Channel Characterization and Modeling for Optical Wireless Body-Area Networks - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue IEEE Open Journal of the Communications Society Année : 2020

Channel Characterization and Modeling for Optical Wireless Body-Area Networks

Oussama Haddad
Mohammad-Ali Khalighi
  • Fonction : Auteur
  • PersonId : 860372
Stanislav Zvanovec
  • Fonction : Auteur
  • PersonId : 1043718
Mouloud Adel

Résumé

We address channel characterization and modeling for medical wireless body-area networks (WBANs) based on the optical wireless technology. We focus on the intra-WBAN communication links, i.e., between a set of medical sensors and a coordination node, placed on the patient's body. We consider a realistic mobility model, e.g., inside a hospital room, which takes into account the effect of shadowing due to body parts movements and the variations of the underlying channels. To take into account the global and local user mobility, we consider a dynamic model based on a three-dimensional animation of a walk cycle, as well as walk trajectories based on an improved random way-point mobility model. Then, Monte Carlo ray-tracing simulations are performed to obtain the channel impulse responses for different link configurations at different instants of the walk scenarios. We then derive first-and second-order statistics of the channel parameters such as the channel DC gain, delay spread, and coherence time, and furthermore, propose best fit statistical models to describe the distribution of these parameters for a general scenario.
Fichier principal
Vignette du fichier
OJCOMS2999104.pdf (3.09 Mo) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte
Loading...

Dates et versions

hal-02903654 , version 1 (21-07-2020)

Identifiants

Citer

Oussama Haddad, Mohammad-Ali Khalighi, Stanislav Zvanovec, Mouloud Adel. Channel Characterization and Modeling for Optical Wireless Body-Area Networks. IEEE Open Journal of the Communications Society, 2020, ⟨10.1109/OJCOMS.2020.2999104⟩. ⟨hal-02903654⟩
39 Consultations
284 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More