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Abstract Low latency beamforming using phased antenna arrays is the key for practical deployment of envisioned millimetre wave (mmWave) Gbps mobile networks. This work aims towards reducing the overhead of the exhaustive sector‐level sweep phase of the analog beamforming adopted in the IEEE 802.11ad standard. This work is the first to propose the use of reconfigurable antenna single RF chain in the sub‐6 GHz new radio (NR) band to aid codebook‐based beam selection in the mmWave band of the NR. We exploit the congruence between the spatial propagation signatures of signals at both mmWave and sub‐6 GHz frequencies to reduce the beam search space. The simulation results show a significant reduction in mmWave beam search overhead up to on average and with an average gain loss of 3dB. 

Abstract Highly integrated, flexible, and ultrathin wireless communication components are in significant demand due to the explosive growth of portable and wearable electronic devices in the fifth‐generation (5G) network era, but only conventional metals meet the requirements for emerging radio‐frequency (RF) devices so far. Here, it is reported on Ti₃C₂T_xMXene microstrip transmission lines with low‐energy attenuation and patch antennas with high‐power radiation at frequencies from 5.6 to 16.4 GHz. The radiation efficiency of a 5.5 µm thick MXene patch antenna manufactured by spray‐coating from aqueous solution reaches 99% at 16.4 GHz, which is about the same as that of a standard 35 µm thick copper patch antenna at about 15% of its thickness and 7% of the copper weight. MXene outperforms all other materials evaluated for patch antennas to date. Moreover, it is demonstrated that an MXene patch antenna array with integrated feeding circuits on a conformal surface has comparable performance with that of a copper antenna array at 28 GHz, which is a target frequency in practical 5G applications. The versatility of MXene antennas in wide frequency ranges coupled with the flexibility, scalability, and ease of solution processing makes MXene promising for integrated RF components in various flexible electronic devices. 

Ultrahigh frequency (UHF) passive radio frequency identification (RFID) tag-based sensors are proposed for intravenous (IV) fluid level monitoring in medical Internet of Things (IoT) applications. Two versions of the sensor are proposed: a binary sensor (i.e., full versus empty state sensing) and a real-time (i.e., continuous level) sensor. The operating principle is demonstrated using full-wave electromagnetic simulation at 910 MHz and validated with experimental results. Generalized Additive Model (GAM) and random forest algorithms are employed for each interrogation dataset. Real-time sensing is accomplished with small deviations across the models. A minimum of 72% and a maximum of 97% of cases are within a 20% error for the GAM model and 62% to 98% for the random forest model. The proposed sensor is battery-free, lightweight, low-cost, and highly reliable. The read range of the proposed sensor is 4.6 m. 

Reconfigurable intelligent surfaces (RISs) are an emerging transmission technology to aid wireless communication. However, the potential of using RIS to mitigate directed energy weapons (DEW) is not widely recognized. In this paper, we propose to leverage RIS (based on spiral antenna elements) to aid the mitigation of high-energy radio-frequency (RF) sources applied to a DEW. For example, integrating a broadband circularly-polarized antenna system with RIS technology can successfully mitigate DEW attacks across a wide range of frequencies regardless of how the radio waves are polarized. We simulated a spiral antenna that operates within a frequency band of 1.3 GHz to 7 GHz with a 3-dB axial ratio bandwidth (ARBW) covering from 2 GHz to 7 GHz. Full-wave simulation results show the potential promising application of RIS for the mitigation of DEW attacks. 

We present a directed high-energy radio wave exposure detection sensor using radio frequency (RF) energy harvesting techniques. The sensor comprises a small dipole antenna and a tunable rectifier circuit. Reverse biasing the diode allows high levels of RF radiation to be detected by the sensor. We also demonstrate how the frequency-dependent nature of the rectifier can be alleviated. The proposed sensor performance is experimentally evaluated in the 5–10 GHz range. 

Although much of the work in behaviorally detecting malware lies in collecting the best explanatory data and using the most efficacious machine learning models, the processing of the data can sometimes prove to be the most important step in the data pipeline. In this work, we collect kernel-level system calls on a resource-constrained Internet of Things (IoT) device, apply lightweight Natural Language Processing (NLP) techniques to the data, and feed this processed data to two simple machine learning classification models: Logistic Regression (LR) and a Neural Network (NN). For the data processing, we group the system calls into n-grams that are sorted by the timestamp in which they are recorded. To demonstrate the effectiveness, or lack thereof, of using n-grams, we deploy two types of malware onto the IoT device: a Denial-of-Service (DoS) attack, and an Advanced Persistent Threat (APT) malware. We examine the effects of using lightweight NLP on malware like the DoS and the stealthy APT malware. For stealthier malware, such as the APT, using more advanced, but far more resource-intensive, NLP techniques will likely increase detection capability, which is saved for future work.