scholarly journals FPGA Implementation of Dynamic Threshold Sphere Detection for MIMO Systems

2006 ◽  
Author(s):  
Kiarash Amiri ◽  
Joseph R. Cavallaro
Keyword(s):  
Mimo Systems ◽  
Fpga Implementation ◽  
Dynamic Threshold ◽  
Sphere Detection

An FPGA Implementation of MML-DFE for Spatially Multiplexed MIMO Systems

2008 ◽  
Vol 55 (7) ◽  
pp. 705-709 ◽  
Author(s):  
Sungwook Yu ◽  
Tae Ho Im ◽  
Chang Hwan Park ◽  
Jaekwon Kim ◽  
Yong Soo Cho
Keyword(s):  
Mimo Systems ◽  
Fpga Implementation

SNR-dependent radius control sphere detection for MIMO systems and relay networks

2013 ◽  
Vol 26 (3) ◽  
pp. 389-402 ◽  
Author(s):  
Shuangshuang Han ◽  
Chintha Tellambura ◽  
Tao Cui
Keyword(s):  
Mimo Systems ◽  
Relay Networks ◽  
Sphere Detection

Iterative Soft-In Soft-Out Sphere Detection for MIMO Systems

2009 ◽  
Author(s):  
Bjorn Mennenga ◽  
Richard Fritzsche ◽  
Gerhard Fettweis
Keyword(s):  
Mimo Systems ◽  
Sphere Detection

scholarly journals FPGA Implementation of ML, ZF and MMSE Equalizers for MIMO Systems

2015 ◽  
Vol 73 ◽  
pp. 226-233 ◽  
Author(s):  
Abdessalem Trimeche ◽  
Anis Sakly ◽  
Abdellatif Mtibaa
Keyword(s):  
Mimo Systems ◽  
Fpga Implementation

Reducing Computational Complexity and Enhancing Performance of IKSD Algorithm for Encoded MIMO Systems

2016 ◽  
Vol 2 (3) ◽  
pp. 636 ◽  
Author(s):  
Mohammed Qasim Sulttan
Keyword(s):  
Computational Complexity ◽  
Maximum Likelihood ◽  
High Performance ◽  
Mimo Systems ◽  
Practical Implementation ◽  
Mimo Detection ◽  
Detection Algorithms ◽  
Sphere Detection ◽  
Manhattan Metric ◽  
Main Challenge

<p>The main challenge in MIMO systems is how to design the MIMO detection algorithms with lowest computational complexity and high performance that capable of accurately detecting the transmitted signals. In last valuable research results, it had been proved the Maximum Likelihood Detection (MLD) as the optimum one, but this algorithm has an exponential complexity especially with increasing of a number of transmit antennas and constellation size making it an impractical for implementation. However, there are alternative algorithms such as the K-best sphere detection (KSD) and Improved K-best sphere detection (IKSD) which can achieve a close to Maximum Likelihood (ML) performance and less computational complexity. In this paper, we have proposed an enhancing IKSD algorithm by adding the combining of column norm ordering (channel ordering) with Manhattan metric to enhance the performance and reduce the computational complexity. The simulation results show us that the channel ordering approach enhances the performance and reduces the complexity, and Manhattan metric alone can reduce the complexity. Therefore, the combined channel ordering approach with Manhattan metric enhances the performance and much reduces the complexity more than if we used the channel ordering approach alone. So our proposed algorithm can be considered a feasible complexity reduction scheme and suitable for practical implementation.</p>

scholarly journals Deep-Learning-Based Non-Coherent DPSK Multiple-Symbol Differential Detection in Single-User Massive MIMO Systems

2021 ◽  
Author(s):  
Omnia Mahmoud ◽  
Ahmed El-Mahdy ◽  
Robert F. H. Fischer
Keyword(s):  
Deep Learning ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Mimo System ◽  
Decision Feedback ◽  
Differential Detection ◽  
Differential Phase Shift ◽  
Detection Techniques ◽  
Sphere Detection ◽  
Single User

<div>In this work, non-coherent massive MIMO differential phase-shift keying modulation (DPSK) detection is considered to get rid of the complexity of channel estimation. However, most of the well-performing DPSK detection techniques require high computational complexity at the receiver. The use of deep-learning is proposed for detecting the transmitted DPSK symbols over a single-user massive MIMO system. We provide a multiple-symbol differential detection implementation using deep-learning. Two deep-learning-based multiple-symbol differential detection receiver designs are proposed and compared with differential detection (DD), decision-feedback differential detection (DFDD), and multiple-symbol differential detection (MSDD) for the same system parameters. Where multiple-symbol differential sphere detection (MSDSD) is used to implement MSDD. The results show that the proposed deep-learning-based classification neural networks outperform decision-feedback differential detection and achieve an optimal performance compared to conventional multiple-symbol differential detection implemented by multiple-symbol differential sphere detection.</div>

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