Low-complexity half-sparse decomposition-based detection for massive MIMO transmission

Hajji, Zahran; Amis, Karine; Bey, Abdeldjalil Aïssa El; Abdelkefi, Fatma

doi:10.1109/comnet.2015.7566636

Cited by 5 publications

(12 citation statements)

References 11 publications

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“…Like (P SA,1 ), the complexity of (P SA,2 ) highly depends on the constellation size. The same decomposition as used in (P HSA,1 ) can be applied to obtain the reduced-complexity problem (P HSA,2 ) which reads [24] (P HSA,2 ) : arg min…”

Section: Noisy Massive Mimo Systemsmentioning

confidence: 99%

“…(P HSA,1 ) is less complex than (P SA,1 ) while achieving the same successful recovery probability. It reduces by about 2 log 2 (p) p 2 the computation cost [24].…”

mentioning

confidence: 99%

“…This makes it less interesting for higher sizes. To address the complexity issue, the authors proposed another sparse decomposition which is done in two steps [24]. The first is a binary decomposition as proposed 8 in [25] which transforms the elements of a vector in F into a size-4N log 2 (p) vector of binary elements {−1, 1}.…”

mentioning

confidence: 99%

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Simplicity-based recovery of finite-alphabet signals for large-scale MIMO systems

Hajji¹,

Bey²,

Amis³

2018

Digital Signal Processing

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In this paper, we consider the problem of finite-alphabet source separation in both determined and underdetermined large-scale systems. First, we address the noiseless case and we propose a linear criterion based on 1 -minimization combined with box constraints. We investigate also the system conditions that ensure successful recovery. Next, we apply the approach to the noisy massive MIMO transmission and we propose a quadratic criterion-based detector. Simulation results show the efficiency of the proposed detection methods for various QAM modulations and MIMO configurations. We mention that there is no change in the computational complexity when the constellation size increases. Moreover, the proposed method outperforms the classical Minimum Mean Square Error (MMSE)-based detection algorithms.

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Section: Noisy Massive Mimo Systemsmentioning

confidence: 99%

“…(P HSA,1 ) is less complex than (P SA,1 ) while achieving the same successful recovery probability. It reduces by about 2 log 2 (p) p 2 the computation cost [24].…”

mentioning

confidence: 99%

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Simplicity-based recovery of finite-alphabet signals for large-scale MIMO systems

Hajji¹,

Bey²,

Amis³

2018

Digital Signal Processing

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“…Contrary to IW-SOAV and IW-SCSR, detection schemes presented in [7]- [9] are non-iterative, in the sense that, they only require the solution of one convex optimization problem. Detectors in [7], [8] are based on a convex relaxation of an exact reformulation of the ML criterion obtained by adopting the idea of Finite-Alphabet Sparse (FAS) detection introduced in [11]. Similarly, the authors in [9], have used the simplicity principle, in CS, to propose another convex criterion for detecting signals in large scale MIMO systems.…”

Section: Introductionmentioning

confidence: 99%

“…In order to take more advantage of the ML reformulation presented in [7], [8], we propose in this work, a new largescale SP-MIMO receiver based on a non-convex criterion. More precisely, we formulate the detection problem as a (smooth) Difference-of-Convex (DC) programming [12] problem.…”

Section: Introductionmentioning

confidence: 99%

Large-Scale MIMO Receiver Based on Finite-Alphabet Sparse Detection and Concave-Convex Optimization

Meslem

Bey

Djeddou

2020

2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

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In this paper, we propose a new receiver for detecting signals in large-scale Spatially Multiplexed (SP) Multiple-Input-Multiple-Output (MIMO) systems that may have fewer receive antennas than transmitted symbols (overloaded case). Relying on the idea of Finite-Alphabet Sparse (FAS) detection, we formulate the Maximum Likelihood (ML) criterion as a Difference-of-Convex (DC) programming problem that can be simply and efficiently solved using the Concave-Convex Procedure (CCP) technique. Since, the considered problem is nonconvex, we theoretically discuss the behavior of the derived algorithm. Numerical experiments confirm the superiority of the proposed detection scheme, when compared with recent detection methods based on convex optimization, in a variety of large-scale MIMO transmission scenarios including the overloaded case.

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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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Low-complexity half-sparse decomposition-based detection for massive MIMO transmission

Cited by 5 publications

References 11 publications

Simplicity-based recovery of finite-alphabet signals for large-scale MIMO systems

Simplicity-based recovery of finite-alphabet signals for large-scale MIMO systems

Large-Scale MIMO Receiver Based on Finite-Alphabet Sparse Detection and Concave-Convex Optimization

Low Complexity Linear Detectors for Massive MIMO: A Comparative Study

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