Simplified ML detection scheme for MIMO systems

2005 ◽  
Author(s):  
Jee Woong Kang ◽  
Kwang Bok Ed
Keyword(s):  
Mimo Systems ◽  
Detection Scheme ◽  
Ml Detection

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.

A new reduced complexity ML detection scheme for MIMO systems

2010 ◽  
Vol 58 (4) ◽  
pp. 1302-1310 ◽  
Author(s):  
Jin-Sung Kim ◽  
Sung-Hyun Moon ◽  
Inkyu Lee
Keyword(s):  
Mimo Systems ◽  
Detection Scheme ◽  
Reduced Complexity ◽  
Ml Detection

scholarly journals Advanced Noncoherent Detection in Massive MIMO Systems via Digital Beamspace Preprocessing

Telecom ◽  
2020 ◽  
Vol 1 (3) ◽  
pp. 211-227
Author(s):  
Stephan Bucher ◽  
Christian Waldschmidt
Keyword(s):  
Mimo Systems ◽  
Near Field ◽  
Multiple Input Multiple Output ◽  
Low Complexity ◽  
Base Station ◽  
Coherent Detection ◽  
Noncoherent Detection ◽  
Detection Scheme ◽  
Channel Knowledge ◽  
Residual Performance

Noncoherent detection in massive multiple-input/multiple-output (MIMO) uplink systems provides a low-complexity alternative to its coherent counterpart. Requiring no actual channel knowledge but the per-user induced power at the base station, comparable performance as channel-estimation-based detection can be achieved when the users are located in the near-field of the base station. However, noncoherent detection fails in scenarios where users are in the far-field due to an insufficient capability to separate the users in terms of their spatially induced power. For this purpose, a dielectric lens or an analog beamforming structure can be employed, which are capable to focus the power of the incident waves to a smaller subset of the antennas at the base station. These so-called analog beamspace techniques have been demonstrated to enable again the noncoherent detection scheme. Analogous to a spatial Fourier transform, beamspace techniques can be also realized in the digital domain offering more flexibility. Its applicability to noncoherent detection is studied in this paper. It is shown numerically that by means of digital beamspace preprocessing, considerable performance gains can be achieved. Applied in dominant line-of-sight channels, a large number of users can be accommodated and the residual performance gap to coherent detection with perfect channel knowledge is minimal.

Sorted QR-Decomposition Based Parallel Detection Algorithm for MIMO Systems

2013 ◽  
Vol 427-429 ◽  
pp. 591-595
Author(s):  
Li Liu ◽  
Jin Kuan Wang ◽  
Xin Song ◽  
Ying Hua Han ◽  
Dong Mei Yan
Keyword(s):  
Maximum Likelihood ◽  
Interference Cancellation ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Detection Algorithm ◽  
Qr Decomposition ◽  
Detection Performance ◽  
System Capacity ◽  
Parallel Detection ◽  
Ml Detection

Multiple input multiple output (MIMO) wireless communication system can increase system capacity enormously. Maximum likelihood (ML) detection algorithm can obtain the optimal detection performance with exponential computational complexity that results it difficulty to use in practice. Classical ordered successive interference cancellation (SIC) algorithm suffers from error propagation and high complexity, so an improved parallel SIC algorithm based on Maximum likelihood (ML) detection is proposed, in which signal detection is performed at two stages. ML detections for one layer is carried out firstly, and redundancy of candidate sequences are selected to perform parallel detection for improving detection performance for next step. Sorted QR decomposition based SIC algorithm are performed in second step in order to reduce calculating complexity. By adjusting the number of candidate sequences, tradeoff between detection performance and calculating complexity can be obtained properly.

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