Near-Optimal Training-Based Estimation of Frequency Offset and Channel Response in OFDM with Phase Noise

We propose an efficient training-based OFDM channel impulse response (CIR) and carrier frequency offset (CFO) estimation algorithm that addresses the problem of phase noise (PHN), assuming that the PHN has a known prior distribution. The optimal joint estimation of CIR, PHN and CFO was described in an earlier work of ours. In this paper, we focus on the case where a training symbol consists of two identical halves in the time domain, and propose a variant to Moose\´s CFO estimation algorithm that accounts for PHN in CFO estimation. This is followed by an optimal joint CIR and PHN estimation scheme tailored for this "repeating training symbol" setup. It is assumed that the PHN process is Gaussian with known mean and covariance matrix. This encompasses both Wiener PHN and Gaussian PHN. It is shown through simulations that the proposed algorithm performs almost as well as the optimal JCPCE algorithm at much lower complexity. To further reduce the complexity of the proposed scheme, the conjugate gradient (CG) method is used and we show that it can be realized using the Fast Fourier Transform (FFT).