Channel estimation in wireless OFDM systems using reservoir computing

• Published in: 2015 IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA) pp. 1 - 5
• Main Authors: Danesh, Wafi, Chenyuan Zhao, Wysocki, Bryant T., Medley, Michael J., Thawdar, Ngwe N., Yang Yi
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-05-2015
• Subjects: Channel estimation; Decision support systems; Echo State Network; Handheld computers; Hardware; OFDM; Orthogonal Frequency Division Multiplexing; Recurrent neural networks; Reservoir Computing; Training
• ISSN: 2329-6267
• DOI: 10.1109/CISDA.2015.7208638

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 However, the time-variance of the channel complicates the channel estimation process by making the current estimation outdated.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.