The University of British Columbia

This paper presents a training signal based improved timing offset estimation method in frequency selective fading channels. A coarse timing offset estimate is first obtained by maximizing a normalized correlation metric. A coarse frequency offset estimate is similarly obtained by a correlation metric. Then a sliding observation vector based maximum likelihood approach (SOV-ML) for joint timing and frequency offset estimation is applied. This SOV-ML requires the knowledge of channel impulse response (CIR). The CIR estimate is usually affected by the coarse timing and frequency offset estimates. The key requirement for SOV-ML is to obtain a CIR estimate which is not affected (or minimally affected) by the coarse timing and frequency offsets. This paper addresses how to obtain this CIR estimate and presents how this channel estimation assists in improving the timing offset estimation. A way of complexity reduction by an adaptive scheme is also presented.