EURASIP Journal on Wireless Communications and Networking
Instant weighed probability model for time variant channel in IEEE 802.16e scenario
Proceedings of the 2009 International Conference on Wireless Communications and Mobile Computing: Connecting the World Wirelessly
EURASIP Journal on Wireless Communications and Networking - Special issue on advances in propagation modelling for wireless systems
A new spatial path persistency model for TOA-based indoor geolocation
IEEE Communications Letters
Modeling wireless channel delay spread trends
RWS'09 Proceedings of the 4th international conference on Radio and wireless symposium
Spatial dynamics of indoor radio wideband channels
EURASIP Journal on Wireless Communications and Networking
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A novel stochastic wideband dynamic spatio-temporal indoor channel model which incorporates both the spatial and temporal domain properties as well as the dynamic evolution of paths when the mobile moves is proposed based on the concept of a Markov process. The derived model is based on dynamic measurement data collected at a carrier frequency of 5.2 GHz in typical indoor environments. Multipath components are estimated using the super-resolution frequency domain space-alternating generalized expectation maximization algorithm prior to identification of path "birth" and "death" using a new data analysis method. Analysis shows that multiple births and deaths are possible at any instant of time. Furthermore, correlation exists between the number of births and deaths. Thus, an M-step 4-state Markov channel model (MCM) is proposed in order to account for these two effects. The spatio-temporal variations of paths within their lifespans are taken into consideration by the spatio-temporal vector which was found to be well modeled by a Gaussian probability density function while the power variation can be modeled by a simple low-pass filter. In addition, the methodology used to extract the MCM parameters from the measurement data is also presented. Due to the distinction in the birth-death statistics, the model is generalized through segmentation of the measurement runs and can be completely parameterized by several sets of Markov parameters associated with the type of environment and scenario under consideration. The implementation of the model is also detailed and, finally, the model is evaluated by comparing key statistics of the simulation results with the measurement results.