On Frequency Offset Estimation for OFDM

This paper presents a comparative study of Schmidl-Cox (SC) and Morelli-Mengali (MM) algorithms for frequency offset estimation in OFDM, along with a new least squares (LS) and a new modified SC algorithm. All algorithms have comparable accuracy approaching asymptotically the Cramer-Rao bound. The complexity of the LS algorithm is between O(N) and O(N log N) operations, where N is the length of the training sequence, while the complexity of the SC algorithm is between O(N log N) and O(N^2) operations, and the complexity of the MM algorithm is O(N^2) operations. The modified version of the SC algorithm requires only one training sequence as opposed to two required by the original SC algorithm, and significantly reduced O(N log N) complexity. The sensitivity of the three algorithms to quantization of the arg function (the argument of a complex number) is analyzed and quantified. The analysis and simulation results demonstrate that while all considered algorithms can be used with coarse quantization of the arg function, the LS algorithm is least affected and the SC algorithm is most affected by this quantization error.