Channel Estimation in Wireless OFDM Systems: Interpolation versus ESPRIT

With the rapid growth of digital communication in recent years, the need for high speed data transmission is increased. Moreover, future wireless systems are expected to support a wide range of services which includes video, data and voice. OFDM is a widely standardized candidate for achieving high data rates in mobile environment, due to its resistance to ISI, which is a common problem found in high speed data communication.

In OFDM systems, estimating channel frequency response is essential to successful data recovery. In this thesis, traditional estimation methods using interpolation among pilot measurements are compared to a parametric model (PM)-based channel estimation. This PM-based technique uses the ESPRIT (Estimation of Signal Parameters by Rotational Invariance Techniques) method to estimate time delays of multiple paths in the channel. Then a least-squares estimator is used to estimate the channel frequency response. Performance of these techniques in terms of symbol error rate and time-complexity is compared. Though this subspace method is computationally more expensive, experimental results show that it performs much better than traditional interpolation techniques.

Use of LDPC channel coding to reduce bit error rates in frequency-selective channels is also explored.