The 60 GHz band plays an important role for wireless signalling with extremely high data rates, both for WiFi and cellular applications. For the design and performance analysis of this band, the impact of human interactions on the propagation from transmitter to receiver has to be taken into account. While the impact of a single human body blocking the line-of-sight (LOS) has been investigated as a deterministic effect, statistical models describing the effect of multiple human bodies, acting as reflectors, on received power and delay spread are still lacking. To close this gap, this paper analyzes measurements of 60 GHz channel impulse responses in static but “evolutionary” office scenarios that involve one and two people and uses them to calibrate a ray tracer that allows the generation of a larger number of channel realizations. Regression fits are applied to the resulting channel responses to obtain an accurate characterization of human-induced power and delay variations in proximity situations where humans give rise to additional multipath 1 1 The work of B. Abou Ali Modad and A.F. Molsich was supported in part by the National Science Foundation and the National Institute of Standards and Technology..

This paper presents a compact ultra-high frequency (UHF) folded antenna design that relies on a single radio frequency micro-electro-mechanical switch (RF-MEMS) to reconfigure its frequency of operation between 575 MHz and 760 MHz. The reconfigurable multi-layered antenna design has a volumetric folded topology with total dimensions of 40×40mm2; equivalent to 0.11λx0.11λ at 575 MHz. The antenna is fabricated and tested where the measurements agree well with the simulation results. The antenna exhibits a radiation efficiency of 40% at 575 MHz and 62% at 760 MHz. Furthermore, the proposed antenna is tested in a cognitive radio environment to validate its ability to adapt to cognitive input and tune its performance across white space within the UHF band. This is achieved by relying on a trained classifier model that is embedded within a microcontroller to autonomously control the state of the integrated switch based on which channel is idle.

Knowing the wireless channel characteristics, particularly the existence of a line-of-sight (LOS) connection from the user equipment (UE) to the base station (BS), is key in wireless system development and deployments. The LOS becomes even more important in high frequency systems such as fifth generation (5G) mm-wave and terahertz (THz) systems where the difference in channel characteristics varies greatly from LOS to non-LOS (NLOS) situations. While existing LOS models such as the 3GPP model and others in the literature are valuable, they lack cell-by-cell specificity and/or do not generalize well to different environments. In this paper, we aim to close these gaps and propose generalized models derived from extensive real-world analysis in diverse environments. We also study the performance of our models in system-level simulations in terms of signal-to-interference-ratio (SIR) and coverage.

Over the last decades, wireless networks have experienced major growth in their enabling technologies and global penetration, with the Internet of Things (IoT) paradigm witnessing a significant growth. To meet the high connectivity demands, the IEEE 802.11 WiFi protocol is constantly evolving, yet performance bottlenecks still exist especially in ultra-dense network scenarios requiring crowd sensing and management, where health monitoring and safety are of utmost importance. In this paper, we propose a practical approach that is compliant with the IEEE 802.11 standard to accommodate periodic IoT traffic in scenarios with a very large number of users and critical delay-sensitive health application data. The proposed approach combines data-control channel separation with optimized time division multiple access (TDMA), including an extension to maintain a level of fairness among different devices. The proposed approach is shown to achieve notable gains in packet delivery ratio, delay, and number of associated and served users compared to the CSMA/CA-based IEEE 802.11 WiFi standards.

A framework is proposed for developing and evaluating algorithms for extracting multipath propagation components (MPCs) from measurements collected by sounders at millimeter-wave (mmW) frequencies. To focus on algorithmic performance, an idealized model is proposed for the spatial frequency response of the propagation environment measured by a sounder. The input to the sounder model is a pre-determined set of MPC parameters that serve as the “ground truth”. A three-dimensional angle-delay (beamspace) representation of the measured spatial frequency response serves as a natural domain for implementing and analyzing MPC extraction algorithms. Metrics for quantifying the error in estimated MPC parameters are introduced. Initial results are presented for a greedy matching pursuit algorithm that performs a least-squares (LS) reconstruction of the MPC path gains within the iterations. The results indicate that the simple greedy-LS algorithm has the ability to extract MPCs over a large dynamic range, and suggest several avenues for further performance improvement through extensions of the greedy-LS algorithm as well as by incorporating features of other algorithms, such as SAGE and RIMAX.

Dense gatherings such as sports events, festivals, and pilgrimages usually include a high risk of tragic incidents such as stampedes and structural failures. In this article, we present a framework for simultaneous crowd and structural monitoring based on a unique combination of sensing technologies with processing using machine learning, for intelligent crowd management decision making. This will enhance efficiency and minimize associated safety risks. A main novelty is the utilization of fiber optic sensors based on fiber Bragg grating for strain measurements combined with wearable biometric sensing devices for location, activity, and physiological measurements. We highlight key research and implementation challenges and propose ideas to address them. Moreover, we present supporting results based on measurements from a prototype experimental testbed in order to demonstrate the effectiveness of the proposed framework and to extract practical insights for future research extensions.

This paper presents a novel planar reconfigurable meandered loop antenna that is suitable for Internet of Things (IoT) applications. This antenna reconfigures its frequency of operation between two ISM bands, 760 MHz and 575 MHz. The antenna structure also has three folded edges, six layers and with total dimensions of 40×40mm 2 . The top layer (layer 1) is a meandered loop patch placed on top of a dielectric substrate and an air gap. The patch is fed through a shorting sheet from the feeding network. The feeding network constitutes the fourth layer of the antenna structure, which is placed on top of a dielectric layer and a full ground plane. The antenna has a compact size with miniaturization percentage of 70% at 760 MHz and 80% at 575 MHz.