Modeling Effects of Human Bodies on Indoor Wireless Systems

Modeling Effects of Human Bodies on Indoor Wireless Systems

April 27, 2018
Researchers approximated the effects of a human body on RF/microwave fading in an indoor environment to develop a dynamic channel model.

As applications for wireless signals continue to grow, signal propagation is taking place both indoors and outdoors, with indoor wireless systems having to operate among numerous interfering objects, including human bodies. Indoor applications are seeing a steady stream of new devices, including safety sensors and other Internet of Things (IoT) wireless monitors that can be tracked via wireless local area networks (WLANs). However, humans sometimes get in the way—literally. Human bodies can serve as interference for indoor wireless channels, resulting in absorption of limited signal power and changes in signal time and phase.

Fortunately, researchers at the Norwegian University of Science and Technology, Trondheim, developed a dynamic channel model for indoor wireless communications systems. It’s based not on statistical evidence, but rather on measured results, showing the different conditions where someone passing through a line-of-sight (LOS) indoor wireless signal could cause significant signal fading.

Drawing from previous studies on the behavior of conducting cylinders at microwave frequencies, in particular a single-cylinder interference model of the human body,

The researchers were able to approximate the effects of a human body on RF/microwave fading in an indoor environment. But the previous model, with only one cylinder, was considered an oversimplification of the signal absorption and reflection brought about by a human body with its many moving parts, and when a human body is moving through an indoor electromagnetic (EM) operating environment.

For a more realistic human-body EM model, the researchers assumed a human-body model with 12 moving parts and represented it with 12 dielectric cylinders of different radii. The one body part considered an exception was the head, which was represented by a sphere. A human walking model was also developed to describe the movements of the different body parts and how those movements could affect the radio waves in that environment.

Applying data from biomechanical and robotic studies on human movements as part of walking gaits, the researchers built a database of interacting moving body parts and their effects on time-varying wireless channels. Moving parts were synchronized starting with left-leg motion and using relative time to record the phase changes of the different body parts during one full cycle of body motion.

The research team also considered the contributions of signal fading due to the absorption and reflection of signals by the indoor environment, with its walls, floor, and ceiling. They used measured relative permittivity values of different dielectric materials at a number of different frequencies (even through millimeter-wave frequencies) to gauge the impact of the human-body model on wireless channel characteristics in different types of indoor environments.

The extensive measurements made by the team, particularly at 2.45 GHz, support the accuracy of their indoor human-body model. By using the received signal strength of an LOS signal as a reference, they compared their human body model to collected data and a single-cylinder simulation, with their model showing close agreement with different references. Measurements were taken at different heights within the indoor environment to better understand the effects of both lower and upper human body parts on the wireless radio environment.

Differences between the new model and the older, single-cylinder human body model were quite large for the lower body parts and the single-cylinder model, and less significant for the upper body parts and the single-cylinder model. The researchers also became quite aware of the impact of different transmitter and receiver antenna heights on the indoor wireless communications channel.

See “A Dynamic Channel Model for Indoor Wireless Signals,” IEEE Antennas & Propagation Magazine, Vol. 60, No. 2, April 2018, p. 82.

About the Author

Jack Browne | Technical Contributor

Jack Browne, Technical Contributor, has worked in technical publishing for over 30 years. He managed the content and production of three technical journals while at the American Institute of Physics, including Medical Physics and the Journal of Vacuum Science & Technology. He has been a Publisher and Editor for Penton Media, started the firm’s Wireless Symposium & Exhibition trade show in 1993, and currently serves as Technical Contributor for that company's Microwaves & RF magazine. Browne, who holds a BS in Mathematics from City College of New York and BA degrees in English and Philosophy from Fordham University, is a member of the IEEE.

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