ADAPTIVE FILTERING TECHNIQUES FOR CP BASED CHANNEL ESTIMATION IN OFDM SYSTEMS

Abstract

The use of Orthogonal Frequency Division Multiplexing (OFDM) for high rate data transmission over fading dispersive channels has been of wide interest in recent years. OFDM, which already forms a part of several wireless broadcasting standards, is being viewed as a potential candidate for design of upcoming 4G systems. When the intervening channel is doubly selective, the transmitted signal undergoes impairments due to multipath fading and Doppler spreads. In order to facilitate the use of coherent modulation techniques, a receiver has to employ efficient channel estimation schemes. Channel can be estimated by exploiting the Cyclic prefix (CP) as a training sequence in OFDM systems. Although it is appended to ease the equalizer design in the OFDM systems and is normally discarded at the receiver. The channel can be modeled as autoregressive model of order 2 (AR-2) and it can be estimated using Kalman filter which provides an optimal solution when the model parameters, noise characteristics are known a priori and Gaussian. For practical filtering applications, noise may not be Gaussian and its statistics are not known in advance. In such situations, H-infinity filters provide a recursive estimation of the channel in case of unknown noise statistics and it requires the knowledge of AR parameters. Dual filtering techniques are used to estimate the fading channel as well as its AR parameters recursively. In this dissertation work, we have used the state space model approach for deriving the different adaptive filtering algorithms namely Kalman, H-infinity, Dual-Kalman and Dual-H-infinity. The CP based channel estimation is carried out using these adaptive filtering techniques in OFDM systems. For simulation MATLAB is used and it is demonstrated through simulation results that the performance of different adaptive filtering algorithms (H-infinity, Dual-Kalman and Dual-H-infinity) approaches to optimal Kalman filtering algorithm with known parameters. iii