Blind signal detection algorithm for MIMO-OFDM systems over multipath channel using PARALIND model

2011 ◽  
Vol 5 (5) ◽  
pp. 606-611 ◽  
Author(s):  
Z. Xiaofei ◽  
W. Fei ◽  
X. Dazhuan
Keyword(s):  
Signal Detection ◽  
Detection Algorithm ◽  
Multipath Channel ◽  
Ofdm Systems ◽  
Blind Signal ◽  
Mimo Ofdm

A simplified QRD-M signal detection algorithm for MIMO-OFDM systems

2010 ◽  
Vol 27 (1) ◽  
pp. 88-93 ◽  
Author(s):  
Haifang Jian ◽  
Xiaocheng Yao ◽  
Yin Shi
Keyword(s):  
Signal Detection ◽  
Detection Algorithm ◽  
Ofdm Systems ◽  
Mimo Ofdm

Index Coding and Signal Detection in Precoded MIMO-OFDM Systems

2021 ◽  
Author(s):  
Juan P. Mayoral Arteaga ◽  
Rodrigo P. David ◽  
Raimundo Sampaio-Neto
Keyword(s):  
Signal Detection ◽  
Ofdm Systems ◽  
Index Coding ◽  
Mimo Ofdm

scholarly journals A Novel Signal Detection Algorithm for Underwater MIMO-OFDM Systems Based on Generalized MMSE

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Gaoli Zhao ◽  
Jianping Wang ◽  
Wei Chen ◽  
Junping Song
Keyword(s):  
Computational Complexity ◽  
Signal Detection ◽  
Mean Square Error ◽  
High Performance ◽  
Minimum Mean Square Error ◽  
Multipath Fading ◽  
Detection Algorithm ◽  
Mean Square ◽  
Ofdm System ◽  
Mimo Ofdm

The MIMO-OFDM system fully exploits the advantages of MIMO and OFDM, effectively resisting the channel multipath fading and inter-symbol interference while increasing the data transmission rate. Studies show that it is the principal technical mean for building underwater acoustic networks (UANs) of high performance. As the core, a signal detection algorithm determines the performance and complexity of the MIMO-OFDM system. However, low computational complexity and high performance cannot be achieved simultaneously, especially for UANs with a narrow bandwidth and limited data rate. This paper presents a novel signal detection algorithm based on generalized MMSE. First, we propose a model for the underwater MIMO-OFDM system. Second, we design a signal coding method based on STBC (space-time block coding). Third, we realize the detection algorithm namely GMMSE (generalized minimum mean square error). Finally, we perform a comparison of the algorithm with ZF (Zero Forcing), MMSE (minimum mean square error), and ML (Maximum Likelihood) in terms of the BER (bit error rate) and the CC (computational complexity). The simulation results show that the BER of GMMSE is the lowest one and the CC close to that of ZF, which achieves a tradeoff between the complexity and performance. This work provides essential theoretical and technical support for implementing UANs of high performance.

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