Joint ML Estimation of Carrier Frequency, Channel, I/Q Mismatch, and DC Offset in Communication Receivers

This paper proposes a technique that jointly estimates the I/Q mismatch, dc offset, carrier frequency offset, and channel for receivers in frequency selective channels. The joint estimator is a data-aided technique that requires a training sequence and is derived based on the maximum likelihood (ML) criterion. The characteristics of the joint estimator are analyzed. In particular, it is shown that its accuracy almost achieves the Crame/spl acute/r-Rao lower bound (CRLB). The advantage of the proposed estimator over an existing method is demonstrated through computer simulation.