Efficient Maximum Likelihood Algorithm for Estimating Carrier Frequency Offset of Generalized Frequency Division Multiplexing Systems

Abstract

This study presents a computationally efficient maximum likelihood (ML) algorithm for estimating the carrier frequency offset (CFO) of generalized frequency division multiplexing systems. The proposed algorithm uses repetitive subsymbols and virtual carriers to estimate the fractional and integer CFOs, respectively. Through the use of repetitive subsymbols, this study first calculates the ML estimate of the fractional CFO in the time domain and then, accordingly, compensates for it from the received signal. The integer CFO can then be estimated through a virtual-carrier-mapping process in the frequency domain. In addition to improving performance in terms of estimation accuracy and computational complexity, the proposed non-data-aided algorithm is spectrally efficient relative to traditional algorithms.