Signal detection of MIMO-OFDM system based on auto encoder and extreme learning machine

2017 ◽  
Author(s):  
Xin Yan ◽  
Fei Long ◽  
Jingshuai Wang ◽  
Na Fu ◽  
Weihua Ou ◽  
...  
Keyword(s):  
Signal Detection ◽  
Extreme Learning Machine ◽  
Ofdm System ◽  
Mimo Ofdm ◽  
Learning Machine

Channel Equalization of MIMO-OFDM System Based on Extreme Learning Machine

2014 ◽  
Vol 536-537 ◽  
pp. 1751-1757
Author(s):  
Ling Yang ◽  
Ming Ming Nie ◽  
Zi Long Zhong ◽  
Bin Bin Xue ◽  
Na Lv
Keyword(s):  
Extreme Learning Machine ◽  
Channel Equalization ◽  
Error Rates ◽  
Rate Performance ◽  
Ofdm System ◽  
Fast Training ◽  
Supervised Learning Algorithms ◽  
Mimo Ofdm ◽  
Feed Forward Neural Networks ◽  
Learning Machine

This paper proposes a novel and efficient method for channel equalization of MIMO-OFDM system. The method utilizes extreme learning machine (ELM), a class of supervised learning algorithms, to achieve fast training and low bit error rates. The numerical simulation results show that the proposed methods significantly outperform traditional feed-forward neural networks based MIMO-OFDM system equalizers in terms of bit error rate performance.

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.

A Portable Integrated Ship Monitoring Command System

2014 ◽  
Vol 536-537 ◽  
pp. 824-827
Author(s):  
Ming Liu ◽  
Huan Zhang
Keyword(s):  
Channel Equalization ◽  
Error Rates ◽  
Rate Performance ◽  
Ofdm System ◽  
Fast Training ◽  
Supervised Learning Algorithms ◽  
Mimo Ofdm ◽  
Feed Forward Neural Networks ◽  
Simulation Results ◽  
Learning Machine

This paper proposes a novel and efficient method for channel equalization of MIMO- OFDM system. The method utilizes extreme learning machine (ELM), a class of supervised learning algorithms, to achieve fast training and low bit error rates. The numerical simulation results show that the proposed methods significantly outperform traditional feed-forward neural networks based MIMO-OFDM system equalizers in terms of bit error rate performance.

scholarly journals Time Domain Signal Detection for MIMO OFDM

2010 ◽  
pp. 151-158
Author(s):  
SARALA PATCHALA ◽  
T. GNANA PRAKASH ◽  
Dr. S. V. SUBBA RAO ◽  
Dr. K. PADMA RAJU
Keyword(s):  
Signal Detection ◽  
Fading Channels ◽  
Time Domain ◽  
Communication Systems ◽  
Channel Length ◽  
System Model ◽  
Ofdm System ◽  
Channel Matrix ◽  
Time Domain Signal ◽  
Mimo Ofdm

The MIMO techniques with OFDM is regarded as a promising solution for increasing data rates, for wireless access qualities of future wireless local area networks, fourth generation wireless communication systems, and for high capacity, as well as better performance. Hence as part of continued research, in this paper an attempt is made to carry out modelling, analysis, channel matrix estimation, synchronization and simulation of MIMO-OFDM system. A time domain signal detection algorithm can be based on Second Order Statistics (SOS) proposed for MIMO-OFDM system over frequency selective fading channels. In this algorithm, an equalizer is first inserted to reduce the MIMO channels to ones with channel length shorter than or equal to the Cyclic Prefix (CP) length. A system model in which the ith received OFDM block left shifted by j samples introduced. MIMO OFDM system model which uses the equalizer can be designed using SOS of the received signal vector to cancel the most of the Inter Symbol Interference (ISI). The transmitted signals are then detected from the equalizer output. In the proposed algorithm, only 2P (P transmitted antennas / users in the MIMO-OFDM system) columns of the channel matrix need to be estimated and channel length estimation is unnecessary, which is an advantage over an existing algorithms. In addition, the proposed algorithm is applicable for irrespective of whether the channel length is shorter than, equal to or longer than the CP length. Simulation results verify the effectiveness of the proposed algorithm and shows that it out performs the existing one in all cases.

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