An iterative detector based on sparse bayesian error recovery for uplink large-scale MIMO systems

Amiri, Mohammad Mehdi; Akhavan, Amir Naser

doi:10.1016/j.aeue.2021.153848

Cited by 4 publications

(3 citation statements)

References 35 publications

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“…7 A plethora of M-MIMO detection techniques are covered in the literature, including linear, non-linear, local search, box detection, belief propagation, machine learning, and sparsity based detectors. [8][9][10][11][12][13][14][15][16] Optimal detectors like Maximum Aposteriori (MAP) and Maximal Likelihood (ML) detectors suffer from exponential computational complexity due to large antenna configurations and modulation sizes and, hence, are not feasible. 17 The belief propagation (BP) algorithm, a tree-based algorithm, can also give performance close to that of ML with low channel correlation.…”

Section: Introductionmentioning

confidence: 99%

Performance analysis and comparison of approximate detectors with linear detectors in massive MIMO systems under imperfect channel state information

Khurshid

Imran

Wakeel

2023

Trans Emerging Tel Tech

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SummaryMassive MIMO systems are a key technology for enhancing the capacity and reliability of wireless communication systems. However, to unlock the full potential of massive MIMO systems, accurate channel state information is crucial at the receivers. In practice, channel estimation errors are inevitable, leading to significant performance degradation in traditional massive MIMO equalizers that assume perfect channel state information. In this work, we investigate the performance of approximate massive MIMO detectors, specifically the Gauss‐Seidel and Neumann series, over a time‐invariant channel with imperfect channel state information. We derive the expressions of signal‐to‐interference‐plus‐noise ratio and average bit error rate for approximate massive MIMO detectors in a unicellular environment using M‐ary quadrature amplitude modulation. Additionally, we compare the performance of linear detectors, including zero‐forcing, minimum‐mean‐squared‐error, and maximal‐ratio combining, with that of the approximate detectors. Through extensive Monte Carlo numerical simulations and various system configurations, we demonstrate that the Gauss‐Seidel detector, under imperfect channel state information, achieves comparable bit error rate performance to the zero‐forcing and minimum‐mean‐squared‐error detectors while requiring less computational complexity. Our study suggests that the Gauss‐Seidel detector has the potential to be used in massive MIMO systems in the presence of imperfect channel state information, highlighting its practical relevance in real‐world scenarios.

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Section: Introductionmentioning

confidence: 99%

Performance analysis and comparison of approximate detectors with linear detectors in massive MIMO systems under imperfect channel state information

Khurshid

Imran

Wakeel

2023

Trans Emerging Tel Tech

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“…Recently, compressive sensing (CS) and sparse signal recovery techniques have received much attention in different signal processing applications. Compressive sensing has emerged as a promising approach for use in large MIMO systems [12,13]. It is noteworthy that the original signals in massive MIMO systems are not intrinsically sparse, but it is expected that the detector output contains an error only for a few number of users.…”

Section: -Introductionmentioning

confidence: 99%

“…The motivation of this paper is to improve the performance of the detector by using the sparsity in the residual error of large MIMO systems. In order to exploit the sparsity of the detection errors, the conventional model is converted into a sparse model via the symbol error vector [13,14]. After that, the error recovery algorithm can be performed to improve the detection performance by recovering the non-zero entries of the error vector.…”

Section: -Introductionmentioning

confidence: 99%

A Novel Detector based on Compressive Sensing for Uplink Massive MIMO Systems

Amiri¹,

Akhavan²

2022

jist

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Massive multiple-input multiple-output is a promising technology in future communication networks where a large number of antennas are used. It provides huge advantages to the future communication systems in data rate, the quality of services, energy efficiency, and spectral efficiency. Linear detection algorithms can achieve a near-optimal performance in largescale MIMO systems, due to the asymptotic orthogonal channel property. But, the performance of linear MIMO detectors degrades when the number of transmit antennas is close to the number of receive antennas (loaded scenario). Therefore, this paper proposes a series of detectors for large MIMO systems, which is capable of achieving promising performance in loaded scenarios. The main idea is to improve the performance of the detector by finding the hidden sparsity in the residual error of the received signal. At the first step, the conventional MIMO model is converted into the sparse model via the symbol error vector obtained from a linear detector. With the aid of the compressive sensing methods, the incorrectly detected symbols are recovered and performance improvement in the detector output is obtained. Different sparse recovery algorithms have been considered to reconstruct the sparse error signal. This study reveals that error recovery by imposing sparse constraint would decrease the bit error rate of the MIMO detector. Simulation results show that the iteratively reweighted least squares method achieves the best performance among other sparse recovery methods.

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