A nondata-aided SNR estimation technique for multilevel modulations exploiting signal cyclostationarity

Signal-to-noise ratio (SNR) estimators of linear modulation schemes usually operate at one sample per symbol at the matched filter output. In this paper we propose a new method for estimating the SNR in the complex additive white Gaussian noise (AWGN) channel that operates directly on the oversample...

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Detalles Bibliográficos
Autores: Riba Sagarra, Jaume|||0000-0002-5515-8169, Villares Piera, Nemesio Javier|||0000-0001-5701-9819, Vázquez Grau, Gregorio|||0000-0002-3007-6247
Tipo de recurso: artículo
Fecha de publicación:2010
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/177798
Acceso en línea:https://hdl.handle.net/2117/177798
https://dx.doi.org/10.1109/TSP.2010.2059017
Access Level:acceso abierto
Palabra clave:Signal processing
Cyclostationarity
SNR estimation
Second-order methods
Spectral coherence
Rate of innovation
Tractament del senyal
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal
Descripción
Sumario:Signal-to-noise ratio (SNR) estimators of linear modulation schemes usually operate at one sample per symbol at the matched filter output. In this paper we propose a new method for estimating the SNR in the complex additive white Gaussian noise (AWGN) channel that operates directly on the oversampled cyclostationary signal at the matched filter input. Exploiting cyclostationarity proves to be advantageous due to the fact that a signal-free Euclidean noise subspace can be identified such that only second order moments of the received waveform need to be computed. The proposed method is nondata-aided (NDA), as well as constellation and phase independent, and only requires prior timing synchronization to fully exploit the cyclostationarity property. The estimator can also be applied to nonconstant modulus constellations without requiring any tuning, which is a feature not found in existing approaches. Implementation aspects and simpler suboptimal solutions are also provided.