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The micro and small unmanned aerial vehicles (msUAVs) internet of things has been widely used in different fields. At the same time, the disordered flight of msUAVs inevitably become a potential threat to public security (such as airports, railway stations, major event venues, etc.). In view of the safety problem of msUAVs, the anti-micro and small unmanned aerial vehicles (AmsUAVs) is used to solve the defense problem of msUAVs in public safety places. The system blocks the communication link of the msUAVs by transmitting high power to achieve the purpose of low altitude control. At the same time, the AmsUAVs will also cause interference to the uplink of other nearby radio communication systems, such as civil aviation and mobile communication systems, In this paper, an analysis method based on the interference-to-noise ratio (INR) criterion is proposed and interference analysis is performed on AmsUAVs and some mobile communication systems uplink. First, to control msUAVs, it is necessary to calculate the minimum transmit power of the AmsUAVs in the corresponding frequency band according to the uplink and the radio wave propagation distance of the msUAVs. Second, to avoid harmful interference to other radio commnication systems (e.g., CDMA and TD-LTE), the frequency and distance separation between the AmsUAVs and mobile communication systems should be calculated according to factors such as application scenarios, radio wave propagation distance, and working frequency band. Finally, a simulation analysis provides the transmit power, off-axis angle, frequency separation, and distance separation of the AmsUAVs and mobile communication base station in different environments. [ABSTRACT FROM AUTHOR]

The graph neural network (GNN) has shown outstanding performance in processing unstructured data. However, the downstream task performance of GNN strongly depends on the accuracy of data graph structural features and, as a type of deep learning (DL) model, the size of the training dataset is equally crucial to its performance. This paper is based on graph neural networks to predict and complete the target radio environment map (REM) through multiple complete REMs and sparse spectrum monitoring data in the target domain. Due to the complexity of radio wave propagation in space, it is difficult to accurately and explicitly construct the spatial graph structure of the spectral data. In response to the two above issues, we propose a multi-source domain adaptive of GNN for regression (GNN-MDAR) model, which includes two key modules: (1) graph structure alignment modules are used to capture and learn graph structure information shared by cross-domain radio propagation and extract reliable graph structure information for downstream reference signal receiving power (RSRP) prediction task; and (2) a spatial distribution matching module is used to reduce the feature distribution mismatch across spatial grids and improve the model's ability to remain domain invariant. Based on the measured REMs dataset, the comparative results of simulation experiments show that the GNN-MDAR outperforms the other four benchmark methods in accuracy when there is less RSRP label data in the target domain. [ABSTRACT FROM AUTHOR]

Today's network is continuously evolving through a variety of wireless communication systems. Wireless communication is now available just about anywhere--it has become an integral part of everyday life and brought us many benefits. At the same time, wireless communication is predicted to become increasingly complex in the future, so there is a need for further evolution of the wireless network to deal with this complexity. NTT Distinguished Researcher Motoharu Sasaki says, "It is important that the network be easy for people to use without having to think about it." In this article, we talk with him about his activities as a rising researcher including his research on "achieving complete wireless communication through estimation and prediction technology for radio wave propagation characteristics." [ABSTRACT FROM AUTHOR]

High latitude ionospheric density structures such as polar cap patches and arcs are capable of deflecting high frequency (HF) radio waves to off‐great circle paths, and are likely detrimental to technologies dependent on HF radio propagation. In this study, nearly 2.5 years of 4.6–14.4 MHz data from a multi‐frequency HF radio link between Qaanaaq, Greenland and Alert, Canada are used to investigate high‐latitude off‐great circle propagation in the polar cap. After an example of HF radio propagation affected by polar cap patches is shown in detail, a statistical analysis of the occurrence and impacts of off‐great circle deflections in the polar cap is presented. Off‐great circle propagation is shown to be increasingly common with increasing frequency up to 11.1 MHz, such that averaged over 1 year, received 11.1 MHz signals experienced deflections >30° from the great circle direction 65.6% of the time. The occurrence of these deflections across the year is shown to be at a maximum in the winter, while occurrence across the day varies with season. Trends across both time of day and time of year for 11.1 and 14.4 MHz deflections are consistent with polar cap patch occurrence trends. Off‐great circle deflections are shown to be associated with increased time‐of‐flights, a larger range of positive and negative Doppler shifts, increased Doppler spreads, and lower signal‐to‐noise ratios. These results are discussed in the context of ionospheric phenomena in the polar cap, and implications for over‐the‐horizon radars operating at high latitudes. Plain Language Summary: High frequency (HF) radio waves can travel long distances by bouncing between the ground and the ionosphere; an upper layer of the Earth's atmosphere, allowing them to be used for communications and surveillance in remote locations. However, strong space weather can change the paths these radio waves take, causing problems for HF radio technologies. In this paper, radio transmissions sent between Qaanaaq, Greenland and Alert, Canada are studied in order to better understand how often HF radio waves are deflected by space weather at high latitudes, and what effect these deflections have on the signals themselves. After a brief example of what space weather deflections look like, transmissions sent over a whole year are studied to determine how deflections depend on radio frequency, time of day, and time of year. Then, the effects these deflections have on other signal properties is studied. It is found that deflections are very common at high latitudes, especially in the winter. Deflections tend to increase the travel time of signals, change their frequency, and reduce their signal strength. These results are shown to be connected to polar cap patches and arcs; which are common space weather phenomena near the poles. Key Points: Off‐great circle propagation occurring in the polar cap is studied using years of data from an HF radio link in the polar capLarge (greater than 30°) deflections from the great circle are shown to be very common, especially in the winterThese deflections are shown to affect HF radio signals in ways that are consistent with interactions with polar cap patches and arcs [ABSTRACT FROM AUTHOR]

Vertical radio refractivity gradients are essential for monitoring propagation conditions when designing and planning terrestrial radio links for communications in the lower atmosphere. Average data gathered from meteorological factors such as temperature, pressure, and relative humidity was used from 1980 to 2020 n this study. This study focuses on the vertical gradient of radio refractivity within the lower atmospheric layer, investigating its influence on factors like signal propagation, route clearance, and line-of-sight communication. The analysis encompasses three cities - Abuja, Makurdi, and Jos. In Figure 2 monthly average changes in the refractivity gradient are presented for Abuja. In addition, the seasonal radio refractivity gradient shows mean values of -110.000N/km for Abuja which shows that the refractivity condition is Super-Refraction, -77.553N/km for Jos which shows that the refractivity condition is Normal-Refraction and - 97.209N/km for Makurdi which shows that the refractivity condition is Normal-Refraction respectively. Higher gradient values, G1, G2, and G3 were observed during the dry season (December-February) which could be attributed to dry air and steeper refractivity gradients. In contrast, lower gradient values were observed during the wet season (June-October) due to increased atmospheric moisture. Similarly, measured vertical radio refractivity gradient data, G1 N/km, G2 N/km, and G3 N/km for Jos displayed consistent fluctuations throughout the year. Similarly, measured vertical radio refractivity gradient data, G1 N/km, G2 N/km, and G3 N/km for Jos displayed consistent fluctuations throughout the year. Notably, wetter months exhibit higher refractivity gradient values, while drier months show lower values. G2 consistently records the highest gradient values, and despite varying magnitudes, the trends remain consistent. Finally, measured vertical radio refractivity gradient data, G1 N/km, G2 N/km, and G3 N/km for Makurdi indicate distinct fluctuations with the most negative values observed in November and December. The Dry Season witnesses relatively lower gradient values, while the Transition and Wet Seasons experience rising values, influenced by atmospheric shifts. The Higher negative values during the wet season suggest pronounced variations in the refractive index, affecting radio wave propagation. These findings emphasize the significance of refractivity gradient variations and their implications for radio wave behaviour particularly during different seasons. [ABSTRACT FROM AUTHOR]