Approximate Neumann Series or Exact Matrix Inversion for Massive MIMO?

Gustafsson, Oscar; Bertilsson, Erik; Klasson, Johannes; Ingemarsson, Carl

doi:10.1109/arith.2017.11

Cited by 21 publications

(21 citation statements)

References 10 publications

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“…The centralized matrix inversion can be performed either using an exact [23] or an approximate [12], [24], [25] algorithm. As shown in [26], the complexity is similar for the best exact algorithm and a Neumann series approximation with three terms. In both cases a 20 × 20 matrix inversion can be performed in less than 40 µs using one processing element running at 200 MHz.…”

Section: Example: Lte-like System Specificationsmentioning

confidence: 83%

A Distributed Processing Architecture for Modular and Scalable Massive MIMO Base Stations

Bertilsson,

Gustafsson,

Larsson

2018

Preprint

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In this work, a scalable and modular architecture for massive MIMO base stations with distributed processing is proposed. New antennas can readily be added by adding a new node as each node handles all the additional involved processing. The architecture supports conjugate beamforming, zero-forcing, and MMSE, where for the two latter cases a central matrix inversion is required. The impact of the time required for this matrix inversion is carefully analyzed along with a generic frame format. As part of the contribution, careful computational, memory, and communication analyses are presented. It is shown that all computations can be mapped to a single computational structure and that a processing node consisting of a single such processing element can handle a broad range of bandwidths and number of terminals.

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Section: Example: Lte-like System Specificationsmentioning

confidence: 83%

A Distributed Processing Architecture for Modular and Scalable Massive MIMO Base Stations

Bertilsson,

Gustafsson,

Larsson

2018

Preprint

Self Cite

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“…The convergence rates of approximate-inversion based data detectors will significantly deteriorate in such scenarios. A study in [16] suggested that exact matrix inversion based detectors are viable alternatives from both complexity and latency perspective. A few implementations of exact-inversion based data detection have been proposed in [17] and [18].…”

Section: A Relevant Prior Artmentioning

confidence: 99%

ADMM-Based Infinity-Norm Detection for Massive MIMO: Algorithm and VLSI Architecture

Shahabuddin

Hautala

Juntti

et al. 2021

IEEE Trans. VLSI Syst.

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We propose a novel data detection algorithm and a corresponding very large scale integration (VLSI) design for massive multi-user (MU) multiple-input multiple-output (MIMO) wireless systems. Our algorithm uses alternating direction method of multipliers (ADMM)-based infinity norm constrained equalization and is called ADMIN. ADMIN is an iterative algorithm that outperforms linear detectors by a large margin when the ratio between the numbers of base-station (BS) and user antennas is small. In the first iteration, ADMIN computes the linear minimum mean-square error (MMSE) solution, which is sufficient when the ratio between the numbers of BS and user antennas is large. We develop time-shared and iterative VLSI architectures for LDL-decomposition based soft-output ADMIN supporting 16-and 32-user systems. We present applicationspecific integrated circuit (ASIC) designs for 16 to 64 antenna base stations in 28 nm complementary metaloxidesemiconductor (CMOS) that supports up to 64 quadrature amplitude modulation (QAM). The 16-user ADMIN ASIC achieves 303 Mbps while dissipating 85 mW. The 32-user ADMIN ASIC achieves 287 Mbps and 241 Mbps while dissipating 121 mW and 135 mW for 32 and 64 BS antennas, respectively. ADMIN has also been implemented on a Xilinx Virtex-7 field-programmable gate array (FPGA) and is compared with state-of-the-art massive MIMO data detectors.

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“…Several linear and non-linear detectors have been proposed. The linear detectors such as Maximum Likelihood (ML), Zero Forcing (ZF), and Minimum Mean Square Error (MMSE) have been considered for massive MIMO signal detection [8][9][10]. ML detection provides optimal performance, but it is computationally infeasible for a system such as massive MIMO involving thousands of antennas.…”

Section: Relevant Prior Art and Motivationmentioning

confidence: 99%

SSOR Preconditioned Gauss-Seidel Detection and Its Hardware Architecture for 5G and beyond Massive MIMO Networks

et al. 2021

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With the limitedness of the sub-6 GHz bandwidth, the world is exploring a thrilling wireless technology known as massive MIMO. This wireless access technology is swiftly becoming key for 5G, B5G, and 6G network deployment. The massive MIMO system brings together antennas at both base stations and the user terminals to provide high spectral service. Despite the fact that massive MIMO offers astronomical benefits such as low latency, high data rate, improved array gain, and far better reliability, it faces several implementation challenges due to the hundreds of antennas at the base station. The signal detection at the base station during the uplink is one of the critical issues in this technology. Detection of user signal becomes computationally complex with a multitude of antennas present in the massive MIMO systems. This paper proposes a novel preconditioned and accelerated Gauss–Siedel algorithm referred to as Symmetric Successive Over-relaxation Preconditioned Gauss-Seidel (SSORGS). The proposed algorithm will address the signal detection challenges associated with massive MIMO technology. Furthermore, we enhance the convergence rate of the proposed algorithm by introducing a novel Symmetric Successive Over-relaxation preconditioner (SSOR) scheme and an initialization scheme based on the instantaneous channel condition between the base station and the user. The simulation results show that the proposed algorithm referred to as Symmetric Successive Over-relaxation Preconditioned Gauss-Seidel (SSORGS) provides optimal BER performance. At BER =10−3, over the range of SNR, the SSORGS algorithm performs better than the traditional algorithms. Additionally, the proposed algorithm is computationally more efficient than the traditional algorithms. Furthermore, we designed a comprehensive hardware architecture for the SSORGS algorithm to find the interrelated components necessary to build the actual physical system.

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Approximate Neumann Series or Exact Matrix Inversion for Massive MIMO?

Cited by 21 publications

References 10 publications

A Distributed Processing Architecture for Modular and Scalable Massive MIMO Base Stations

A Distributed Processing Architecture for Modular and Scalable Massive MIMO Base Stations

ADMM-Based Infinity-Norm Detection for Massive MIMO: Algorithm and VLSI Architecture

SSOR Preconditioned Gauss-Seidel Detection and Its Hardware Architecture for 5G and beyond Massive MIMO Networks

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