Pilot-assisted maximum-likelihood frequency-offset estimation for OFDM systems

Abstract

For orthogonal frequency-division multiplexing (OFDM) signals that suffer from frequency-selective fading, we derive the maximum-likelihood (ML) pilot-assisted carrier frequency offset (CFO) estimate and show that most proposals based on repetitive pilot symbols did not use the complete set of sufficient statistics. We convert the problem of obtaining the ML solution from searching exhaustively over the entire uncertainty range to that of solving a spectrum polynomial, thereby greatly reducing the computational load. By properly truncating the polynomial, we obtain a closed-form expression for the corresponding zeros so that the root-searching procedure is greatly simplified. The complexity of locating the desired root is further reduced at almost no expense of performance degradation by an alternate algorithm that uses the fact that the solution is related to the root of a special factor of the polynomial. This alternate method is very attractive for its simplicity and excellent performance that, even at low signal-to-noise ratios (SNRs), is very close to the corresponding Cramér-Rao lower bound. A detailed analysis of the mean-squared error performance is presented and the analysis is validated by simulations.

Frequency estimators for MIMO-OFDM systems

“…This ML solution is similar to that derived in [6] with an extended definition (MR combined) of the correlation matrixR Y Y . It can be proved that using this extended correlation matrix and the transform domain approach of [6] we obtain an ML algorithm similar to that of [6].…”

“…For this case, we can use a procedure similar to that presented in the previous section to derive the corresponding ML estimator. An alternative but more efficient approach for the case of multiple pilot symbols is to apply the transform domain method of [6]. The mth sample of the kth (time-domain) pilot symbol received by the ith receive antenna, y i (k, m), can be represented as…”

“…Its maximum frequency acquisition range is only ±1/2 subcarrier spacing because of the modulo 2π ambiguity. Yu and Su (YS) [6] used a transform domain approach to derive an ML estimator based on multiple (>2) identical pilot symbols and presented very efficient alternatives that give mean squared error (MSE) performance close to the Cramér-Rao bound (CRB).…”

Low-complexity adaptive iteration algorithm for frequency tracking in OFDM systems

“…To secure CFO estimates with high accuracy, the technique using maximum-likelihood (ML) estimation perhaps is the best choice for CFO tracking, securing fine CFO estimates. In [10][11][12][13], the ML CFO estimators are developed. However, they can only work for systems employing repeated preambles.…”

Channel Identification for OFDM Communication System in Frequency Domain

“…If the spatial information is available, the subspace method is the possible choice (Muquet et al, 2002). These algorithms have been utilized in channel estimation and equalization, and have helped to improve the communication performance in applications of equalization (Giannakis et al, 2000), compensation of frequency offset (Yu & Su, 2004), compensation of nonlinearity distortion (Ding et al, 2004), interference compensation in relay station (Shibuya, 2006;Sun & Sano, 2005).…”