Recurrent neural network based BER prediction for NLOS channels

Authors:
Gowrishankar;P. S. Satyanarayana
Affiliations:
B.M.S. College of Engineering, Bangalore, India;B.M.S. College of Engineering, Bangalore, India
Venue:
Mobility '07 Proceedings of the 4th international conference on mobile technology, applications, and systems and the 1st international symposium on Computer human interaction in mobile technology
Year:
2007

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Abstract

Bit error rate (BER) will enumerate the Channel State Information (CSI) in wireless network. Accurate and timely estimation of CSI will guarantee the Quality of Service (QoS) by admission control, inter and intra network handovers. Here the BER of time varying Non line of Sight (NLOS) channels are predicted by neural network system. The wireless channel is modeled as time variant nonlinear system. The neural network systems are the best suitable paradigm to predict and analyze the behaviors of time varying nonlinear system. In this framework BER is predicted by two different recurrent neural network architectures such as Recurrent Radial Basis Function Network (RRBFN) and Echo state network (ESN).