A Fixed-Complexity Smart Candidate Adding Algorithm for Soft-Output MIMO Detection

Milliner, D.L.; Zimmermann, Ernesto; Barry, John R.; Fettweis, Gerhard

doi:10.1109/jstsp.2009.2035800

Cited by 44 publications

(40 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Moreover, K-Best decoder suffers similarly as LSD from missing counter hypothesis problem due to the limited list size. Numerous approaches have been proposed to address this problem such as smart candidates adding [51], bit flipping [52], path augmentation and LLR clipping [22,27].…”

Section: K-best Decodermentioning

confidence: 99%

Iterative receivers combining MIMO detection with turbo decoding: performance-complexity trade-offs

Chall

Nouvel

Hélard

et al. 2015

J Wireless Com Network

View full text Add to dashboard Cite

Recently, iterative receiver combining multiple-input multiple-output (MIMO) detection with channel decoding has been widely considered to achieve near-capacity performance and reliable high data rate transmission, for future wireless communication systems. However, such iterative processing increases the computational complexity at the receiver. In this paper, the computational complexity of MIMO detection algorithms combined with turbo decoding is investigated. We first present an overview of the family of MIMO detection algorithms based on sphere decoding, K-Best decoding, and interference cancellation. A recently proposed low-complexity K-Best decoder (LC-K-Best) is also presented. Moreover, we analyze the convergence of combining these detection algorithms with the turbo decoder using the extrinsic information transfer (EXIT) chart. Consequently, a new scheduling order of the number of iterations for the iterative process is proposed. Several system configurations are developed and compared in terms of performance and complexity. Simulations and analytical results show that the new scheduling provides good performance with a large saving in the complexity. Additionally, the LC-K-Best decoder shows a good performance-complexity tradeoff, and it is therefore suitable for parallel and pipeline architectures that can meet high throughput requirements.

show abstract

Section: K-best Decodermentioning

confidence: 99%

Iterative receivers combining MIMO detection with turbo decoding: performance-complexity trade-offs

Chall

Nouvel

Hélard

et al. 2015

J Wireless Com Network

View full text Add to dashboard Cite

show abstract

“…그러나 근사적인 LLR 계산의 복잡도 또한 꽤 높아 서 Ⅲ장에서 설명할 여러 가지 준최적 기법들에 대한 연구들이 광범위하게 수행되었다 [10][11][12][13][14][15][16][17][18] . 알고리즘의 연판정 버전들이 소개되었다 [12] .…”

Section: ⅰ 서 론unclassified

Detection Techniques for High Dimensional Spatial Multiplexing MIMO System

Lim

Kim

Choi

2014

The Journal of Korean Institute of Communications and Informati

View full text Add to dashboard Cite

With the increasing demands on high data rate, there has been growing interests in multi-input multi-output (MIMO) technology based on spatial multiplexing (SM) since it can transmit independent information in each spatial stream. Recent standards such as 3GPP LTE-advanced and IEEE 802.11ac support up to eight spatial streams, and massive MIMO and mm-wave systems that are expected to be included in beyond 4G systems are considering employment of tens to hundreds of antennas. Since the complexity of the optimum maximum likelihood based detection method increases exponentially with the number of antennas, low-complexity SM MIMO detection becomes more critical as the number of antenna increases. In this paper, we first review the results on the detection schemes for SM MIMO systems. In addition, massive MIMO reception schemes based on simple linear filtering which does not require exponential increment of complexity will be explained, followed by brief description on receiver design for future high dimensional SM MIMO systems.

show abstract

“…Some of these soft-output algorithms are Repeated Tree Search (RTS) [57], a modified RTS algorithm [58], Single Tree Search (STS) [59], the List-based SD (LSD) scheme [51], Soft-output Fixed-complexity (SFSD) [60], the Smart Ordering and Candidate Adding (SOCA) algorithms [17], and Soft-output K-BEST [61] [62].…”

Section: Max-log Soft Detectionmentioning

confidence: 99%

“…Thereby, a variation of the BOHD-SO method is presented in this section, which is employed when the decoder provides a priori information to the detector. Instead of running Algorithm 3, we propose to use Bit-Flipping [17] with SIC to build a candidate list. Then, the candidate list is employed to compute the counter-hypothesis distances.…”

Section: Minimum Mean Square Error With Parallel Interference Cancelamentioning

confidence: 99%

“…For a threshold value of K th , the mean complexity of the hybrid precoded can be calculated as 17) where C T , C T HP and C LT HP represent the complexity for the hybrid, THP and LRAP THP precoders respectively, in this dissertation this complexity is measured in terms of flops. For the Gaussian MIMO channel the cumulative density function (cdf) F K (K th ) on (5.17), can be obtained as [87] …”

Section: Hybrid Precoding Schemementioning

confidence: 99%

See 1 more Smart Citation

Effi cient algorithms for iterative detection and decoding in Multiple-Input and Multiple-Output Communication Systems.

Haro¹,

Angeles²

View full text Add to dashboard Cite

A Fixed-Complexity Smart Candidate Adding Algorithm for Soft-Output MIMO Detection

Cited by 44 publications

References 27 publications

Iterative receivers combining MIMO detection with turbo decoding: performance-complexity trade-offs

Iterative receivers combining MIMO detection with turbo decoding: performance-complexity trade-offs

Detection Techniques for High Dimensional Spatial Multiplexing MIMO System

Effi cient algorithms for iterative detection and decoding in Multiple-Input and Multiple-Output Communication Systems.

Contact Info

Product

Resources

About