Sensing human activity and/or location using wireless channel received signal strength, aka channel response, has been extensively investigated for a variety of applications. To this end, modeling of wireless channels in dynamic indoor environments typically relies on stochastic channel distributions with constant distribution parameters coupled with site-specific calibration. The Rician distribution with a constant Rician factor is a prominent example. In this paper, we propose and investigate a calibration-less stochastic channel model based on the Rician distribution with a time varying Rician factor, where the variation in time is set to reflect specific dynamic scenarios. We apply this model to experimental data captured from several typical indoor scenarios such as walking, jumping, and entering/exiting a room. We show the proposed model provides a high level of predictability when simulating the channel response compared to experimental data. We also demonstrate how the model can be used to explain captured channel response data in such dynamic indoor environments. The proposed model and modeling approach can be used to test existing methods of location and activity sensing as well as support the development of new methods.
Electrical Engineering (MS)
Department, Program, or Center
Department of Electrical and Microelectronic Engineering (KGCOE)
Sohail A. Dianat
Haag, Benjamin J., "Device-Free Sensing and Modeling of Wireless Channels in Dynamic Indoor Environments" (2022). Thesis. Rochester Institute of Technology. Accessed from
RIT – Main Campus
Available for download on Thursday, December 21, 2023