dc.contributor.author | Olama, M. M. | en |
dc.contributor.author | Djouadi, S. M. | en |
dc.contributor.author | Charalambous, Charalambos D. | en |
dc.creator | Olama, M. M. | en |
dc.creator | Djouadi, S. M. | en |
dc.creator | Charalambous, Charalambos D. | en |
dc.date.accessioned | 2019-04-08T07:47:31Z | |
dc.date.available | 2019-04-08T07:47:31Z | |
dc.date.issued | 2009 | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/44414 | |
dc.description.abstract | Mobile-to-mobile networks are characterized by node mobility that makes the propagation environment time varying and subject to fading. As a consequence, the statistical characteristics of the received signal vary continuously, giving rise to a Doppler power spectral density (DPSD) which varies from one observation instant to the next. The current models do not capture and track the time varying characteristics. This paper is concerned with dynamical modeling of time varying mobile-to-mobile channels, parameter estimation and identification from received signal measurements. The evolution of the propagation environment is described by stochastic differential equations, whose parameters can be determined by approximating the band-limited DPSD using the Gauss-Newton method. However, since the DPSD is not available online, we propose to use a filter-based expectation maximization algorithm and Kalman filter to estimate the channel parameters and states, respectively. The scheme results in a finite dimensional filter which only uses the first and second order statistics. The algorithm is recursive allowing the inphase and quadrature components and parameters to be estimated online from received signal measurements. The algorithms are tested using experimental data collected from moving sensor nodes in indoor and outdoor environments demonstrating the method's viability. © 2006 IEEE. | en |
dc.source | IEEE Transactions on Wireless Communications | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-65949102543&doi=10.1109%2fTWC.2009.071068&partnerID=40&md5=765c8c17014d8faee83baa2c4b421cb8 | |
dc.subject | Kalman filter | en |
dc.subject | Optimization | en |
dc.subject | Random processes | en |
dc.subject | Differential equations | en |
dc.subject | Stochastic control systems | en |
dc.subject | Stochastic differential equations | en |
dc.subject | Measurement theory | en |
dc.subject | Wireless networks | en |
dc.subject | Mobile telecommunication systems | en |
dc.subject | Signal processing | en |
dc.subject | Parameter estimation | en |
dc.subject | Control theory | en |
dc.subject | Maximum principle | en |
dc.subject | Fading channels | en |
dc.subject | Kalman filters | en |
dc.subject | Fading (radio) | en |
dc.subject | Sensor nodes | en |
dc.subject | Telecommunication equipment | en |
dc.subject | Estimation and identification | en |
dc.subject | Doppler spectral density | en |
dc.subject | Expectation maximization | en |
dc.subject | Mulipath fading channels | en |
dc.subject | Newton-raphson method | en |
dc.subject | Time varying networks | en |
dc.title | Stochastic differential equations for modeling, estimation and identification of mobile-to-mobile communication channels | en |
dc.type | info:eu-repo/semantics/article | |
dc.identifier.doi | 10.1109/TWC.2009.071068 | |
dc.description.volume | 8 | |
dc.description.issue | 4 | |
dc.description.startingpage | 1754 | |
dc.description.endingpage | 1763 | |
dc.author.faculty | Πολυτεχνική Σχολή / Faculty of Engineering | |
dc.author.department | Τμήμα Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών / Department of Electrical and Computer Engineering | |
dc.type.uhtype | Article | en |
dc.source.abbreviation | IEEE Trans.Wireless Commun. | en |
dc.contributor.orcid | Charalambous, Charalambos D. [0000-0002-2168-0231] | |
dc.gnosis.orcid | 0000-0002-2168-0231 | |