This article proposes a novel Airborne Internet of Things Network (AIN) system architecture for monitoring water quality, combining existing wireless technologies with the aid of a low altitude platform (LAP) to relay data over long distances in hilly terrain. The proposed system consists of water quality sensors, smart utility network (SUN) devices, long range (LoRa) wireless devices, an LAP air balloon, and Wi-Fi devices. A measurement campaign was conducted to assess the proposed system, focusing on the communication link reliability and the LAP stability and robustness. Several constraints, such as payload limit and safe weather conditions, were also highlighted for operating the LAP with extensive and reliable coverage. On the other hand, characterizing the wireless channel has become a crucial parameter for planning and deploying Internet of Things (IoT) applications. Accordingly, this work proposes a novel hybrid machine learning (ML)-based semi-empirical path loss (PL) model for LoRa wireless communication. The results validated the proposed system’s effectiveness, unique characteristics, and capability to monitor water quality in a harsh environment. Results also revealed a significant difference in packet delivery rate (PDR) for different gateway height and spreading factor (SF) configurations. For instance, switching SF7 to SF12 increased PDR by 28.7%. Meanwhile, increasing gateway height increased PDR by 29.2% for similar SF configurations. The evaluation also revealed that none of the established PL models are suitable to represent harsh tropical environments. Finally, the proposed model achieved nearly 90% prediction accuracy for testing samples and 95% accuracy for training and overall measurement samples, vastly outperforming conventional models.

This paper presents a novel method for estimating received signal strength (RSS) in indoor radio propagation using a deep learning approach. The proposed method utilizes a training dataset comprised of imitated real-world indoor environments and radio-map images generated through 2.4 GHz ray-tracing. Additionally, we introduce a convolutional neural network (CNN) named Radio Residual UNet (RadioResUNet) to facilitate the training and prediction of indoor radio propagation. To assess the feasibility and effectiveness of this deep learning network for indoor radio estimation, we compare the RSS obtained from practical wireless equipment with that obtained by RadioResUNet in two indoor environments: an anechoic chamber and an office floor. Furthermore, we explore the prediction outcomes achieved using different loss functions, including mean squared error (MSE), binary cross-entropy (BCE), and dice binary cross-entropy (Dice_BCE), across varying dataset sizes. The results reveal that the proposed deep learning-based radio estimation method exhibits estimation discrepancies of 4.25 dB and 5.4 dB compared to practical measurements in real-world environments of the anechoic chamber and the office floor, respectively. These results indicate a performance that is comparable to the indoor propagation model of ITU-R P.1238. Additionally, we introduce an indoor radio estimation tool that utilizes the deep learning network of RadioResUNet to predict radio propagation in a target area with minimal input.

An integration process for the fabrication of thin film bulk acoustic wave resonator (FBAR) above the CMOS IC is proposed. An adhesive-bonding-based film transfer technique is utilized to transfer high resistivity Si film onto a CMOS chip. Benzocyclobutene (BCB) is used as an adhesive film. It is a heat resistive polymer and processes of temperature up to 300 °C are allowed on it. The CMOS is protected by BCB and thus is not damaged by plasma and chemical treatments. The transferred Si film offers flat and stable surface which is utilized for the deposition of ruthenium, aluminum nitride & aluminum to fabricate the FBAR structure. Finally, Si underneath the active device area is sacrificially etched to fabricate the air gap type FBAR. In this paper, we present the fabrication process and discuss important issues related to the fabrication.

IBFD (In-Band Full-Duplex) has attracted renewed attentions in recent years along with the improvement of self-interference cancellation technologies because IBFD can ideally double the system capacity. Although there have been various reports applying IBFD to the cellular system, some impair the compatibility with the conventional LTE (Long-Term Evolution) system and some are not evaluated in practical scenario such as a multi-cell environment. In this paper, we propose an advanced DDC (Dynamic-Duplex Cellular) system applying IBFD to all cells in a multi-cell environment. The proposed system suppresses inter-cell interferences among all cells and enhances the system capacity with proper scheduling and power control without information exchange among neighboring cells while maintaining compatibility with the conventional TD (Time division Duplex)-LTE system. Practicality of the proposed DDC system is also evaluated by computer simulation with regard to the enhancement of the user throughput.

Current vehicular communication systems are disadvantageous in dealing with a full-range futuristic ITS (Intelligent Transportation System) with regard to coverage and cost. On the contrary, TVWS (TV White-Spaces) have the potential to provide a wide-coverage, large-capacity, and costeffective connectivity for ITS because of the excellent propagation properties, and there are increasing demands for applying IEEE 802.22 to mobile communication system capable of a wide coverage of 10–30 km. Meanwhile, since IEEE 802.22 was standardized as a fixed communication system, its scattered pilot pattern hinders accurate channel estimation in a highly mobile environment due to the Doppler shift and long-delay multipath fading. In this paper, we propose a reliable channel estimation scheme enabling an IEEE 802.22-based mobile communication system in rural areas without altering PHY (Physical layer) parameters. The proposed scheme effectively uses pilot symbols in adjacent subcarriers to extend interpolation range and shorten interpolation interval and offers high-accurate and stable channel estimation while maintaining flexibility in DS/US (Downstream/Upstream) ratio. As a simulation result, our proposed scheme sufficiently achieves a required BER (Bit Error Rate) of 2×10-4 in a highly mobile long-delay multipath fading environment even in the presence of high CFO (Carrier Frequency Offset).