Abstract
A novel stochastic wideband dynamic directional 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 several typical indoor environments. 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 is proposed in order to account for these two effects. The spatio-temporal variations of paths within their lifespan 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. The implementation of the model is detailed and finally, the model validity is evaluated by comparing key statistics of the simulation results with the measurement results.
Original language | English |
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Title of host publication | 14th IEEE Proceedings on Personal, Indoor and Mobile Radio Communications, 2003. PIMRC 2003. |
Publisher | IEEE |
Pages | 670-674 |
Number of pages | 5 |
Volume | 1 |
ISBN (Print) | 0-7803-7822-9 |
DOIs | |
Publication status | Published - 2003 |
Event | 14th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications 2003 - Beijing, China Duration: 7 Sept 2003 → 10 Sept 2003 |
Conference
Conference | 14th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications 2003 |
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Abbreviated title | PIMRC 2003 |
Country/Territory | China |
City | Beijing |
Period | 7/09/03 → 10/09/03 |
Keywords
- Gaussian processes
- spatial domain propert
- power variation
- mobile communication
- low pass filter
- Markov process
- Gaussian probability density function
- telecommunication channels 5.2 GHz
- mobile radio
- low-pass filters
- indoor radio
- Markov processes