Impact of User Number on Massive MIMO with a Practical Number of Antennas

Hasan, Wael Boukley; Harris, Paul; Doufexi, Angela; Beach, M.A.

doi:10.1109/vtcspring.2018.8417581

Cited by 4 publications

(4 citation statements)

References 15 publications

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“…Hence, experimental results suggest that, compared to the 4th generation networks, LS‐MIMO provides a main enhancement in performance towards realising a thousand time improved throughput that corroborate the significant role of LS‐MIMO in 5G and beyond 5G technologies [6, 7]. By equipping the BS with extra antennas, the capacity enhancement is also possible [8]. In addition, massive MIMO supports communications between various devices in large sensor networks [9] and internet of things (IoT) environments [10, 11].…”

Section: Introductionmentioning

confidence: 85%

“…The complexity order of RT based method is (N 2 t ) for detecting N symbols whereas for per symbol detection it is (N t ). Since the proposed method, conventional GS and P-GS iteratively improve the initial solution via the Gauss-Seidel equation (8), the complexity order of iterative cycles is same for all these detectors. The GS detection employed diagonal initial solution which has higher complexity than the proposed initial solution, and the proposed algorithm needs additional multiplications for the hybrid iteration to compute (10).…”

Section: Algorithm Analysismentioning

confidence: 99%

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Improved Gauss–Seidel detector for large‐scale MIMO systems

et al. 2022

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Large-scale multiple-input multiple-output (LS-MIMO) is one of the promising technologies beyond the 5G cellular system in which large antenna arrays at the base station (BS) improve the system capacity and energy-efficiency. However, the large number of antennas at the BS makes it challenging to design low-complexity high-performance data detectors. Thus, a number of iterative detection methods, such as Gauss-Seidel and conjugate gradient, are introduced to achieve complexity-performance tradeoff. However, their performance deteriorates for the systems with small BS-to-user antenna ratio or for the channels that exhibit correlation. This paper proposes a new efficient iterative detection algorithm based on the improved Gauss-Seidel iteration to address this problem. The proposed method performs one conjugate gradient iteration that enables better performance with less number of iterations. A new hybrid iteration is introduced and a low-complexity initial estimation is utilised to enhance detection accuracy while reducing the complexity further. In addition, a novel preconditioning technique is proposed to maintain the benefits of the proposed detector in correlated MIMO channels. It is mathematically demonstrate that the proposed detector achieves low approximated error. Theoretical analysis and numerical results show that the proposed algorithm provides a faster convergence rate compared to conventional methods.

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Section: Introductionmentioning

confidence: 85%

Section: Algorithm Analysismentioning

confidence: 99%

Improved Gauss–Seidel detector for large‐scale MIMO systems

et al. 2022

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“…In addition, distributed MIMO eliminates the interference and improves the throughput in wireless communication networks [ 10 ]. The capacity enhancement is also possible by furnishing the base station (BS) with extra antennas [ 11 ]. In [ 12 ], influence of different number of antennas on the performance is comprehensively illustrated.…”

Section: Introductionmentioning

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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“…Simplification of the existing NCMA decoding algorithm, designed for the binary phase shift keying (BPSK) caused throughput degradation. In [7], the authors investigated several constrains such as bandwidth, latency and complexity on the design of 5G. The authors evaluated the impact of spatially multiplexing more UEs on performance within a massive MIMO system to improve spectral efficiency of 5G system.…”

mentioning

confidence: 99%

5G Downlink Throughput Enhancement by Beams Consolidating at Vacant Traffic

Janaby¹

2019

JCOMSS

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The 3GPP release for 5G (R15) assigns each User Equipment (UE) a radio beam by employing Massive Multi-User MU-Multiple-Input-Multiple-Output (MIMO) technology. Each beam carries, at the downlink, a data rate according to the modulation and coding scheme (MCS) assigned by the base station (BS). For the limited existence of active UEs and during vacant traffic or standby UEs, the assigned beams will be transmitted, but not to any UE. This paper proposes a new scheme that consolidates vacant beams of inactive UEs, to the adjacent beam of the active UE or UE at the cell edge to duplicate the bandwidth of the new beam. The proposed scheme increases the level of desired modulation and coding scheme (MCS) to a higher scheme and hence enhances the spectral efficiency (SE) of the 5G mobile networks. Specifically, the BS consolidates (combines) multiple radio beams along with the assigned beam during vacant traffic. More than two beams are consolidated in particular to the active UE to increase the bit rate by assigning higher MCS. The simulation evaluation depicted that the performance of beams consolidation provides a gain of 3.5 dB above than the state before beams consolidation. Moreover, more than 40 % improvement in UE throughput is achieved.

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Impact of User Number on Massive MIMO with a Practical Number of Antennas

Cited by 4 publications

References 15 publications

Improved Gauss–Seidel detector for large‐scale MIMO systems

Improved Gauss–Seidel detector for large‐scale MIMO systems

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

5G Downlink Throughput Enhancement by Beams Consolidating at Vacant Traffic

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