@Article{iasc.2020.013920,
AUTHOR = {Divneet Singh Kapoor, Amit Kumar Kohli},
TITLE = {Intelligence-based Channel Equalization for 4x1 SFBC-OFDM Receiver},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {26},
YEAR = {2020},
NUMBER = {3},
PAGES = {439--446},
URL = {http://www.techscience.com/iasc/v26n3/40003},
ISSN = {2326-005X},
ABSTRACT = {This research paper represents an intelligent receiver based on the artificial-neuralnetworks (ANNs) for a 4x1 space-frequency-block-coded orthogonal-frequencydivision-multiplexing (SFBC-OFDM) system, working under slow time-varying 
frequency-selective fading channels. The proposed equalizer directly recovers 
transmitted symbols from the received signal, without the explicit requirement of 
the channel estimation. The ANN based equalizer is modelled by using feedforward 
as well as the recurrent neural-network (NN) architectures, and is trained using 
error backpropagation algorithms. The major focus is on efficiency and efficacy of 
three different strategies, namely the gradient-descent with momentum (GDM), 
resilient-propagation (RProp), and Levenberg-Marquardt (LM) algorithms. The 
recurrent neural network architecture based SFBC-OFDM system is found to be an 
appropriate choice in terms of the low bit-error-rate performance, while using 
different quasi-orthogonal space-time block codes.},
DOI = {10.32604/iasc.2020.013920}
}