A Low Complexity Expectation Propagation Detection for Massive MIMO System

Yao, Guoqiang; Yang, Guiwu; Hu, Jianhao; Chao, Fei

doi:10.1109/glocom.2018.8647172

Cited by 13 publications

(5 citation statements)

References 20 publications

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“…The original EP detector is the object to optimize. The EP-SU detector is our former work in [42] and it is not ideal. The high-efficiency EP detector is proposed in this paper.…”

Section: Performance Analysismentioning

confidence: 99%

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High-Efficiency Expectation Propagation Detector for High-Order Massive MIMO Systems

Yao

Yang

et al. 2019

IEEE Access

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Multiple-inputs multiple-outputs (MIMO) technology, including massive MIMO, plays an important role in modern wireless communication systems. Massive MIMO systems with high-order modulation can promote spectrum efficiency. The algorithm complexity and detection performance are the main challenges for the massive MIMO system. Thus, the low complexity and high-performance MIMO detector is important for the practical massive MIMO application. The Expectation Propagation (EP) detector outperforms many conventional detectors in high-order massive MIMO scenarios. However, its complexity increases exponentially with the number of sending antennas and the modulation order. In this paper, we propose a novel information updating scheme for EP MIMO detection algorithm to achieve high performance with low complexity. The high-efficiency EP detector is based on the expectation propagation algorithm with the jointed scheme of successive updating, sorting updating and sphere search aided algorithm. Numerical results show the high-efficiency EP detector reduces over 85% complexity of the original EP detector for the scenario N t = N r = 20 with 64-QAM modulation, and the gain on complexity becomes more evident with the increase of antenna scale and the modulation order. The high-efficiency EP detector can outperform the original EP detector in different high-order massive scenarios. Compared with MMSE algorithm, the proposed scheme can get huge performance gain with 1.5 times complexity for high-order massive MIMO systems.INDEX TERMS Expectation propagation, massive MIMO detector, low complexity, high performance, high-order modulation.

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“…The original EP detector is the object to optimize. The EP-SU detector is our former work in [42] and it is not ideal. The high-efficiency EP detector is proposed in this paper.…”

Section: Performance Analysismentioning

confidence: 99%

“…It's unacceptable for high-order massive MIMO systems. We have proposed a low-complexity EP MIMO detection algorithm in [42], but its limitation is still obvious.…”

Section: Introductionmentioning

confidence: 99%

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High-Efficiency Expectation Propagation Detector for High-Order Massive MIMO Systems

Yao

Yang

et al. 2019

IEEE Access

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“…• First Bayesian M-MIMO iterative receiver that uses PIC scheme [10]. This leads to an elimination of the matrix inversion operations or approximations used in several advanced iterative receivers [11], [13], [14], [18]. As a result, linearly computational latency processing and a near-optimal performance are achieved.…”

Section: Introductionmentioning

confidence: 99%

“…• First Bayesian M-MIMO iterative receiver that derives the learning parameters directly from the symbol errors between estimations and observations in different iterations via the DSC scheme. This is in contrast to the trial and error process used by many Bayesian receivers [11], [13], [14], [16] to find the learning parameters.…”

Section: Introductionmentioning

confidence: 99%

A Linear Bayesian Learning Receiver Scheme for Massive MIMO Systems

Kosasih

Hardjawana

Vucetic

et al. 2020

2020 IEEE Wireless Communications and Networking Conference (WCNC)

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Much stringent reliability and processing latency requirements in ultra-reliable-low-latency-communication (URLLC) traffic make the design of linear massive multipleinput-multiple-output (M-MIMO) receivers becomes very challenging. Recently, Bayesian concept has been used to increase the detection reliability in minimum-mean-square-error (MMSE) linear receivers. However, the latency processing time is a major concern due to the exponential complexity of matrix inversion operations in MMSE schemes. This paper proposes an iterative M-MIMO receiver that is developed by using a Bayesian concept and a parallel interference cancellation (PIC) scheme, referred to as a linear Bayesian learning (LBL) receiver. PIC has a linear complexity as it uses a combination of maximum ratio combining (MRC) and decision statistic combining (DSC) schemes to avoid matrix inversion operations. Simulation results show that the bit-error-rate (BER) and latency processing performances of the proposed receiver outperform the ones of MMSE and best Bayesian-based receivers by minimum 2 dB and 19 times for various M-MIMO system configurations.

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