The degree of reception of BS signals is affected by various factors. After routinely recording it at two observation points at two locations, we found that momentary upward and downward level shifts occurred multiple times, mainly during daytime. These level shifts were observed at one location. No such signal was sensed at the other location. After producing an algorithm to extract such momemtary level shifts, their statistical properties were investigated.Careful analyses, including assessment of the signal polarity, amplitude, duration, hours, and comparison with actual flight schedules and route information implied that these level shifts are attributable to the interference of direct and reflected waves from aircraft flying at approximately tropopause altitude. This assumption is further validated through computer simulations of BS signal interference.
Low-power wide-area (LPWA) is a major communication technology used in the Internet of Things (IoT). However, since LPWA does not conform to the TCP/IP protocol stack and employs its own unique protocol, it is difficult for sensors equipped with LPWA I/F to connect to the Internet directly. In the present study, to realize general TCP/IP communication on LPWA networks, we construct a TCP/IP network over Private LoRa, and evaluate its communication performance. Moreover, we verify the feasibility of TCP/IP communication over LoRa by using a popular Internet application.
This study is motivated by the demand for an efficient deep learning-based model that helps us predict the future link quality for intelligent decision-making systems. In this letter, we propose a transfer learning-based approach to predict millimeter-wave future received power in an indoor environment. The model is pre-trained using formulation-aid generated data and fine-tuned using measured data. The proposed framework reduces more than 30% in root-meansquare error and 6.5% in accuracy with high training speed compared to the baseline training from scratch.
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