Joint ML Estimation of CFO and Channel, and a Low Complexity Turbo Equalization Technique for High Mobility OFDMA Uplinks

In this paper, we propose a pilot aided joint estimation technique for carrier frequency offsets (CFOs) and doubly selective channels (DSCs), and a low complexity turbo equalization (TE) scheme for orthogonal frequency division multiple access (OFDMA) uplink systems with high mobility users. We propose the use of Chebyshev polynomials of the first kind (CPF) for accurately representing the DSCs. As the exact solution to the estimation problem is computationally intensive, we make use of space alternating generalized expectation maximization (SAGE) algorithm, that replaces the complex multidimensional search with many one dimensional searches. We derive the exact Cramer-Rao bound (CRB) of the joint estimation problem and the mean squared error of the proposed estimation technique is shown to be closely comparable to it. The convergence characteristics are also studied and it is proved analytically that the proposed method converges. Additionally, we propose a low complexity turbo equalization method with iterative multiple access interference (MAI) cancellation, which uses the soft feedback symbol estimate for removing the MAI and self inter-carrier interference (ICI) in each iteration. Our proposed estimation and equalization technique can be applied to any type of carrier assignment scheme (CAS) in OFDMA and offers very good performance even at high mobile speeds.