Large-Scale MIMO Detection for 3GPP LTE: Algorithms and FPGA Implementations

Wu, Michael; Yin, Bei; Wang, Guohui; Dick, Chris; Cavallaro, Joseph R.; Studer, Christoph

doi:10.1109/jstsp.2014.2313021

Cited by 362 publications

(402 citation statements)

References 34 publications

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“…In this paper, we propose a host of novel low-complexity, soft-output data detection methods for wideband massive MU-MIMO systems that further reduce the complexity compared to existing approximate methods in [1,[6][7][8][9][10][11]. Our contributions are summarized as follows:…”

Section: Contributionsmentioning

confidence: 99%

“…In order to evaluate the efficacy of our algorithms, we study the associated performance/complexity trade-offs in a 3GPP LTE-based massive MU-MIMO wireless system 1 , and we provide a detailed performance and complexity comparison with existing approximate inversion algorithms proposed in [1,[6][7][8][9][10][11]. 1 The methods proposed in this paper can easily be extended to other multi-carrier waveforms that support frequencydomain equalization [13], such as OFDM-based systems.…”

Section: Contributionsmentioning

confidence: 99%

“…This technology not only promises significant improvements in terms of the spectral efficiency compared to traditional, smallscale MIMO through fine-grained beamforming, but also enables simple and low-complexity data detection methods to achieve near-optimal error-rate performance [1]. However, for wideband massive MU-MIMO wireless systems with a large number of subcarriers, such as long-term evolution (LTE)-based systems with thousands of subcarriers, even some of the least expensive linear data-detection algorithms, e.g., methods relying on linear minimum mean square error (MMSE) equalization, require excessive hardware complexity and power consumption (see [6] for a detailed discussion).…”

Section: Introductionmentioning

confidence: 99%

“…In order to address the complexity and power consumption issue of linear data detection in wideband massive MU-MIMO systems, a variety of approximate matrix inversion methods have been proposed in recent years [1,[6][7][8][9][10][11]. These methods require, in general, lower computational complexity than exact, linear methods or non-linear algorithms, and entail only a small error-rate performance loss in massive MU-MIMO systems having a large BS-to-user-antenna ratio.…”

Section: Introductionmentioning

confidence: 99%

“…For systems in which the BS-to-user-antenna ratio is below two, however, approximate methods result in a strong error floor-often too high to enable reliable communication at high data rates. Furthermore, corresponding high-throughput very-large scale integration (VLSI) designs for wideband massive MU-MIMO systems that use orthogonal frequency-division multiplexing (OFDM) or single-carrier frequency-division multiple access (SC-FDMA), such as the reference hardware designs in [6,12], still require large silicon area and excessively high power consumption. Hence, to successfully deploy massive MU-MIMO in practical wideband systems, new algorithm solutions that achieve near-optimal error-rate performance at low hardware complexity are necessary.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Implicit vs. Explicit Approximate Matrix Inversion for Wideband Massive MU-MIMO Data Detection

Yin

et al. 2017

J Sign Process Syst

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Massive multi-user (MU) MIMO wireless technology promises improved spectral efficiency compared to that of traditional cellular systems. While datadetection algorithms that rely on linear equalization achieve near-optimal error-rate performance for massive MU-MIMO systems, they require the solution to large linear systems at high throughput and low latency, which results in excessively high receiver complexity. In this paper, we investigate a variety of exact and approximate equalization schemes that solve the system of linear equations either explicitly (requiring the computation of a matrix inverse) or implicitly (by directly computing the solution vector). We analyze the associated performance/complexity trade-offs, and we show that for small base-station (BS)-to-user-antenna ratios, exact and implicit data detection using the Cholesky decomposition achieves near-optimal performance at low complexity. In contrast, implicit data detection using approximate equalization methods results in the best trade-off for large BS-to-user-antenna ratios. By combining the advantages of exact, approximate, implicit, and explicit matrix inversion, we develop a new frequencyadaptive equalizer (FADE), which outperforms existing data-detection methods in terms of performance and complexity for wideband massive MU-MIMO systems.

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Section: Contributionsmentioning

confidence: 99%

Section: Contributionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Implicit vs. Explicit Approximate Matrix Inversion for Wideband Massive MU-MIMO Data Detection

Yin

et al. 2017

J Sign Process Syst

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Distributed state estimation through co‐acting Kalman filters

Kanagala

Detroja

2020

Asian Journal of Control

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Distributing calculations of a central Kalman filter requires subsystem level expressions for the propagation and update steps of the Kalman filter. It is difficult to obtain subsystem level expressions due to the inverse term present in the update step. In this manuscript, a non-iterative way of decomposing the inverse of a matrix is presented. This decomposition allows rewriting the update equations of the Kalman filter subsystem-wise. Subsequently, a Co-acting Kalman Filter (CoKF) is proposed using these decomposed central Kalman filter equations to perform distributed state estimation. The convergence of the CoKF algorithm is established under the assumption that each subsystem is observable. Two variants of the proposed CoKF, namely (m-CoKF and p-CoKF), suitable for applications on opposite ends of computation and communication resource spectrum, are presented along with the trade-offs involved. A comparison of the proposed method with existing distributed Kalman filters is also presented. The proposed CoKF algorithm is implemented on a standard wireless sensor network example with 200 nodes. The simulation results demonstrate the accuracy of the proposed CoKF algorithm relative to the central Kalman filter. KEYWORDS distributed state estimation, distributed systems, Kalman filter 1. It should distribute the calculations of a central estimator in such a way that the computations involved in central estimation are equally distributed among each distributed estimator 2. It needs to duly consider the interactions betweensubsystems and systematically incorporate information received from other subsystems 3. While keeping the communication between distributed estimators to a minimum, the estimates produced by

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Bit error rate aware accurate prediction of original signals with low distortion using low complexity detection algorithms

Kasiselvanathan¹,

Kumar²

2019

Concurrency and Computation

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Summary In the Massive MIMO system, complexity reduction acts as a significant role. This problem is focused in previous method whose foremost objective is to attain the low complexity. The main goal of this algorithm is to process the signal vector with more maximum likelihood cost instead of handling every signals vector, which might lead to more iteration. This is done by introducing different heuristic algorithms which attempts to find the signal vectors with more maximum likelihood values. The algorithms introduced in the existing work are QP detector and the Branch and Bound algorithm. The existing work still poses more complexity by taking more number of iterations for finding the more relative signal vector. These problems are resolved in the proposed system by introducing the novel framework, namely, pruning‐based maximum likelihood detection using low complexity detection algorithms (PRUN‐MLD‐LCDA). In the proposed system, complexity of the system is further reduced by introducing the cross correlation based pruning technique which attempts to find the similar signal, which is similar based on more signal components. The modified branch and bound algorithm is introduced to perform low complexity detection for the different QAM modulation techniques. The overall evaluation of the proposed research work is done in the Matlab simulation environment from which it is proved that the proposed research method leads to provide better result than the existing research methods.

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Large-Scale MIMO Detection for 3GPP LTE: Algorithms and FPGA Implementations

Cited by 362 publications

References 34 publications

Implicit vs. Explicit Approximate Matrix Inversion for Wideband Massive MU-MIMO Data Detection

Implicit vs. Explicit Approximate Matrix Inversion for Wideband Massive MU-MIMO Data Detection

Distributed state estimation through co‐acting Kalman filters

Bit error rate aware accurate prediction of original signals with low distortion using low complexity detection algorithms

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