Paper Info
Title
Channel estimation in wireless OFDM systems using reservoir computing
Abstract
Reservoir Computing (RC) is a recent neurologically inspired concept for processing time dependent data that lends itself particularly well to hardware implementation by using the device physics to conduct information processing. In this paper, we apply RC to channel estimation in Orthogonal Frequency Division Multiplexing (OFDM) systems. Due to the multipath propagation environment between a transmitter and receiver, the received signal undergoes attenuation, time delay and phase shift. For mitigating these random effects and decoding the transmitted signal at the receiver, accurate channel estimation is vital. Statistical approaches for channel estimation assume that accurate channel information is available at the receiver. However, the time-variance of the channel complicates the channel estimation process by making the current estimation outdated. Recurrent Neural Networks (RNNs), which are analogous to the functioning of the human brain, are therefore utilized for channel prediction. Training algorithms for RNNs are categorized as gradient-descent methods, which often results in high computational complexity and leads to non-convergence due to the presence of bifurcations. In this paper, an Echo State Network (ESN), which is a class of RC approach, has been used for training a RNN to estimate the channel state information. Using this approach, the training and hence, the implementation complexity is significantly reduced. Simulation results show significant improvement in channel estimation accuracy for the proposed method.
Year
DOI
Venue
2015
10.1109/CISDA.2015.7208638
Computational Intelligence for Security and Defense Applications
Keywords
Field
DocType
OFDM modulation,channel estimation,computational complexity,gradient methods,phase shift keying,radio receivers,radio transmitters,recurrent neural nets,statistical analysis,ESN,RNN training algorithms,channel estimation process,channel prediction,channel state information,computational complexity,device physics,echo state network,gradient-descent methods,hardware implementation,multipath propagation environment,neurologically inspired concept,orthogonal frequency division multiplexing systems,phase shift,receiver,recurrent neural networks,reservoir computing,statistical approach,time delay,transmitter,wireless OFDM systems,Channel Estimation,Echo State Network,Orthogonal Frequency Division Multiplexing,Recurrent Neural Networks,Reservoir Computing
Multipath propagation,Computer science,MIMO,Communication channel,Artificial intelligence,Reservoir computing,Decoding methods,Precoding,Machine learning,Orthogonal frequency-division multiplexing,Channel state information
Conference
ISBN
Citations 
PageRank 
978-1-4673-7556-6
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Wafi Danesh151.19
Chenyuan Zhao2274.57
Bryant T. Wysocki3656.76
Michael J. Medley433726.06
Ngwe Thawdar572.73
Yang Yi615926.70