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This paper presents the design and comprehensive measurements of a compact high-gain 32 element planar antenna array covering the n257 (26.5-29.5 GHz) millimeter wave (mmWave) band. First an 8-element quasi-uniform linear array is designed using a series-fed topology with fan shaped beams for point-to-multipoint connectivity followed by a compact corporate series feed network to design high-gain directive array for point-to-point connectivity. The radiation patterns of both antenna arrays in the azimuth and elevation planes are measured across a 180 degrees span using an over-the-air (OTA) compact antenna test range (CATR) system with a single rotary positioner. Moreover the procedure for quantifying and measuring the gain of mmWave antenna arrays is demonstrated in detail. The peak measured gain of the planar array is 18.45 dBi at 28.5 GHz while the half-power beamwidth of the planar array in the elevation and azimuth planes varies between 11 to 13 degrees, and 23-27 degrees respectively within the 26.5-29.5 GHz range. The measurement results match well with the simulations. The designed antenna array is suitable for various emerging 5G and beyond mmWave applications such as fixed wireless access, mmWave near-field focusing, high-resolution radar systems, and the characterization of mmWave path loss and channel sounding in diverse indoor environments and smart factories., Comment: 11 Pages, 15 Figues, Orignalsubmission

Unmanned aerial vehicle (UAV)-assisted communication is becoming a streamlined technology in providing improved coverage to the internet-of-things (IoT) based devices. Rapid deployment, portability, and flexibility are some of the fundamental characteristics of UAVs, which make them ideal for effectively managing emergency-based IoT applications. This paper studies a UAV-assisted wireless IoT network relying on non-orthogonal multiple access (NOMA) to facilitate uplink connectivity for devices spread over a disaster region. The UAV setup is capable of relaying the information to the cellular base station (BS) using decode and forward relay protocol. By jointly utilizing the concepts of unsupervised machine learning (ML) and solving the resulting non-convex problem, we can maximize the total energy efficiency (EE) of IoT devices spread over a disaster region. Our proposed approach uses a combination of k-medoids and Silhouette analysis to perform resource allocation, whereas, power optimization is performed using iterative methods. In comparison to the exhaustive search method, our proposed scheme solves the EE maximization problem with much lower complexity and at the same time improves the overall energy consumption of the IoT devices. Moreover, in comparison to a modified version of greedy algorithm, our proposed approach improves the total EE of the system by 19% for a fixed 50k target number of bits.

The outbreak of viruses have necessitated contact tracing and infection tracking methods. Despite various efforts, there is currently no standard scheme for the tracing and tracking. Many nations of the world have therefore, developed their own ways where carriers of disease could be tracked and their contacts traced. These are generalized methods developed either in a distributed manner giving citizens control of their identity or in a centralised manner where a health authority gathers data on those who are carriers. This paper outlines some of the most significant approaches that have been established for contact tracing around the world. A comprehensive review on the key enabling methods used to realise the infrastructure around these infection tracking and contact tracing methods is also presented and recommendations are made for the most effective way to develop such a practice.