This study evaluated an indoor location estimation method using a wireless local area network (LAN). The Fingerprint method was used as a location estimation technique. This method can estimate the current location based on the received signal strength indicator and media access control address obtained from the wireless LAN access points. However, this method is greatly affected by changes in the radio propagation environment, and the presence or absence of obstacles reduces the location estimation accuracy. This paper proposes a database developed by measuring data of different radio propagation environments. The environment was experimentally confirmed to change with the opening and closing of doors and the direction of radio wave measurement.

This letter studies a placement method of optical ground stations (OGSs) to realize site diversity in optical satellite-to-ground communications under cloud attenuation. Using a meteorological ERA-Interim database, the monthly average cloud attenuation over Japan during 2018-2022 is calculated. Based on the obtained results, a set of candidates for the OGS is proposed, including places with the lowest attenuation, major cities, and a JAXA observatory station. Then, a greedy heuristic method is presented to determine a sub-optimal set of OGSs. Simulation results reveal that sufficiently high system availabilities are achieved when site diversity is performed from the proposed set of candidates.

This paper proposes a path loss prediction model based on a convolutional neural network utilizing side-view images to consider over-rooftop propagation, in addition to the top-view images around the receiving station of the conventional model in the urban macrocell environment. The building profile between the transmitting and receiving stations was used for side-view images. In addition, the scalar parameter of frequency was added to the fully connected neural network part as a proposed method to consider frequency characteristics. The model was learned and validated using the measured data, and the estimation error was compared with the conventional model to evaluate its validity. Our findings showed that the RMS error of 12.1dB using the conventional model was improved to 4.4dB by the proposed model.