Enhanced Linear Iterative Detector for Massive Multiuser MIMO Uplink

Tan, Xiaosi; Jin, Jiejun; Sun, Kai; Xu, Yunhao; Li, Muhao; Zhang, Yaping; Zhang, Zaichen; You, Xiaohu; Zhang, Chuan

doi:10.1109/tcsi.2019.2924970

Cited by 16 publications

(22 citation statements)

References 35 publications

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“…The design in [42] implements a parallel Gauss-Seidel (PGS) algorithm that iteratively solves an L-MMSE data detection problem for frequency-flat channels. In Table I, we also include two of the most recent, state-of-the-art linear detectors for massive MU-MIMO systems, proposed in [27] and [43]. The design presented in [27], implements a recursive conjugate-gradient-based L-MMSE detector, called RCG, and the design in [43] implements an algorithm based on steepest descent and Barzilai-Borwein algorithms, abbreviated to ES-DBB, that solves the L-MMSE detection problem iteratively.…”

Section: E Implementation Results and Comparisonmentioning

confidence: 99%

“…Therefore, in Table I, we scale the reported throughput of these reference designs by a factor of 4/6 so that the throughput of all designs is with respect to 16-QAM. We include the throughput for ESDBB as it was reported in [43], since the modulation scheme was not specified for that design. We note that PGS, NS-SCFDMA and RCG designs contain hardware to compute post-equalization signalto-noise-plus-interference ratio (SINR) and log-likelihood ratios (LLRs), which is not present in our design.…”

Section: E Implementation Results and Comparisonmentioning

confidence: 99%

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Algorithm and VLSI Design for 1-Bit Data Detection in Massive MIMO-OFDM

Mirfarshbafan

Shabany

Nezamalhosseini

et al. 2020

IEEE Open J. Circuits Syst.

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The use of low-resolution data converters in the radio-frequency (RF) chains of all-digital massive multiple-input multiple-output (MIMO) basestations promises significant reductions in power consumption, hardware costs, and interconnect bandwidth. We propose a quantization-aware data-detection algorithm which mitigates the performance loss of 1-bit quantized massive MIMO orthogonal frequency-division multiplexing (OFDM) systems. Since the system performance heavily depends on the quality of channel estimates, we also develop a nonlinear 1-bit channel estimation algorithm that builds upon the proposed data detection algorithm. We show that the proposed algorithms significantly outperform linear data detectors and channel estimators in terms of bit error rate. For the proposed nonlinear data detection algorithm, we develop a very large scale integration (VLSI) architecture and present implementation results on a Xilinx Virtex-7 field programmable gate array (FPGA). Our implementation results are, to the best of our knowledge, the first for 1-bit massive MU-MIMO-OFDM systems and demonstrate comparable hardware efficiency with respect to state-of-the-art linear data detectors designed for systems with high-resolution data converters, while achieving lower bit error rate. Index Terms-1-bit analog-to-digital converter (ADC), channel estimation, data detection, FPGA implementation, massive multiple-input multiple-output (MIMO), orthogonal frequencydivision multiplexing (OFDM), VLSI design.

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Section: E Implementation Results and Comparisonmentioning

confidence: 99%

Section: E Implementation Results and Comparisonmentioning

confidence: 99%

Algorithm and VLSI Design for 1-Bit Data Detection in Massive MIMO-OFDM

Mirfarshbafan

Shabany

Nezamalhosseini

et al. 2020

IEEE Open J. Circuits Syst.

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“…The received signal vector,

boldy \in C^{N_{B} \times 1}

, at the base station is represented as 21

boldy = boldHs + boldn .

Here,

bolds \in ψ^{N_{U} \times 1}

is the transmitted symbol vector and

ψ

is the constellation set of square

M

‐QAM defined as 20,33

ψ = ({}, ψ_{r} + j ψ_{i} (|, ψ_{r}, ψ_{i} \in \sqrt{\frac{E_{s}}{{normalГ N}_{U}}} ((), - \sqrt{M} + 1, \dots, - 1, 1, \dots \sqrt{M} - 1)))

where

M

E_{s}

, and

Г = \frac{2 ((), M - 1)}{3}

represents the constellation size, total transmitted power and average symbol power, respectively. The covariance matrix of

bolds

R_{ss} = E ([], bolds s^{H}) = \frac{E_{s}}{N_{U}} I_{N_{U}}

.…”

Section: Mu‐mimo Uplink Modelmentioning

confidence: 99%

“…For example, an approximate inverse is computed by Shafivulla et al 19 based on Cayley–Hamilton theorem 19 . Further, to improve the performance of constrained descent search (CDS) detector, symmetric successive over relaxation (SSOR)‐based pre‐conditioner is introduced by Zhang et al 20 Also, a novel enhanced iterative detector based on steepest descent (SD) and Barzilai–Borwein (BB) algorithms is proposed by Tan et al 21 However, when the number of active users scales up and the total number of transmitting antennas becomes close to the number of base station antennas, that is, as

β

approaches unity, the iterative methods suffers from slow convergence rate. Under this scenario, decomposition algorithms like Cholesky and QR are preferred to compute the pseudo‐inverse of channel matrix 22–24…”

Section: Introductionmentioning

confidence: 99%

BER analysis of truncated SVD‐based MU‐MIMO ZF detection scheme under correlated Rayleigh fading channel

Eduru

Nakkeeran

2021

Int J Communication

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Summary In multi‐user multiple input multiple output (MU‐MIMO) uplink, the bit error rate (BER) of linear detection schemes, namely, zero forcing (ZF) and minimum mean square error (MMSE) under correlated Rayleigh fading channel deteriorates with increase in spatial correlation and load factor due to the increase in the condition number of the channel matrix. Moreover, the performance gap between ZF and MMSE increases with increase in the antenna number for large load factors. To address this issue, in this paper, truncated singular value decomposition (TSVD), which is a popular method for solving ill‐posed problem, is used in the ZF detection process through proper selection of hard threshold. However, the pseudo‐inverse computed using TSVD is an approximate inverse, which results in interference at the output of the detection process. Therefore, a mathematical expression for the post‐processing signal‐to‐interference‐plus‐noise ratio (SINR) of the substream is derived and subsequently BER is evaluated. Further, the analytical BER is analyzed with respect to the parameters namely, average signal‐to‐noise ratio (SNR) ( EsN0), correlation coefficient ( ρU,ρB), load factor ( β), and number of singular values considered to compute the hard threshold ( n) and also validated by Monte Carlo trails. From the simulation results, it is observed that for higher order MU‐MIMO systems and at lower values of EsN0, the performance of TSVD‐based scheme is comparable with that of MMSE by decreasing the truncation parameter ( k). Moreover, as the complexity of TSVD‐based ZF using randomized schemes is O(NU2NB+NU2log(k)+NUk2), therefore, the complexity of detection significantly decreases with decrease in k.

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“…Therefore, the ML detection is prohibited in largescale MIMO networks. In contrast, linear detectors are simpler than the ML detectors but they involve an unfavourable computation of a matrix inversion and multiplications [78]. It is well known that the matrix inversion complexity of linear detectors is O K 3 .…”

Section: Introductionmentioning

confidence: 99%

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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Enhanced Linear Iterative Detector for Massive Multiuser MIMO Uplink

Cited by 16 publications

References 35 publications

Algorithm and VLSI Design for 1-Bit Data Detection in Massive MIMO-OFDM

Algorithm and VLSI Design for 1-Bit Data Detection in Massive MIMO-OFDM

BER analysis of truncated SVD‐based MU‐MIMO ZF detection scheme under correlated Rayleigh fading channel

Low Complexity Linear Detectors for Massive MIMO: A Comparative Study

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