Signal position-based adaptive QR decomposition-based M detection algorithm for multiple-input multiple-output systems

2011 ◽  
Vol 5 (6) ◽  
pp. 598 ◽  
Author(s):  
H. Yoo ◽  
J. Jeong ◽  
J. Lee
Keyword(s):  
Multiple Input Multiple Output ◽  
Detection Algorithm ◽  
Qr Decomposition ◽  
Multiple Input ◽  
Input Multiple Output

Depth-First QRD-M Detection Algorithm for MIMO Systems

2013 ◽  
Vol 347-350 ◽  
pp. 3478-3481
Author(s):  
Li Liu ◽  
Jin Kuan Wang ◽  
Xin Song ◽  
Yin Hua Han ◽  
Yu Huan Wang
Keyword(s):  
Ordered Set ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Detection Algorithm ◽  
Qr Decomposition ◽  
Detection Performance ◽  
Detection Scheme ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Ml Detection

Maximum likelihood (ML) detection algorithm for multiple input multiple output (MIMO) systems provided the best bit error rate (BER) performance with heavy calculating complexity. The use of QR decomposition with M-algorithm (QRD-M) had been proposed to provide near-ML detection performance and lower calculating complexity. However, its complexity still grew exponentially with increasing dimension of the transmitted signal. To reduce the problem, an improved detection scheme was proposed here. After constructing the tree detecting model of MIMO systems, the ML search of one layer was done, the branch metrics were calculated and sorted, which gave an ordered set of the layer, then depth-first search were used to search the left layers with termination methods. The proposed algorithm provides near QRD-M detection performance.

Improved Stack Algorithm for MIMO Systems

2013 ◽  
Vol 333-335 ◽  
pp. 666-669
Author(s):  
Li Liu ◽  
Jin Kuan Wang ◽  
Xin Song ◽  
Yin Hua Han
Keyword(s):  
Bit Error Rate ◽  
Ordered Set ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Detection Algorithm ◽  
Qr Decomposition ◽  
System Capacity ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Ml Detection

Multiple input multiple output (MIMO) systems could increase wireless communication system capacity enormously. The best optimal detection algorithm for MIMO systems was maximum likelihood (ML) detection algorithm, which could provide the best bit error rate (BER) performance for MIMO systems. However, the computational complexity of ML detection algorithm grew exponentially with the number of transmit antennas and the order of modulation, which resulted in difficult using for practice. A modified MIMO signal detection algorithm which combined ML detection with stack algorithm was presented in this paper. After performing QR decomposition of the channel matrix, the ML detection with length L was done firstly. The partial accumulated metrics were calculated and sorted, which produced an ordered set secondly. Based on the ordered set, stack algorithm was performed to search for the symbol with the minimum accumulated metrics. The proposed algorithm reduced the probability of look back in stack algorithm.

scholarly journals Fast Scalable Architecture of a Near-ML Detector for a MIMO-QSM Receiver

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1509 ◽  
Author(s):  
Ismael Lopez ◽  
L. Pizano-Escalante ◽  
Joaquin Cortez ◽  
O. Longoria-Gandara ◽  
Armando Garcia
Keyword(s):  
Multiple Input Multiple Output ◽  
Low Complexity ◽  
Detection Algorithm ◽  
Spatial Modulation ◽  
Impact Performance ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Receiver Performance ◽  
Limited Precision ◽  
Numerical Precision

This paper presents a proposal for an architecture in FPGA for the implementation of a low complexity near maximum likelihood (Near-ML) detection algorithm for a multiple input-multiple output (MIMO) quadrature spatial modulation (QSM) transmission system. The proposed low complexity detection algorithm is based on a tree search and a spherical detection strategy. Our proposal was verified in the context of a MIMO receiver. The effects of the finite length arithmetic and limited precision were evaluated in terms of their impact on the receiver bit error rate (BER). We defined the minimum fixed point word size required not to impact performance adversely for n T transmit antennas and n R receive antennas. The results showed that the proposal performed very near to optimal with the advantage of a meaningful reduction in the complexity of the receiver. The performance analysis of the proposed detector of the MIMO receiver under these conditions showed a strong robustness on the numerical precision, which allowed having a receiver performance very close to that obtained with floating point arithmetic in terms of BER; therefore, we believe this architecture can be an attractive candidate for its implementation in current communications standards.

scholarly journals A CORDIC based QR Decomposition Technique for MIMO Detection

2018 ◽  
Vol 8 (5) ◽  
pp. 2731
Author(s):  
Shirly Edward A. ◽  
Malarvizhi S.
Keyword(s):  
Complexity Analysis ◽  
Multiple Input Multiple Output ◽  
Qr Decomposition ◽  
Mimo Detection ◽  
Hardware Complexity ◽  
Channel Matrix ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Processing Techniques ◽  
Complex Channel

CORDIC based improved real and complex QR Decomposition (QRD) for channel pre-processing operations in (Multiple-Input Multiple-Output) MIMO detectors are presented in this paper. The proposed design utilizes pipelining and parallel processing techniques and reduces the latency and hardware complexity of the module respectively. Computational complexity analysis report shows the superiority of our module by 16% compared to literature. The implementation results reveal that the proposed QRD takes shorter latency compared to literature. The power consumption of 2x2 real channel matrix and 2x2 complex channel matrix was found to be 12mW and 44mW respectively on the state-of-the-art Xilinx Virtex 5 FPGA.

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