Subspace algorithms for the stochastic identification problem
Automatica (Journal of IFAC)
Kalman Filtering and Neural Networks
Kalman Filtering and Neural Networks
Linear System Theory and Design
Linear System Theory and Design
Multi-Carrier Digital Communications: Theory and Applications of Ofdm
Multi-Carrier Digital Communications: Theory and Applications of Ofdm
Autoregressive modeling for fading channel simulation
IEEE Transactions on Wireless Communications
IEEE Communications Magazine
Channel estimation for OFDM systems with transmitter diversity in mobile wireless channels
IEEE Journal on Selected Areas in Communications
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A fading channel identification and tracking method for orthogonal frequency division multiplexing (OFDM) communication system is proposed in this paper. The fading channel is modeled as an auto-regression (AR) process with unknown AR parameters. With the AR model of channel, a state-space representation of the the system is formulated. The system parameters are identified using subspace based method and Kalman filtering. With training symbols and feedback decisions, the system model is simplified to have fixed parameters contrast with other subspace based identification methods. Moreover, QR based subspace method is proposed to solve this problem efficiently. Numerical simulation results show that the proposed method can be applied in fast fading environment.