Joint I/Q Mismatch Calibration, Compensation and Channel Equalization Approach for STBC 2 $$\times $$ × 2 MIMO OFDM Transceivers

Advances in Smart Vehicular Technology, Transportation, Communication and Applications - Smart Innovation, Systems and Technologies ◽

10.1007/978-3-319-70730-3_46 ◽

2017 ◽

pp. 388-396

Author(s):

Chih-Feng Wu ◽

Yi-Hung Lin ◽

Muh-Tian Shiue ◽

Jeng-Shyang Pan

Keyword(s):

Channel Equalization ◽

Mimo Ofdm

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.536-537.1751 ◽

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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A Portable Integrated Ship Monitoring Command System

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.536-537.824 ◽

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.

Download Full-text