Effective Adaptive Iteration Algorithm for Frequency Tracking and Channel Estimation in OFDM Systems
| Autor: | Hong-Yu Liu, Rainfield Y. Yen |
|---|---|
| Rok vydání: | 2010 |
| Předmět: |
Mean squared error
Adaptive algorithm Computer Networks and Communications Orthogonal frequency-division multiplexing Estimation theory Aerospace Engineering Estimator Frequency-division multiplexing Automotive Engineering Frequency offset Electrical and Electronic Engineering Multidimensional systems Algorithm Computer Science::Information Theory Mathematics |
| Zdroj: | IEEE Transactions on Vehicular Technology. 59:2093-2097 |
| ISSN: | 1939-9359 0018-9545 |
| DOI: | 10.1109/tvt.2010.2042738 |
| Popis: | For joint maximum-likelihood (ML) frequency tracking and channel estimation using orthogonal frequency-division multiplexing (OFDM) training blocks in OFDM communications over mobile wireless channels, a major difficulty is the local extrema or multiple-solution complication arising from the multidimensional log-likelihood function. To overcome this, we first obtain crude ML frequency-offset estimators using single-time-slot samples from the received time-domain OFDM block. These crude frequency estimators are shown to have unique closed-form solutions. We then optimally combine these crude frequency estimators in the linear-minimum-mean-square-error (LMMSE) sense for a more accurate solution. Finally, by alternatively updating the LMMSE frequency estimator and the ML channel estimator through adaptive iterations, we successfully avoid the use of a multidimensional log-likelihood function, hence obviating the complex task of global solution search and, meanwhile, achieve good estimation performance. Our estimators have mean square errors (MSEs) tightly close to Cramer-Rao bounds (CRBs) with a wide tracking range. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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