Reservoir Computing Meets Extreme Learning Machine in Real-Time MIMO-OFDM Receive Processing

2022 ◽  
pp. 1-1
Author(s):  
Lianjun Li ◽  
Lingjia Liu ◽  
Zhou Zhou ◽  
Yang Yi
Keyword(s):  
Real Time ◽  
Extreme Learning Machine ◽  
Reservoir Computing ◽  
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.

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