Near MAP Dynamical Delay Estimator and Bayesian CRB for Coded QAM Signals
| Autor: | Leila Najjar Atallah, Imen Hamdi Nasr, Sofiane Cherif, Benoit Geller |
|---|---|
| Přispěvatelé: | Ingénierie Systèmes (IS), Unité d'Informatique et d'Ingénierie des Systèmes (U2IS), École Nationale Supérieure de Techniques Avancées (ENSTA Paris)-École Nationale Supérieure de Techniques Avancées (ENSTA Paris), Laboratoire des Sciences des Procédés et des Matériaux (LSPM), Centre National de la Recherche Scientifique (CNRS)-Université Sorbonne Paris Cité (USPC)-Institut Galilée-Université Paris 13 (UP13), Geller, Benoit |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
[INFO.INFO-NI] Computer Science [cs]/Networking and Internet Architecture [cs.NI]
Mean squared error Applied Mathematics 05 social sciences Bayesian probability Estimator 050801 communication & media studies 020206 networking & telecommunications 02 engineering and technology Random walk Synchronization Computer Science Applications QAM [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI] 0508 media and communications Synchronizer ACM: H.: Information Systems UTC offset Statistics 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Algorithm Mathematics |
| Zdroj: | IEEE Transactions on Wireless Communications IEEE Transactions on Wireless Communications, Institute of Electrical and Electronics Engineers, 2018 |
| ISSN: | 1536-1276 |
| Popis: | International audience; This paper presents an off-line algorithm for dy-namical time delay recovery for which the whole observation block is used. The time offset varies over the observation interval following a random walk model. The proposed synchronizer applies to data-aided (DA), non-data-aided (NDA) and code-aided (CA) modes. Theoretical performance of the off-line technique is derived and compared to simulation results. The Bayesian Cramer-Rao Bound (BCRB) is also evaluated for DA, NDA and CA modes and for both the off-line and on-line scenarios. Simulation results show the improvement brought by the off-line and the CA schemes. The presented algorithm outperforms the conventional on-line estimator, which only takes into account the current and previous observations, and its MSE approaches the BCRB. |
| Databáze: | OpenAIRE |
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