Frequency tracking by method of least squares combined with channel estimation for OFDM over mobile wireless channels

[[abstract]]To track frequency offset and time-varying channel in orthogonal frequency division multiplexing (OFDM) systems over mobile wireless channels, a common technique is, based on one OFDM training block sample, to apply the maximum-likelihood (ML) algorithm to perform joint frequency tracking and channel estimation employing some adaptive iteration processes. The major drawback of such joint estimation techniques is the local extrema problem arising from the highly nonlinear nature of the log-likelihood function. This makes the joint estimation process very difficult and complicated, and many a time the results are not very satisfactory if the algorithm is not well designed. In this study, rather than using the ML algorithm, we shall apply the method of least squares (LS) for frequency tracking utilizing repeated OFDM training blocks. As will be seen, by using such an LS approach, the frequency offset estimation requires no channel knowledge. The channel state can be estimated separately after the LS frequency offset correction. This not only circumvents the local extrema complication, but also obviates the need for the lengthy adaptive iteration process of joint estimation thus greatly simplifies the entire estimation process. Most importantly, our technique can achieve excellent estimation performance as compared to the usual ML algorithms.[[incitationindex]]SCI[[booktype]]紙

Iterative joint frequency offset and channel estimation for OFDM systems using first and second order approximation algorithms

[[abstract]]To implement an algorithm for joint estimation of carrier frequency offset (CFO) and channel impulse response (CIR) in orthogonal frequency division multiplexing (OFDM) systems, the maximum-likelihood criterion is commonly adopted. A major difficulty arises from the highly nonlinear nature of the log-likelihood function which renders local extrema or multiple solutions for the CFO and CIR estimators. Use of an approximation method coupled with an adaptive iteration algorithm has been a popular approach to ease problem solving. The approximation used in those existing methods is usually of the first order level. Here, in addition to a new first order approximation method, we also propose a second order approximation method. Further, for the part of the adaptive iteration algorithm, we adopt a new technique which will enable performance improvement. Our first order approximation method is found to outperform the existing ones in terms of estimation accuracies, tracking range, computation complexity, and convergence speed. As expected, our second order approximation method provides an even further improvement at the expense of higher computation complication.[[notice]]補正完畢[[journaltype]]國外[[incitationindex]]SCI[[booktype]]紙本[[booktype]]電子版[[countrycodes]]DE