A Low-Complexity Semi-Blind Joint CFO and Data Estimation Algorithm for OFDM Systems

In this paper, we propose a low-complexity semi-blind joint carrier frequency offset (CFO) and data estimation algorithm for orthogonal frequency division multiplexing (OFDM) systems. Given channel information, we first provide a new iterative algorithm which jointly estimates the CFO and data based on pilots by minimizing the mean square error between the received OFDM symbol and its regenerated signal. By using the matrix inversion lemma, the joint CFO and data estimator is divided into a CFO estimator and a data detector without loss of optimality, which significantly reduces the computational complexity. Also, we present a decision feedback strategy to select reliable data from previously detected data by adopting the probability metric which evaluates the reliability. Then, the simplified CFO estimator can utilize the selected reliable data as pilots in the next iteration step. Simulation results show that the simplified CFO estimator can achieve the average Cramer Rao bound in moderate and high signal to noise ratio (SNR) regions within a few iterations even for a small number of pilots with the help of the proposed decision feedback strategy.

I. Introduction

Orthogonal frequency division multiplexing (OFDM) is the most popular scheme for broadband communications because of its high spectral efficiency and simple receiver implementation [1]. However, it has been shown that the OFDM is sensitive to carrier frequency offset (CFO) which comes from Doppler shifts or transceiver oscillator instabilities. The CFO causes inter-carrier interference (ICI) among subcarriers, which results in severe bite error rate (BER) performance degradation [2]. Thus, many algorithms have been proposed to estimate the CFO in OFDM for decades [3]–[11].