Design and implementation of a tdd-based 128-antenna massive MIMO prototype system

Yang, Xi; Lu, Wen‐Jun; Wang, Ning; Nieman, Karl; Wen, Chao-Kai; Zhang, Chuan; Jin, Shi; Mu, Xiaomin; Wong, Ian; Huang, Yongming; You, Xiaohu

doi:10.1109/cc.2017.8246333

Cited by 32 publications

(36 citation statements)

References 17 publications

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“…Then, in Fig. 6, we compare the performance of Algorithm 1 with that of various algorithms under full subarray model, including decentralized conjugate gradient (DeCG) and alternating direction method of multipliers (DeADMM) proposed in [33] 5 , and the original centralized EP MIMO detector proposed in [17]. To facilitate parallel simple computation of matrix inversion using Algorithm 2, we set N c = 2, which yields a similar performance as that under larger N c as shown in Fig.…”

Section: A Stationary Casementioning

confidence: 99%

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Expectation Propagation Detector for Extra-Large Scale Massive MIMO

Wang¹,

Kosasih²,

Wen³

et al. 2020

IEEE Trans. Wireless Commun.

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The deployment of extremely large-scale antenna array at the base station, which is referred to as extra-large-scale massive multiple-input-multiple-output (MIMO) system, is a brand new communication paradigm allowing for significant performance gain at the cost of excessively large amount of fronthaul data and ultra-high computational complexity. These practical challenges inspire the subarray-based processing architecture. Moreover, the spatial no-stationarity occurs and different portions of array observe different channel properties. Therefore, the aforementioned aspects should be considered into detector design for extra-large-scale massive MIMO systems. In this paper, the iterative detector is developed leveraging the expectation propagation (EP) principle based on the factor graph describing the subarray-based architecture. Its convergence is intuitively verified from the evolution analysis. We also demonstrate that different sizes and numbers of subarrays yield similar performance. In addition, we consider various implementation related aspects. We show that the non-stationary property can be exploited to reduce complexity. To avoid excessively high-complexity matrix inversion, we propose a hierarchical implementation architecture to enable parallel computation of simple-form matrix inversion.Moreover, we modify the proposed EP-based detector which requires only one feedforward from each subarray to the central processing unit to minimize the transfer latency. Simulation results demonstrate that the proposed detector outperforms its counterparts and verify the validity of our analyses.

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Section: A Stationary Casementioning

confidence: 99%

“…Massive MIMO communication systems have recently been implemented in academia and various industries, and many prototyping systems have been constructed [5], [6], implying the potential of its future commercial success.…”

Section: Introductionmentioning

confidence: 99%

Expectation Propagation Detector for Extra-Large Scale Massive MIMO

Wang¹,

Kosasih²,

Wen³

et al. 2020

IEEE Trans. Wireless Commun.

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“…Consequently, experimental activities on Massive MIMO are concentrated to research-grade testbeds that require the investment of many person-years to build, and permanent engineering staff to maintain ---thus inherently unsuitable for instructional purposes. Worldwide, only few such testbeds, operational and under construction, are known [5,6,7,8,9,10].…”

Section: Communications Laboratory Exercises: Rf Versus Acousticmentioning

confidence: 99%

Teaching the Principles of Massive MIMO: Exploring reciprocity-based multiuser MIMO beamforming using acoustic waves

Larsson

Danev

Olofsson

et al. 2017

IEEE Signal Process. Mag.

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“…Some works about implementation of massive MIMO prototyping system have been presented in [17], where large-scale antennas are used to verify the significant gains in data rates. A rapid prototyping system architecture is proposed in [18], where the proposed system has high scalability in terms of the number of antennas.…”

Section: Simplified Spinal Codes Based Mimo Demo System With Ni Usrp mentioning

confidence: 99%

Research and Implementation of Rateless Spinal Codes Based Massive MIMO System

Wang

Chen

Tan

2018

Wireless Communications and Mobile Computing

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The potential performance gains promised by massive multi-input and multioutput (MIMO) rely heavily on the access to accurate channel state information (CSI), which is difficult to obtain in practice when channel coherence time is short and the number of mobile users is huge. To make the system with imperfect CSI perform well, we propose a rateless codes-aided massive MIMO scheme, with the aim of approaching the maximum achievable rate (MAR) as well as improving the achieved rate over that based on the fixed-rate codes. More explicitly, a recently proposed family of rateless codes, called spinal codes, are applied to massive MIMO systems, where the spinal codes bring the benefit of approximately achieving the MAR with sufficiently large encoding block size. In addition, the multilevel puncturing and dynamic block-size allocation (MPDBA) scheme is proposed, where the block sizes are determined by user MAR to curb the average retransmission delay for successfully decoding the messages, which further enhances the system retransmission efficiency. Multilevel puncturing, which is MAR dependent, narrows the gap between the system MAR and the related achieved rate. Theoretical analysis is provided to demonstrate that spinal codes with the MPDBA can guarantee the system retransmission efficiency as well as achieved rate, which are also verified by numerical simulations. Finally, a simplified but comparable MIMO testbed with 2 transmit antennas and 2 single-antenna users, based on NI Universal Software Radio Peripheral (USRP) and LabVIEW communication toolkits, is built up to demonstrate the effectiveness of our proposal in realistic wireless channels, which is easy to be extended to massive MIMO scenarios in future.

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Design and implementation of a tdd-based 128-antenna massive MIMO prototype system

Cited by 32 publications

References 17 publications

Expectation Propagation Detector for Extra-Large Scale Massive MIMO

Expectation Propagation Detector for Extra-Large Scale Massive MIMO

Teaching the Principles of Massive MIMO: Exploring reciprocity-based multiuser MIMO beamforming using acoustic waves

Research and Implementation of Rateless Spinal Codes Based Massive MIMO System

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