A Low-Complexity Data Detection Algorithm for Massive MIMO Systems

Khoso, Imran A.; Dai, Xiaoming; Irshad, Muhammad Nauman; Khan, Asif; Wang, Xiyuan

doi:10.1109/access.2019.2907366

Cited by 20 publications

(14 citation statements)

References 38 publications

(59 reference statements)

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“…By taking advantage of the M-MIMO properties, some algorithms to reduce the complexity of linear detectors are proposed in [10] and [11]. In particular, in [10] is proposed a low-complexity MMSE detector algorithm based 1 The performance is similar to the Maximum-Likelihood detector on the Damped Jacobi method to determine the optimum and quasi-optimum damped parameter by exploiting the massive MIMO channel property of asymptotic orthogonality.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, in [10] is proposed a low-complexity MMSE detector algorithm based 1 The performance is similar to the Maximum-Likelihood detector on the Damped Jacobi method to determine the optimum and quasi-optimum damped parameter by exploiting the massive MIMO channel property of asymptotic orthogonality. Moreover, in [11], the authors propose a novel computationally efficient data detection algorithm based on the modified Richardson method that outperforms the existing methods and achieves near-MMSE performance with a significantly reduced computational complexity.…”

Section: Introductionmentioning

confidence: 99%

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BER Evaluation of Linear Detectors in Massive MIMO Systems Under Imperfect Channel Estimation Effects

et al. 2019

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New perspectives for wireless communications have brought new techniques, such as a very large number of antennas at a base station (BS) serving multiple user terminals (UTs) with a single antenna each, known as massive MIMO (M-MIMO). M-MIMO linear detectors, such as maximal-ratio combining (MRC), zero-forcing (ZF) or minimum-mean-square error (MMSE) can achieve excellent performance with low complexity due to the channel hardening property. However, imperfect channel estimation produces a penalty in the performance. An average bit error rate (BER) performance analysis over time-invariant channel is presented for M-MIMO systems under imperfect channel estimation in contrast with most of M-MIMO literature that uses the ergodic capacity approach. Closed-form expressions and bounds to evaluate the average BER are derived for MRC, ZF and MMSE detectors in a unicellular environment considering M -QAM modulation. Furthermore, an expression to evaluate the normalized signal-to-noise ratio (E b /N 0 ) penalty due to the imperfect channel estimation is presented. Montecarlo numerical simulations are used to verify the tightness of the derived equations which are a function of the number of BS antennas, number of users, coherence time interval, number of pilot symbols and the E b /N 0 of pilot and data symbols used for channel estimation and data detection.INDEX TERMS BER, imperfect channel estimation, massive MIMO, maximal-ratio combining, minimummean-square error, zero-forcing.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

BER Evaluation of Linear Detectors in Massive MIMO Systems Under Imperfect Channel Estimation Effects

et al. 2019

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“…It is also implemented in Xilinx Virtex-7 FPGA for a

in [ 63 ]. However, it suffers from low parallelism and considerable correlation issues [ 64 ].…”

Section: Matrix Inversion Methodsmentioning

confidence: 99%

“…In the RI method, symmetric matrices are used and defined as positive at their execution. Similar to the SOR method, it is overly sensitive to a relaxation parameter (

) to achieve faster convergence where

and

is the largest eigenvalue of H [ 64 ]. The signal is estimated as

In a Richardson-based detector, the initial solution

can be set as a zero vector without loss of generality as no prior knowledge of the final solution is available [ 65 ].…”

Section: Matrix Inversion Methodsmentioning

confidence: 99%

A Low Complexity Near-Optimal Iterative Linear Detector for Massive MIMO in Realistic Radio Channels of 5G Communication Systems

Albreem

Alsharif

Kim

2020

Entropy

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Massive multiple-input multiple-output (M-MIMO) is a substantial pillar in fifth generation (5G) mobile communication systems. Although the maximum likelihood (ML) detector attains the optimum performance, it has an exponential complexity. Linear detectors are one of the substitutions and they are comparatively simple to implement. Unfortunately, they sustain a considerable performance loss in high loaded systems. They also include a matrix inversion which is not hardware-friendly. In addition, if the channel matrix is singular or nearly singular, the system will be classified as an ill-conditioned and hence, the signal cannot be equalized. To defeat the inherent noise enhancement, iterative matrix inversion methods are used in the detectors’ design where approximate matrix inversion is replacing the exact computation. In this paper, we study a linear detector based on iterative matrix inversion methods in realistic radio channels called QUAsi Deterministic RadIo channel GenerAtor (QuaDRiGa) package. Numerical results illustrate that the conjugate-gradient (CG) method is numerically robust and obtains the best performance with lowest number of multiplications. In the QuaDRiGA environment, iterative methods crave large n to obtain a pleasurable performance. This paper also shows that when the ratio between the user antennas and base station (BS) antennas ( β ) is close to 1, iterative matrix inversion methods are not attaining a good detector’s performance.

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“…The positive semi-definite property of regularized G is exploited to perform iterative iterations where the signal can be detected accordingly. The convergence rate is very sensitive to a selection of relaxation parameter (ω) where 0 < ω ≤ 2 λ and λ is the largest eigenvalue of the symmetric positive definite matrix H [45]. The estimated signal is obtained as…”

Section: F Richardsonmentioning

confidence: 99%

Low Complexity Linear Detectors for Massive MIMO: A Comparative Study

et al. 2021

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Massive multiple-input multiple-output (M-MIMO) is a significant pillar in fifth generation (5G) networks where a large number of antennas is deployed. It provides massive advantages to modern communication systems in data rate, spectral efficiency, number of users serviced simultaneously, energy efficiency, and quality of service (QoS). However, it requires advanced signal processing for data detection. The growing MIMO size leads to complicated scenarios, which makes the detector design a knotty problem. The problem is also becoming more complicated when high-order modulation schemes are exploited and more users are multiplexed. Therefore, it is not practical to employ the maximum likelihood (ML) detector despite the excellent performance. Linear detectors are alternative solutions and relatively simple. Unfortunately, they still need an exact matrix inversion computation, which bears to a significant high complexity. Therefore, several iterative methods are utilized to approximate or evade the matrix inversion rather than computing it. This paper studies the pros and cons of iterative matrix inversion methods where the number of computations and bit-error-rate (BER) are considered to compare between the methods. The comparison is conducted in several scenarios such as different ratio between the number of base station (BS) antennas and user terminal (UT) antennas (β), the number of iterations (n), and the relaxation parameter (ω). This paper also studies the impact of ω in the performance of Richardson (RI) and the successive over-relaxation (SOR) methods. Numerical results show that the conjugate gradient (CG) and optimized coordinate descent (OCD) methods exhibit the lowest complexity with an acceptable performance. In addition, the Gauss-Seidel (GS) method outperforms all other detectors with a trivial complexity increment. It is also noticed that the performance is not improved with every iteration. It is also shown that ω has a great impact and a significant role in achieving a satisfactory performance in both RI and SOR based detectors. From implementation point of view, detectors based on RI, OCD, and CG methods have achieved the highest hardware efficiency (HE) while Jacobi (JA) based detector has obtained the lowest HE. Recent research advances of detection methods are also presented in the open research direction with a potential impact of linear detection methods in initialization and pre-processing.

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A Low-Complexity Data Detection Algorithm for Massive MIMO Systems

Cited by 20 publications

References 38 publications

BER Evaluation of Linear Detectors in Massive MIMO Systems Under Imperfect Channel Estimation Effects

BER Evaluation of Linear Detectors in Massive MIMO Systems Under Imperfect Channel Estimation Effects

A Low Complexity Near-Optimal Iterative Linear Detector for Massive MIMO in Realistic Radio Channels of 5G Communication Systems

Low Complexity Linear Detectors for Massive MIMO: A Comparative Study

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