JQRPSD Detection with Low Complexity for SDM MIMO Wireless Communication System

Lin, Hsin-Lei; Chen, Hung-Lieh; Chang, Robert Chen-Hao

doi:10.1109/vdat.2007.373228

Cited by 3 publications

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 12 shows the performance of MIMO-OFDM detection with the JQRPSD. The radius size at each stage of the sphere decoder is 15 : R ¼ 2K 2 N R , N R 2 ½Nr; Nr À 1; Nr À 2; . .…”

Section: Simulation and Implementation Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Recursive Qr Decomposition Architecture for Mimo-Ofdm Detection Systems

Chang

Chen

Lin

et al. 2013

J CIRCUIT SYST COMP

View full text Add to dashboard Cite

This paper presents a modi¯ed implementation of QR decomposition for multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) detection based on the Givens rotation method. The QR decomposition hardware is constructed using the coordinate rotation digital computer (CORDIC) algorithm operating with fewer gate counts and lower power consumption than do triangular systolic array (TSA) structures. Accurate signal transmission is essential to wireless communication systems. Thus, a more e®ective data detection algorithm and precise channel estimation method play vital roles in MIMO systems. Implementing data detection with QR decomposition helps reduce the complexity of MIMO-OFDM detection. Implementation results reveal that the proposed recursive QR decomposition (RQRD) architecture has lower clock latency than do TSA structures, and has a smaller hardware area than do GramÀSchmidt structures.

show abstract

“…Figure 12 shows the performance of MIMO-OFDM detection with the JQRPSD. The radius size at each stage of the sphere decoder is 15 : R ¼ 2K 2 N R , N R 2 ½Nr; Nr À 1; Nr À 2; . .…”

Section: Simulation and Implementation Resultsmentioning

confidence: 99%

“…Detecting the joint QR decomposition and partial sphere decoder (JQRPSD) method has six discrete steps 15 : radius constraint of the sphere, successive cancellation detection (SCD), QR decomposition, slicer, tree pruning, and min-search.…”

Section: Mimo Detectionmentioning

confidence: 99%

Recursive Qr Decomposition Architecture for Mimo-Ofdm Detection Systems

Chang

Chen

Lin

et al. 2013

J CIRCUIT SYST COMP

View full text Add to dashboard Cite

show abstract

“…Therefore, the iterative QR-sorted decomposition algorithm is proposed to improve the BER performance. On the other hand, algorithmic, architectural, and arithmetic efforts [5][6][7][8] [9][10] are made to enhance the throughput or to improve the decomposition stability. However, limited study focuses on the parallel processing for the QR decomposition [11] .…”

mentioning

confidence: 99%

A modified sorted-QR decomposition algorithm for parallel processing in MIMO detection

Lai

Chen

Ting

et al. 2009

2009 IEEE International Symposium on Circuits and Systems

View full text Add to dashboard Cite

This paper proposes a modified sorted-QR decomposition algorithm for the high-dimensional multiple-input multipleoutput (MIMO) detection. Due to the growing demands of high-dimension MIMO channels and large number of OFDM subcarriers, the sorted-QR decomposition becomes one of the computational bottlenecks in the QR-based MIMO detection. The proposed Givens-Rotation-based algorithm aims to improve the throughput and the hardware utilization efficiency by relaxing the sorting condition and allowing the cross-column parallel rotation operations. The simulation results show that our proposed parallel algorithm can significantly reduce the minimal computation latency from 28% to 21% of the original algorithm, and the hardware utilization rate can be enhanced from 71% to 90% for 16×16 MIMO using four Given-Rotation processing elements. The detection performance degradation is negligible and better hardware efficiency can be obtained for the larger number of MIMO antennas. I. INTRODUCTIONMultiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) technique has being developed in the next-generation mobile communication systems such as WiMAX IEEE802.16e [1] and 3GPP-LTE [2] standards. In order to improve the spectrum efficiency, more and more antennas are going to be employed, and thus higher transmission data rate can be obtained. Sorted-QR preprocessing in the MIMO detection algorithm is usually applied to optimize the channel processing order so that the better performance can be achieved [3][4]. One-time sorted-QR decomposition algorithm [5] is proposed to reduce the complexity but the performance gain is small. Therefore, the iterative QR-sorted decomposition algorithm is proposed to improve the BER performance. On the other hand, algorithmic, architectural, and arithmetic efforts [5][6][7][8] [9][10] are made to enhance the throughput or to improve the decomposition stability. However, limited study focuses on the parallel processing for the QR decomposition [11] . This paper aims to develop a computationally-efficient parallel QR decomposition algorithm for the high-dimension and high-throughput MIMO system.In this paper, we propose a modified sorted-QR decomposition algorithm for the MIMO detection. This algorithm is based on the Givens-Rotation QR decomposition algorithm. The sorting condition is modified by rearranging the rotation matrix multiplication on the parallel rotation processing elements (PE). This algorithm has better performance than

show abstract