Blind joint channel and data estimation for OFDM signals in Rayleigh fading

In this paper, we present several blind model-based joint channel and data estimation methods for Orthogonal Frequency Division Multiplexing (OFDM) signals. We model the time-varying channel response as a polynomial in time. Joint channel estimation and data detection are accomplished by finding the data sequence and regression coefficients that results in the minimum metric between the data-dependent polynomial and the received samples. Our method does not require the information of the channel statistics like signal-to-noise ratio (SNR) or correlation function. Performing exhaustive search among candidate sequences, though optimal, is impractical for long sequences. We develop some suboptimal methods and discuss their pro and con. A two-stage hybrid detection algorithm is proposed and used for detecting differential phase shift keying (DPSK) signals in Rayleigh fading.