Relaxed $K$-Best MIMO Signal Detector Design and VLSI Implementation

Chen, Sizhong; Zhang, Tong; Yan, Xin

doi:10.1109/tvlsi.2007.893621

Cited by 148 publications

(96 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…TVLSI'07 [18] adopts a radius-based pruning strategy and as such does not have a fixed throughput. The implementation achieves a comparable throughput to the proposed detector; however, our implementation outperforms it significantly in terms of the power consumption and energy-efficiency.…”

Section: Resultsmentioning

confidence: 99%

Hardware Implementation of a Low-Power K-Best MIMO Detector Based on a Hybrid Merge Network

Bello

Halak

El-Hajjar

et al. 2018

2018 28th International Symposium on Power and Timing Modeling, Optimization and Simulation (PATMOS)

View full text Add to dashboard Cite

Abstract-Multiple input multiple output (MIMO) technology is anticipated to play a key role in future wireless communications systems. However, one of the main challenges of MIMO technology is the high complexity of the signal detection, which results in a high power consumption at the MIMO receiver. In this paper, we present the hardware implementation of a K-best detector based on a single-stage architecture, targeted at low-rate and lowpower applications. To achieve a low complexity, we optimise the sorting stage of the detector by systematically eliminating redundant comparators. Furthermore, the sorter incorporates different merge algorithms at selected stages in order to reduce the total comparator count. For a 64-QAM 4 × 4 MIMO system, the detector achieves a power consumption of 34 mW using the STMicroelectronics 65 nm CMOS library, which compares favourably with similar works from the literature.

show abstract

Section: Resultsmentioning

confidence: 99%

Hardware Implementation of a Low-Power K-Best MIMO Detector Based on a Hybrid Merge Network

Bello

Halak

El-Hajjar

et al. 2018

2018 28th International Symposium on Power and Timing Modeling, Optimization and Simulation (PATMOS)

View full text Add to dashboard Cite

show abstract

“…Since the upper triangular matrix R is more tractable than the original channel matrix H, many MIMO detection methods use (2) instead of (1) [6], [7]. Figure 1 shows the pseudo-code of the proposed method, where N T =N R =4 is assumed for convenience.…”

Section: Algorithmmentioning

confidence: 99%

An Efficient Soft-Output MIMO Detection Method Based on a Multiple-Channel-Ordering Technique

Im¹

2011

ETRI J

View full text Add to dashboard Cite

In this paper, we propose an efficient soft-output signal detection method for spatially multiplexed multiple-input multiple-output (MIMO) systems. The proposed method is based on the ordered successive interference cancellation (OSIC) algorithm, but it significantly improves the performance of the original OSIC algorithm by solving the error propagation problem. The proposed method combines this enhanced OSIC algorithm with a multiple-channel-ordering technique in a very efficient way. As a result, the log likelihood ratio values can be computed by using a very small set of candidate symbol vectors. The proposed method has been synthesized with a 0.13-μm CMOS technology for a 4×4 16-QAM MIMO system. The simulation and implementation results show that the proposed detector provides a very good solution in terms of performance and hardware complexity. Keywords: MIMO, OSIC, K-best, QRD-M, QRM-MLD.Manuscript received Oct. 1, 2010; revised Mar. 9, 2011; accepted Mar. 23, 2011. Tae Ho Im (phone: +82 10 2971 4008, email: taeho.im@gmail.com), Insoo Park (email: rhyle@naver.com), Hyun Jong Yoo (email: hyunjong.yoo84@gmail.com), Sungwook Yu (email: sungwook@cau.ac.kr), and Yong Soo Cho (corresponding author, email: yscho@cau.ac.kr) are with the School of Electrical and Electronic Engineering, Chung-Ang University, Seoul, Rep. of Korea.http://dx.doi.org/10.4218/etrij.11.0110.0574 I. IntroductionMultiple-input multiple-output (MIMO) communication systems have received tremendous attention because of their high spectral efficiency and near-capacity performance. As a result, MIMO has become a key component in several wireless communication standards, including LTE-Advanced and IEEE 802.16m [1].Multiple antennas can be used to improve the reception reliability by sending the same data (spatial diversity) or to increase data rates by sending different data (spatial multiplexing) [1], [2]. There are several detection methods for spatially multiplexed MIMO systems. The maximum likelihood (ML) algorithm leads to the best error performance, but it involves considerable computational complexity [1]. On the other hand, linear detection methods such as the zeroforcing algorithm or minimum mean-square-error algorithm are quite simple, but they show very poor performance. The ordered successive interference cancellation (OSIC) algorithm reduces the effect of interference signals by eliminating signals that are already detected [2]. Although the OSIC algorithm performs better than linear detection methods, it suffers from the error propagation problem [1], [2].As a result, most recent works have focused on the detection methods that are based on tree searches, which achieve nearoptimal performance but involve significantly less complexity than the original ML method [17]. These methods, however, cannot provide the best solution for soft-decoding MIMO systems because of the empty set problem [12], [13]. The proposed method enhances the OSIC algorithm and combines the algorithm with a multiple-channelordering technique in a very efficien...

show abstract

“…In [9], a K-best detector with K = 5 and real decomposition is implemented. In [4], a relaxed K-best detector with K = 48 is implemented. In [2], a K-best with K = 7 detector is implemented.…”

Section: Asic/fpga/asip Comparisonsmentioning

confidence: 99%

“…As can be seen, the proposed detection algorithm is not only suitable for parallel ASIC implementation but also suitable for GPU-based parallel software implementation. Compared to ASIC/ FPGA/ASIP solutions from [2,4,8,9] for 4 × 4 MIMO systems, our GPU design can achieve comparable or even higher throughput. In summary, the GPU design has more flexibility to support different MIMO system configurations and has the capability to support floating-point signal processing which can eliminate the need for fixed-point design analysis.…”

Section: Asic/fpga/asip Comparisonsmentioning

confidence: 99%

Implementation of a High Throughput Soft MIMO Detector on GPU

Sun

Gupta

et al. 2010

J Sign Process Syst

View full text Add to dashboard Cite

Multiple-input multiple-output (MIMO) significantly increases the throughput of a communication system by employing multiple antennas at the transmitter and the receiver. To extract maximum performance from a MIMO system, a computationally intensive search based detector is needed. To meet the challenge of MIMO detection, typical suboptimal MIMO detectors are ASIC or FPGA designs. We aim to show that a MIMO detector on Graphic processor unit (GPU), a low-cost parallel programmable co-processor, can achieve high throughput and can serve as an alternative to ASIC/FPGA designs. However, careful architecture aware software design is needed to leverage the performance offered by GPU. We propose a novel soft MIMO detection algorithm, multi-pass trellis traversal (MTT), and show that we can achieve ASIC/FPGAlike performance and handle different configurations in software on GPU. The proposed design can be used to accelerate wireless physical layer simulations and to offload MIMO detection processing in wireless testbed platforms.

show abstract

Relaxed $K$-Best MIMO Signal Detector Design and VLSI Implementation

Cited by 148 publications

References 15 publications

Hardware Implementation of a Low-Power K-Best MIMO Detector Based on a Hybrid Merge Network

Hardware Implementation of a Low-Power K-Best MIMO Detector Based on a Hybrid Merge Network

An Efficient Soft-Output MIMO Detection Method Based on a Multiple-Channel-Ordering Technique

Implementation of a High Throughput Soft MIMO Detector on GPU

Contact Info

Product

Resources

About