A Low-Complexity Soft-Output Signal Data Detection Algorithm for UL Massive MIMO Systems

Berra, Salah; Albreem, Mahmoud A.; Malek, M. Ikram; Dinis, Rui; Li, Xingwang; Rabie, Khaled M.

doi:10.1109/cits52676.2021.9618605

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…where ξ = N /K is the ratio between the number of antennas and users. Based on the analysis above, the optimal value of α opt , β opt , and ω opt can be expressed as [18]…”

Section: A the Aor And Sor Detectormentioning

confidence: 99%

“…The second category of detection techniques for massive MIMO is based on iterative methods, in which the transmitted vector can be approximated without the need for a matrix inverse. In general, the iterative method achieves a balance between complexity and performance than the approximation inversion matrix methods [5] Examples of these methods include Jacobi (JA) [15], Gauss-Seidel (GS) [16], successive over-relaxation (SOR) [17], accelerated over-relaxation (AOR) [18], Richardson iteration (RI) [19], and conjugate gradient (CG) [20] etc. The Jacobi method [15] is a linear detection technique that can estimate the transmitted vector without requiring computationally intensive matrix inversions.…”

Section: Introductionmentioning

confidence: 99%

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Deep Unfolding of Chebyshev Accelerated Iterative method for Massive MIMO Detection

Berra,

Chakraborty,

Dinis

et al. 2023

IEEE Access

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“…where ξ = N /K is the ratio between the number of antennas and users. Based on the analysis above, the optimal value of α opt , β opt , and ω opt can be expressed as [18]…”

Section: A the Aor And Sor Detectormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Unfolding of Chebyshev Accelerated Iterative method for Massive MIMO Detection

Berra,

Chakraborty,

Dinis

et al. 2023

IEEE Access

Self Cite

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show abstract

“…Recently, many enabling technologies such as massive MIMO [14] have been introduced to support data rates beyond 5G (B5G) and sixth generation (6G) networks [4]. In mMIMO base station (BS) is equipped with a huge number of antennas, much larger than the number of serviced users [15,16]. mMIMO increases network capacity and data throughput by splitting data packets over multiple signal paths as well as allowing simultaneous multiple users by using multi-user MIMO (MU-MIMO).…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Massive MIMO-OFDM System Incorporated with Various Transforms for Image Communication in 5G Systems

et al. 2022

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Modern-day applications of fifth-generation (5G) and sixth-generation (6G) systems require fast, efficient, and robust transmission of multimedia information over wireless communication medium for both mobile and fixed users. The hybrid amalgamation of massive multiple input multiple output (mMIMO) and orthogonal frequency division multiplexing (OFDM) proves to be an impressive methodology for fulfilling the needs of 5G and 6G users. In this paper, the performance of the hybrid combination of massive MIMO and OFDM schemes augmented with fast Fourier transform (FFT), fractional Fourier transform (FrFT) or discrete wavelet transform (DWT) is evaluated to study their potential for reliable image communication. The analysis is carried over the Rayleigh fading channels and M-ary phase-shift keying (M-PSK) modulation schemes. The parameters used in our analysis to assess the outcome of proposed versions of OFDM-mMIMO include signal-to-noise ratio (SNR) vs. peak signal-to-noise ratio (PSNR) and SNR vs. structural similarity index measure (SSIM) at the receiver. Our results indicate that massive MIMO systems incorporating FrFT and DWT can lead to higher PSNR and SSIM values for a given SNR and number of users, when compared with in contrast to FFT-based massive MIMO-OFDM systems under the same conditions.

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Closed-Form Bit Error Probability of ZF Detection for OFDM-M-MIMO Systems Using Effective Noise PDF

Chumchewkul

Tsimenidis

2022

IEEE Access

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This paper derives the closed-form bit error probability (BEP) of massive multiple-input, multiple-output (M-MIMO) systems using orthogonal frequency-division multiplexing (OFDM) and zeroforcing (ZF) detection. We improve the BEP accuracy by increasing the Neumann series expansion (NSE) to second order for the system that was previously analyzed in [1] employing a derived probability distribution function (PDF) of the effective noise. The proposed PDF is then utilized to evaluate the BEP, the PDF of output signal-to-noise ratio (SNR), and the outage probability as a function of the output SNR of the system. Furthermore, a simplified closed-form expression for the effective noise PDF, in terms of the Gaussian distribution, and the noise variance are firstly derived in this paper for simplifying the performance analysis. Monte-Carlo simulation results confirm that the outcome from the derived equation and the approximation closely matched those obtained by simulation. In addition, we employ the proposed noise variance to estimate the log-likelihood ratio (LLR) instead of the approximate noise variance for the low-complexity soft-output ZF detection. The computational complexity of the proposed detection is thus significantly reduced, whereas its bit error rate (BER) is lower than that of the classical detection. Focusing on a 10 × 200 Coded-OFDM-M-MIMO system, 97.81% of multiplications, required for producing the LLR from the estimated symbol, were minimized by utilizing the proposed detection. Therefore, the derived equations can be efficiently used for analyzing the performance of OFDM-M-MIMO systems, and reducing the computational complexity of the soft-output ZF detection.

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A Low-Complexity Soft-Output Signal Data Detection Algorithm for UL Massive MIMO Systems

Cited by 4 publications

References 17 publications

Deep Unfolding of Chebyshev Accelerated Iterative method for Massive MIMO Detection

Deep Unfolding of Chebyshev Accelerated Iterative method for Massive MIMO Detection

Performance Analysis of Massive MIMO-OFDM System Incorporated with Various Transforms for Image Communication in 5G Systems

Closed-Form Bit Error Probability of ZF Detection for OFDM-M-MIMO Systems Using Effective Noise PDF

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