ML solutions for resolving frequency ambiguity in OFDM systems

Abstract

The conventional approach for compensating the carrier frequency offset (CFO) of OFDM signals is to estimate the fractional part of the CFO first and then resolve the remaining ambiguity, if exists, by locating the integer part. This paper gives a systematic derivation of the maximum likelihood (ML) integer CFO estimate, that takes into account the effects of channel fading and residual fractional CFO, unifying various earlier proposals. As the complexity of the resulting ML estimate is relatively high, we also suggest various near-optimal and suboptimal combinations and examine their performance. It is shown that, with judicious choice of the component terms of the ML estimate, one can obtain low-complexity integer CFO estimates with excellent performance even in the presence of highly frequency-selective fading.