this paper, we propose a look-up table (LUT)-based direct-detection-faster-than-Nyquist (DDFTN) algorithm to compensate for pattern-dependent distortion. The performances of feedforward-equalization (FFE) only, the original DDFTN, least-squares (LS)-based DDFTN, and LUT-based DDFTN algorithms in IM/DD-based 112/140 Gbit/s four-level pulse-amplitude modulation (PAM-4) signal transmission were evaluated. The experimental results indicate that LUT-based DDFTN performs better with low computational complexity.

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“…A compressed sensing (CS) algorithm based MMSE channel estimation is proposed in [21]. This method offers similar performance to that of LMMSE, with much lower computational complexity.…”

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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Performance Analysis of Compressive Sensing based LS and MMSE Channel Estimation Algorithm

Cited by 11 publications

References 28 publications

Channel Estimation for RIS-Assisted MIMO Systems in Millimeter Wave Communications

Channel Estimation for RIS-Assisted MIMO Systems in Millimeter Wave Communications

Look-Up-Table-Based Direct-Detection-Faster-Than-Nyquist-Algorithm-Enabled IM/DD Transmission with Severe Bandwidth Limitation

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

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