Browsing by Author "Yu, JH"

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ML solutions for resolving frequency ambiguity in OFDM systems
(2005)
Yu, JH
;
Jiang, JS
;
Su, YT
;
電信工程研究所
;
Institute of Communications Engineering
The conventional approach for compensating the carrier frequency offset (CFO) of OFDM signals is to estimate the fractional part of the CFO first and then resolve the remaining ambiguity, if exists, by locating the integer part. This paper gives a systematic derivation of the maximum likelihood (ML) integer CFO estimate, that takes into account the effects of channel fading and residual fractional CFO, unifying various earlier proposals. As the complexity of the resulting ML estimate is relatively high, we also suggest various near-optimal and suboptimal combinations and examine their performance. It is shown that, with judicious choice of the component terms of the ML estimate, one can obtain low-complexity integer CFO estimates with excellent performance even in the presence of highly frequency-selective fading.
Pilot-assisted maximum-likelihood frequency-offset estimation for OFDM systems
(2004-11-01)
Yu, JH
;
Su, YT
;
電信工程研究所
;
Institute of Communications Engineering
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 Cramer-Rao lower bound. A detailed analysis of the mean-squared error performance is presented and the analysis is validated by simulations.