Adaptive channel estimation based on the interacting multiple model for OFDM systems

Abstract

Propagation environment on a wireless communication usually consists of a large number of scatters that result in multiple propagation paths. Rayleigh fading channel model is a statistical model for the effect of this environment and this model is most applicable when there is no line of sight between the transmitter and the receiver. It has been proposed that the ¯rst-order Markov channel model can be modeled in slowly varying Rayleigh fading channel with additive Gaussian noise. In particular, the first-order Markov channel provides a mathematically tractable model for slowly time-varying channel. In this paper, an adaptive channel estimation algorithm based on the Interacting Multiple Model (IMM) is introduced to estimate the first-order Markov channel coefficients in single-input single-output (SISO) OFDM systems and single-input multiple-output (SIMO) OFDM systems. The IMM algorithm estimates the state of a dynamic system which is characterized by several behavior models and very effective approach when the system has discrete uncertainties in the dynamic or measurement models as well as continuous uncertainties. The Kalman filter, that is a part of the IMM algorithm, is an efficient recursive filter which can also estimate the state of a dynamic system from a series of incomplete and noisy measurements. But the Kalman filter requires observation model for state estimation. Because of this limitation of the Kalman filter, however, the IMM algorithm is used for channel estimation. The proposed algorithm reduces the number of models used in this recursion and avoids the exponential growth complexity caused by increasing channel memory length. The performance of the IMM algorithm is studied and compared with pilot based channel estimation for SISO OFDM systems, and compared with blind channel estimation for SIMO OFDM systems.