Discrete state space channel modeling and channel estimation using Kalman filter for OFDMA systems

Authors:
Bhagyesh Balakrishnan;T. K. Geethu;Nithin Govindankutty;Priyanka Pradeep;Vishal Karnani;S. Kirthiga
Affiliations:
Electronics and Communication Department, Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu, India;Electronics and Communication Department, Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu, India;Electronics and Communication Department, Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu, India;Electronics and Communication Department, Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu, India;Electronics and Communication Department, Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu, India;Electronics and Communication Department, Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu, India
Venue:
ICNVS'10 Proceedings of the 12th international conference on Networking, VLSI and signal processing
Year:
2010

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Abstract

In this paper, a communication system using Orthogonal Frequency Division Multiple Access (OFDMA) is implemented. An iterative Kalman filtering algorithm for estimation of the time-variant Rayleigh fast fading channel is proposed. The Rayleigh channel is approximated to be a Jakes process which is modelled using an autoregressive model. An autoregressive (AR) channel model is used to provide the state space estimates necessary for Kalman filter based channel estimation. The Kalman algorithm, using state space concepts, computes the channel matrix which can then be used to estimate the baseband signal transmitted. Since this algorithm uses both pilot sequences and the underlying channel model to estimate the channel, they are more bandwidth efficient compared to only data-based algorithms. The error plots validate the performance of the algorithm.