Massive MIMO Detection Techniques: A Survey

Albreem, Mahmoud A.; Juntti, Markku; Shahabuddin, Shahriar

doi:10.1109/comst.2019.2935810

Cited by 341 publications

(281 citation statements)

References 239 publications

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“…The most straightforward method of computing LLR is to use the Kaczmarz algorithm to estimateŴ −1 t,k after each iteration. Combining Equations (2) and (11), an estimate of the transmit vector at each iteration can then be computed as:ŝ…”

Section: Exact Methodsmentioning

confidence: 99%

“…To the authors' best knowledge, the MMSE criterion-based low complexity signal detection algorithms can be basically categorized into three typical types, namely the approximate matrix inversion algorithms (AMIA), the iterative approaches for solving linear equations (IASLE), and the matrix gradient search methods (MGSM). Firstly, the AMIA algorithms deal with the matrix inversion operation, required by the MMSE signal detection, in an approximation manner where the Neumann series expansion and Newton iteration are evoked to estimate the matrix inversion approximately [11][12][13]. The approximation accuracy depends on the number of Neumann items or the Newton iterations and may result in a high complexity when the number of items or iterations is set large for achieving a satisfactory performance.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Low-Complexity Soft-Output Signal Detection Based on Improved Kaczmarz Iteration Algorithm for Uplink Massive MIMO System

Shen

Zhao

et al. 2020

Sensors

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For multi-user uplink massive multiple input multiple output (MIMO) systems, minimum mean square error (MMSE) criterion-based linear signal detection algorithm achieves nearly optimal performance, on condition that the number of antennas at the base station is asymptotically large. However, it involves prohibitively high complexity in matrix inversion when the number of users is getting large. A low-complexity soft-output signal detection algorithm based on improved Kaczmarz method is proposed in this paper, which circumvents the matrix inversion operation and thus reduces the complexity by an order of magnitude. Meanwhile, an optimal relaxation parameter is introduced to further accelerate the convergence speed of the proposed algorithm and two approximate methods of calculating the log-likelihood ratios (LLRs) for channel decoding are obtained as well. Analysis and simulations verify that the proposed algorithm outperforms various typical low-complexity signal detection algorithms. The proposed algorithm converges rapidly and achieves its performance quite close to that of the MMSE algorithm with only a small number of iterations.

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Section: Exact Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Low-Complexity Soft-Output Signal Detection Based on Improved Kaczmarz Iteration Algorithm for Uplink Massive MIMO System

Shen

Zhao

et al. 2020

Sensors

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show abstract

“…For 5G higher frequency band communication systems such as mmWaves, large-scale antenna arrays would be a system pre-requisite for combating the poor propagation characteristics within these bands. This technology also enhances spectral and energy efficiency by adding the number of antennas at the BS (Basestation) to serve users with directed signal and reasonably simple signal processing [11]. Accordingly, massive MIMO has potential advantages like: capacity and link reliability, high spectral and energy efficiency, security enhancement and robustness improvement, high degree of freedom, cost efficiency and simple signal processing [12][13][14][15][16].…”

Section: Massive Mimo Communication Technologymentioning

confidence: 99%

Massive MIMO, mmWave and mmWave-Massive MIMO Communications: Performance Assessment with Beamforming Techniques

Engda¹,

Wondie²,

Steinbrunn³

2020

Preprint

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A considerable amount of enabling technologies are being explored in the era of fifth generation (5G) mobile system. The dream is to build a wireless network that substantially improves the existing mobile networks in all performance metrics. To address this 5G design targets, massive MIMO (multiple input multiple output) and mmWave (millimeter wave) communication are also candidate technologies. Luckily, in many respects these two technologies share a symbiotic integration. Accordingly, a logical step is to integrate mmWave communications and massive MIMO to form mmWave-massive MIMO which substantially increases user throughput, improve spectral and energy efficiencies, increase the capacity of mobile networks and achieve high multiplexing gains. Thus, this work analyses the concepts, performances, comparison and discussion of these technologies called: massive MIMO, mmWave Communications and mmWave-massive MIMO systems jointly. Besides, outcomes of extensive researches, emerging trends together with their respective benefits, challenges, proposed solutions and their comparative analysis is addressed. The performance of hybrid analog-digital beamforming architecture with a fully digital and analog beamforming techniques are also analyzed. Analytical and simulation results show that the low-complexity hybrid analog-digital precoding achieves all round comparable precoding gains for mmWave-Massive MIMO technology.

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“…In addition, the VLC provides high energy efficiency, high security, and free of interference [4]. The M-MIMO system is an extension of a small scale MIMO where a large number of antennas at the base-station (BS) are deployed to serve multiple user terminals [5]. It contributes positively in spectrum efficiency (SE) and energy efficiency (EE), increases the degree of freedom, and reduces the latency.…”

Section: Introductionmentioning

confidence: 99%

Impact of Stair and Diagonal Matrices in Iterative Linear Massive MIMO Uplink Detectors for 5G Wireless Networks

2020

Self Cite

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In massive multiple-input multiple-output (M-MIMO) systems, a detector based on maximum likelihood (ML) algorithm attains optimum performance, but it exhaustively searches all possible solutions, hence, it has a very high complexity and realization is denied. Linear detectors are an alternative solution because of low complexity and simplicity in implementation. Unfortunately, they culminate in a matrix inversion that increases the computational complexity in high loaded systems. Therefore, several iterative methods have been proposed to approximate or avoid the matrix inversion, such as the Neuamnn series (NS), Newton iterations (NI), successive overrelaxation (SOR), Gauss–Siedel (GS), Jacobi (JA), and Richardson (RI) methods. However, a detector based on iterative methods requires a pre-processing and initialization where good initialization impresses the convergence, the performance, and the complexity. Most of the existing iterative linear detectors are using a diagonal matrix ( D ) in initialization because the equalization matrix is almost diagonal. This paper studies the impact of utilizing a stair matrix ( S ) instead of D in initializing the linear M-MIMO uplink (UL) detector. A comparison between iterative linear M-MIMO UL detectors with D and S is presented in performance and computational complexity. Numerical Results show that utilization of S achieves the target performance within few iterations, and, hence, the computational complexity is reduced. A detector based on the GS and S achieved a satisfactory bit-error-rate (BER) with the lowest complexity.

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Massive MIMO Detection Techniques: A Survey

Cited by 341 publications

References 239 publications

Low-Complexity Soft-Output Signal Detection Based on Improved Kaczmarz Iteration Algorithm for Uplink Massive MIMO System

Low-Complexity Soft-Output Signal Detection Based on Improved Kaczmarz Iteration Algorithm for Uplink Massive MIMO System

Massive MIMO, mmWave and mmWave-Massive MIMO Communications: Performance Assessment with Beamforming Techniques

Impact of Stair and Diagonal Matrices in Iterative Linear Massive MIMO Uplink Detectors for 5G Wireless Networks

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