Channel Estimation in Multicarrier Communication Systems

Abstract

The data rate and spectrum efficiency of wireless mobile communications have been significantly improved over the last decade or so. Recently, the advanced systems such as 3GPP LTE and terrestrial digital TV broadcasting have been
sophisticatedly developed using OFDM and CDMA technology. In general, most mobile communication systems transmit bits of information in the radio space to the receiver. The radio channels in mobile radio systems are usually multipath fading channels, which cause inter-symbol interference (ISI) in the received signal. To remove ISI from the signal, there is a need of strong equalizer which requires knowledge on the channel impulse response (CIR).This is primarily provided by a separate channel estimator. Usually the channel estimation is based on the known sequence of bits, which is unique for a certain transmitter and which is repeated in every transmission burst. Thus, the channel estimator is able to estimate CIR for each burst separately by exploiting the known transmitted bits and the corresponding received samples.
In this thesis we investigate and compare various efficient channel estimation schemes for OFDM systems which can also be extended to MC DS-CDMA systems.The channel estimation can be performed by either inserting pilot tones into all subcarriers of OFDM symbols with a specific period or inserting pilot tones into each OFDM symbol. Two major types of pilot arrangement such as block type and comb type
pilot have been focused employing Least Square Error (LSE) and Minimum Mean Square Error (MMSE) channel estimators. Block type pilot sub-carriers is especially suitable for slow-fading radio channels whereas comb type pilots provide
better resistance to fast fading channels. Also comb type pilot arrangement is sensitive to frequency selectivity when comparing to block type arrangement. However, there is
another supervised technique called Implicit Training (IT) based channel estimation which exploits the first order statistics in the received data, induced by superimposing
periodic training sequences with good correlation properties, along with the information symbols. Hence, the need for additional time slots for training the equalizer is avoided. The performance of the estimators is presented in terms of the mean square estimation error (MSEE) and bit error rate (BER).