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Radio frequency (RF) sensing and evaluation frameworks for indoor wireless systems
Dissertation   Open access

Radio frequency (RF) sensing and evaluation frameworks for indoor wireless systems

Md Shakir Hossain
Doctor of Philosophy (Ph.D.), Drexel University
Jun 2026
DOI:
https://doi.org/10.17918/00011414
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Abstract

Channel emulation Multi-armed bandit problem Radio frequency identification Reconfigurable antennas Reconfigurable intelligent aurfaces Machine Learning
Indoor wireless systems are becoming increasingly important in healthcare, automation, asset tracking, and next-generation communication networks. In these environments, radio frequency (RF) signals are strongly shaped by walls, furniture, human presence, device placement, antenna behavior, blockage, and multipath. These interactions create both an opportunity and a challenge: they can provide useful information for sensing, but they also make reliable communication difficult in confined indoor spaces. This dissertation addresses these two needs by developing RF sensing and evaluation methods for indoor wireless systems, with emphasis on machine-learning-based sensing, adaptive link optimization, and reconfigurable propagation control. The sensing portion of this work demonstrates how ultra-high-frequency (UHF) radio-frequency identification (RFID) technology can be extended beyond identification and tracking to support practical indoor sensing applications. Two systems are developed: an RFID-based indoor localization method for position-aware indoor environments and an RFID-based intravenous fluid-level sensor for real-time healthcare monitoring. In both cases, machine learning is used to map complex RFID measurements to useful physical quantities, enabling low-cost and battery-free sensing with simple passive tags. The evaluation portion of this dissertation develops end-to-end frameworks for reconfigurable transceivers, including pattern-reconfigurable antennas and reconfigurable intelligent surfaces (RIS), for indoor millimeter-wave systems. These frameworks span three stages: full-wave EM simulation (HFSS, CST, openEMS), ray-tracing-based channel modeling with machine-learning-driven state selection and channel-tap reduction, and hardware-in-the-loop emulation. This dissertation also develops a fully automated Python-based RIS design and simulation workflow that streamlines parameterized unit-cell modeling, EM characterization, and integration with site-specific channel modeling, enabling repeated full-wave simulations, geometry updates, and far-field radiation-pattern extraction for distinct RIS on-off configurations. The emulation platform combines a wireless channel emulator, software-defined radios, and DragonRadio to support packet-level wireless experiments under repeatable channel conditions. This simulation-to-emulation pipeline enables realistic and repeatable evaluation of next-generation wireless systems under site-specific indoor propagation conditions.

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