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On the inclusion of channel's time dependence in a hidden Markov model for blind channel estimation
Antón Haro, Carles; Rodríguez Fonollosa, José Adrián; Faulí Prats, Claudio; Rodríguez Fonollosa, Javier
Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions; Universitat Politècnica de Catalunya. SPCOM - Grup de Recerca de Processament del Senyal i Comunicacions
In this paper, the theory of hidden Markov models (HMM) isapplied to the problem of blind (without training sequences) channel estimationand data detection. Within a HMM framework, the Baum–Welch(BW) identification algorithm is frequently used to find out maximum-likelihood (ML) estimates of the corresponding model. However, such a procedureassumes the model (i.e., the channel response) to be static throughoutthe observation sequence. By means of introducing a parametric model fortime-varying channel responses, a version of the algorithm, which is moreappropriate for mobile channels [time-dependent Baum-Welch (TDBW)] isderived. Aiming to compare algorithm behavior, a set of computer simulationsfor a GSM scenario is provided. Results indicate that, in comparisonto other Baum–Welch (BW) versions of the algorithm, the TDBW approachattains a remarkable enhancement in performance. For that purpose, onlya moderate increase in computational complexity is needed.
Peer Reviewed
Àrees temàtiques de la UPC::Enginyeria electrònica i telecomunicacions::Processament del senyal
Mobile communication systems
Markov processes
Blind channel estimation
Blind data detection
Hidden Markov models
Mobile channels
Cellular radio
Computational complexity
Fading channels
Maximum likelihood estimation
Parameter estimation
Signal detection
Channel time dependence
Baum-Welch algorithm
Observation sequence
Parametric model
Time-varying channel response
Computer simulations
Comunicacions mòbils
Processos de Markov
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