A Dominance-Based Soft-Input Soft-Output MIMO Detector With Near-Optimal Performance

Papa, G; Ciuonzo, Domenico; Romano, Gianmarco; Rossi, Pierluigi Salvo

doi:10.1109/tcomm.2014.2367013

Cited by 22 publications

(10 citation statements)

References 51 publications

(97 reference statements)

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“…Recovering multiplexed data from signals received by many antennas in an optimal manner requires tremendously high amount of computations, so the reduction of detection complexity has been a great concern for utilizing the massive MIMO technique in practical communication systems [3,4]. As an approach to reduce the detection complexity, suboptimal linear detection algorithms have been intensively studied [7,[16][17][18][19][20][21][22][23][24][25][26], where matched filter (MF) detection, zero forcing (ZF) detection and minimum mean squared error (MMSE) detection are well known examples. Nevertheless, these linear detection schemes cannot lower the computational complexity of the massive MIMO receiver to an acceptable level because the inversion of high dimensional matrices is still required.…”

Section: Introductionmentioning

confidence: 99%

“…Then, low complexity detection algorithms based on approximate matrix inversion [18,19], low complexity factor graph (FG) based belief propagation (BP) algorithms [20][21][22] and pairwise Markov random fields (MRF) based MIMO detection algorithms [20,21] have been proposed. Tree-searching soft-input soft-output (SISO) MIMO detection algorithms have also been proposed in various forms [23][24][25][26]. The FG based BP detection with Gaussian approximation of interference (GAI), called FG-GAI BP detector, was proposed as one of the promising solutions to reduce the computational complexity of the massive MIMO receiver to the practically allowable level [21,22].…”

Section: Introductionmentioning

confidence: 99%

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LDPC Coded Massive MIMO Systems

Hwang

Park

Lee

2019

Entropy

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We design a coded massive multiple-input multiple-output (MIMO) system using low-density parity-check (LDPC) codes and iterative joint detection and decoding (JDD) algorithm employing a low complexity detection. We introduce the factor graph representation of the LDPC coded massive MIMO system, based on which the message updating rule in the JDD is defined. We devise a tool for analyzing extrinsic information transfer (EXIT) characteristics of messages flowing in the JDD and the three-dimensional (3-D) EXIT chart provides a visualization of the JDD behavior. Based on the proposed 3-D EXIT analysis, we design jointly the degree distribution of irregular LDPC codes and the JDD strategy for the coded massive MIMO system. The JDD strategy was determined to achieve a higher error correction capability with a given amount of computational complexity. It was observed that the coded massive MIMO system equipped with the proposed LDPC codes and the proposed JDD strategy has lower bit error rate than conventional LDPC coded massive MIMO systems.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

LDPC Coded Massive MIMO Systems

Hwang

Park

Lee

2019

Entropy

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“…Therefore, to obtain a channel capacity close to the Shannon limit, a new kind of MIMO system, known as the Turbo-MIMO system that is based on bit-interleaved coded modulation, was investigated by Sellathurai and Haykin [5]. Endowed with turbo learning principle [6], these iterative receivers make detection and decoding by exchanging soft bits information mutually, which lets them approach approximately optimal performance with in a computationally feasible manner [5] [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

An Iterative SISO Improved Complex Sphere Detection and Decoder for Turbo-MIMO Systems

Ni¹,

Hirata²,

Li³

et al. 2019

ijSciences

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An iterative soft-input soft-output (SISO) improved complex sphere detection and decoder algorithm is proposed for signal detection in Turbo-MIMO system. It forms candidate points set Θ in terms of an accumulated cost function based on a search arc constrained by the received signals. Then, the candidate points subset, the lower cost bound of which is not smaller than upper bound, is fathomed and dropped from further consideration. Meanwhile, once a new feasible candidate point is turned up, the path closest to completion is casted upon to generate the set Θ with optimal candidate vectors, aiming to determining the extrinsic information for a Turbo coded bit with most likelihood. Bridged by de-multiplexing and multiplexing, an SISO improved complex sphere detection is concatenated with a SISO Log maximum a posteriori Turbo decoder as if a principal Turbo detection is embedded with a subordinate Turbo decoder, exchanging each other's detection and decoding soft-decision information iteratively. As a result, the proposed algorithm converges rapidly, which results in lower computational complexity. The transfer curves that relate the input mutual information to the output mutual information is achieved through simulations. Thus, an asymptotic interval of the input SNR threshold for the proposed scheme to converge has been observed. Finally, an upper bound of the diversity has been obtained based on the intuitional deduction and theoretical analyses. The simulation results also show that the proposed scheme has a strong ability of anti-multi-stream interference, and its performance is close to that of the iterative soft-input soft-output list complex sphere detection and decoder algorithm, but with a shorter time delay.

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“…Specifically, the complexity for the QRD-M increases exponentially when the modulation order is high, due to tree structures. In [19][20][21][22], many detection algorithms were developed to reduce the complexity for tree search algorithms. In [19][20][21], the proposed algorithms are based on sphere decoding (SD) and K-best decoding.…”

Section: Introductionmentioning

confidence: 99%

“…In [19][20][21][22], many detection algorithms were developed to reduce the complexity for tree search algorithms. In [19][20][21], the proposed algorithms are based on sphere decoding (SD) and K-best decoding. These algorithms have a very lower complexity than the conventional detection scheme and the ML, with similar error performance.…”

Section: Introductionmentioning

confidence: 99%

Linear Approximation Signal Detection Scheme in MIMO-OFDM Systems

Kim

You

et al. 2018

Applied Sciences

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Abstract:In this paper, a linearly approximate signal detection scheme is proposed in multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems. The huge MIMO-OFDM system, which uses many transmit antennas and high order modulation, requires a detection scheme at the receiver with very low complexity for practical implementation. In the proposed detection scheme, one N × N MIMO-OFDM system is divided into N/2 2 × 2 MIMO-OFDM systems for linear increase of complexity. After the partial zero-forcing (ZF), decision feedback equalizer (DFE) and QR decomposition-M algorithm (QRD-M) are applied to each 2 × 2 MIMO-OFDM system. Despite nonlinear detection schemes, the overall complexity of the proposed algorithm increases almost linearly because the DFE and the QRD-M are applied to 2 × 2 MIMO-OFDM systems. Also, the value of M in the QRD-M is fixed according to position of the center point in constellation for efficient signal detection. In simulation results, the proposed detection scheme has higher error performance and lower complexity than the conventional ZF. Also, the proposed detection scheme has very lower complexity than the conventional DFE, with slight loss of error performance.

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A Dominance-Based Soft-Input Soft-Output MIMO Detector With Near-Optimal Performance

Cited by 22 publications

References 51 publications

LDPC Coded Massive MIMO Systems

LDPC Coded Massive MIMO Systems

An Iterative SISO Improved Complex Sphere Detection and Decoder for Turbo-MIMO Systems

Linear Approximation Signal Detection Scheme in MIMO-OFDM Systems

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