Efficient SIC-MMSE MIMO detection with three iterative loops

Ahmed, Saleem; Kim, Sooyoung

doi:10.1016/j.aeue.2016.11.015

Cited by 20 publications

(16 citation statements)

References 13 publications

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“…In the last decade, cooperative and multiple-input multiple-output (MIMO) techniques have been extensively studied as their improvements in performance do not require additional power or frequency spectrum [1][2][3][4][5][6][7][8][9][10][11][12][13]. In this work, the performance of existing linear and nonlinear decoders [2,[14][15][16][17][18][19][20] for MIMO systems is compared with the newly proposed decoder that is particularly suitable for implementation on software-defined-radio architectures. The maximum likelihood (ML) decoder is the optimal detector for MIMO systems [2,15].…”

Section: Introductionmentioning

confidence: 99%

“…The ML detection proves to be optimal, however, at the cost of high complexity which increases exponentially with the increase of the modulation size and the number of transmit antennas [15,16]. On the other hand, linear detectors such as the zero forcing (ZF) and minimum mean squared error (MMSE) detectors are the simplest and widely used detectors with reasonably lower bit error rate (BER) performances at very low computational complexity [2,4,17,18]. Correspondingly, the vertical Bell laboratories layered space-time (V-BLAST) technique uses an iterative detector that implements the concept of successive interference cancellation (SIC) to find a good trade-off between complexity and performance [2,[18][19][20].…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, linear detectors such as the zero forcing (ZF) and minimum mean squared error (MMSE) detectors are the simplest and widely used detectors with reasonably lower bit error rate (BER) performances at very low computational complexity [2,4,17,18]. Correspondingly, the vertical Bell laboratories layered space-time (V-BLAST) technique uses an iterative detector that implements the concept of successive interference cancellation (SIC) to find a good trade-off between complexity and performance [2,[18][19][20]. SIC decoder can be further improved by incorporating appropriate ordering of the symbols, i.e., first decoding the symbols that exhibit small estimation error before detecting the weaker symbols.…”

Section: Introductionmentioning

confidence: 99%

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A computationally efficient detector for MIMO systems

Alabed

2019

IJECE

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<p>In this work, a newly designed multiple-input multiple-output (MIMO) detector for implementation on software-defined-radio platforms is proposed and its performance and complexity are studied. In particular, we are interested in proposing and evaluating a MIMO detector that provides the optimal trade-off between the decoding complexity and bit error rate (BER) performance as compared to the state of the art detectors. The proposed MIMO decoding technique appears to find the optimal compromise between competing interests encountered in the implementation of advanced MIMO detectors in practical hardware systems where it i) exhibits deterministic decoding complexity, i.e., deterministic latency, ii) enjoys a good complexity–performance trade-off, i.e., it keeps the complexity considerably lower than that of the maximum likelihood detectors with almost optimal performance, iii) allows fully parameterizable performance to complexity trade-off where the performance (or complexity) of the MIMO detector can be adaptively adjusted without the requirement of changing the implementation, iv) enjoys simple implementation and fully supports parallel processing, and v) allows simple and efficient extension to soft-bit output generation for support of turbo decoding. From the simulation results, the proposed MIMO decoding technique shows a substantially improved complexity–performance trade-off as compared to the state of the art techniques.</p>

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A computationally efficient detector for MIMO systems

Alabed

2019

IJECE

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“…It can greatly increase the PD in shadowing channels. CSS is classified into three categories based on how SUs share their sensing data in network [5]: centralized [1,6], distributed [7] and relay assisted [3,8]. In centralized category information from different SUs is combined in central base station to make a final decision, which is used here.…”

Section: Introductionmentioning

confidence: 99%

Efficient error detection in soft data fusion for cooperative spectrum sensing

Bhatti

Ahmed

Saeed

et al. 2018

AEU - International Journal of Electronics and Communications

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The primary objective of cooperative spectrum sensing (CSS) is to determine whether a particular spectrum is occupied by a licensed user or not, so that unlicensed users called secondary users (SUs) can utilize that spectrum, if it is not occupied. For CSS, all SUs report their sensing information through reporting channel to the central base station called fusion center (FC). During transmission, some of the SUs are subjected to fading and shadowing, due to which the overall performance of CSS is degraded. We have proposed an algorithm which uses error detection technique on sensing measurement of all SUs. Each SU is required to re-transmit the sensing data to the FC, if error is detected on it. Our proposed algorithm combines the sensing measurement of limited number of SUs. Using Proposed algorithm, we have achieved the improved probability of detection (PD) and throughput. The simulation results compare the proposed algorithm with conventional scheme.

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“…Recently, a number of researches reported results on minimum mean square error (MMSE) based MIMO detection schemes with soft iterative processes, due to their reasonable performance and complexity trade-offs [2] [3]. The parallel interference cancellation with MMSE (PIC-MMSE) MIMO detection schemes were proposed in order to enhance the performance as well as the computational efficiency compared to the conventional MMSE-based scheme [4] [5] [6].…”

Section: Introductionmentioning

confidence: 99%

Soft Iterative Linear Detection for LDPC Coded MIMO Scheme with PSK

Zhang¹,

Kim²

2017

IJCNS

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A number of study results demonstrated that the performance of the coded MIMO scheme can be highly enhanced by incorporating iterative decoding and detection scheme by exchanging soft information between the symbol detector and decoder. One of the critical problems of these iterative schemes is an exponential order of the complexity with increase of number of bits in a symbol and the number of antennas. In this paper, we present an efficient iterative detection and decoding scheme for MIMO systems with phase shift keying (PSK) modulation schemes and low density parity check (LDPC) codes. In order to reduce the complexity by the number of antennas, we adopt minimum mean square error (MMSE) based linear detection scheme with parallel interference cancellation. In addition, soft bit estimation is made only with a single distance calculation per bit, with approximating performance to the maximum likelihood detection 1 .

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Efficient SIC-MMSE MIMO detection with three iterative loops

Cited by 20 publications

References 13 publications

A computationally efficient detector for MIMO systems

A computationally efficient detector for MIMO systems

Efficient error detection in soft data fusion for cooperative spectrum sensing

Soft Iterative Linear Detection for LDPC Coded MIMO Scheme with PSK

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