Semiblind Iterative Data Detection for OFDM Systems with CFO and Doubly Selective Channels

Data detection for OFDM systems over unknown doubly selective channels (DSCs) and carrier frequency offset (CFO) is investigated. A semiblind iterative detection algorithm is developed based on the expectation-maximization (EM) algorithm. It iteratively estimates the CFO, channel and recovers the unknown data using only limited number of pilot subcarriers in one OFDM symbol. In addition, efficient initial CFO and channel estimates are also derived based on approximated maximum likelihood (ML) and minimum mean square error (MMSE) criteria respectively. Simulation results show that the proposed data detection algorithm converges in a few iterations and moreover, its performance is close to the ideal case with perfect CFO and channel state information.

I. Introduction

Orthogonal frequency division multiplexing (OFDM) has been widely recognized as an efficient transmission technique for wireless communications. In OFDM systems, each subcarrier has a bandwidth narrow enough to experience flat fading, which makes the signal robust against a frequency selective fading channel. However, OFDM systems are known to be sensitive to carrier frequency offset (CFO), and frequency synchronization becomes a critical part in the design of OFDM receivers. In time-invariant frequency-selective channels (at least for one OFDM symbol), the problem of CFO, channel estimation and data detection has been addressed in [1]–[3].