**6. Conclusions**

Due to the air mass flow, the inhomogeneous tropospheric turbulence exists almost all of the time. The tropospheric duct is very likely to be formed after heavy rain in the summer or with day–night temperature change. Researchers have confirmed the truth of over-the-horizon propagation of radio waves over the sea. On the land, when we simulate the over-the-horizon propagation to evaluate whether CCI could form between cities, the terrain relief, land cover type, and dielectric constant of land cover would be essential. The real elevation of intercity links from DEM data, real land cover type from MODIS data, and the corresponding dielectric permittivity of land cover types from different literature were considered in our simulation. Moreover, the method of the terrain shift map on WPE with range-dependent non-uniform turbulence was used to simulate the propagation loss on real intercity links. We also used the deep learning model to predict the distribution of the PL of the over-the-horizon signal cities at different distributions of intercity land forms in the ducting and random tropospheric turbulence environments. The prediction loss value was stable at about 0.6 m. This deep learning model could be applied to inter-city remote interference prediction when ducting occurs in similar plain terrains.

When terrain relief is rather high compared to the duct trapping layer height, the effects of terrain dominate in the propagation. When the terrain relief is relatively flat with no large peaks, ducting and turbulence dominate in the propagation. In our simulation, propagation loss could keep under 160 dB in the trapping layer of duct on intercity link length around 100 km, which could form CCI between cities. Thus, with 5G base stations,

frequency band networking, arrangements of large-scale antenna arrays, the effects of tall building block outs, and tropospheric ducts need to be considered.

While the deployment of 5G base stations is still in full swing, the 6G satellite low-orbit internet has also developed simultaneously. Due to the limited number of satellites that can be accommodated in low-Earth orbit, the world is currently undergoing constellation plans in different states, such as networking, testing, and project approval, and is conducting an unprecedented competition that cannot be lost. The development of satellite communication will have higher requirements on the utilization rate of the current communication frequency band, signal propagation efficiency, and anti-interference ability. This requires us to take into account various dynamic conditions, such as meteorology, terrain, and LC types, and accurately model the propagation of useful and interfering signals for communication at current frequency band.

With the continuous development of commercial areas and urban–rural junctions, the topography and land cover types of build-up areas will continue to change. The coastal and plain cities near the water are more prone to form ducts, and the coastal cities are developing rapidly. Urban buildings, greening, natural landscape development, urban– rural integration areas development, crop planting, etc., are all constantly developing and changing. The LC type has a very obvious influence on the propagation loss of the over-the-horizon communication, so the over-the-horizon effect caused by the duct and tropospheric turbulence may trigger remote CCI to coastal cities. The uplink transmission signal may be exceeded by the guard time slot, interfering with the downlink received signal. The work done in this paper could play a guiding role in the deployment of 5G base stations to prevent possible CCI and the selection of base station relay locations.

The analysis of this paper gives a prediction method of communication signals in real mixed duct and non-uniform tropospheric turbulent environments for real and constantly evolving and changing land covers. The dielectric permittivity of constantly changing land coverage could be improved by referencing more literature studies and experimental results.

**Author Contributions:** Conceptualization, K.Y., Z.W. and J.W.; Data curation, X.G. and K.Z.; Formal analysis, T.W.; Investigation, K.Y.; Project administration, X.G.; Resources, J.W.; Supervision, T.Q.; Validation, K.Z.; Writing—original draft, K.Y.; Writing—review and editing, Z.W., T.W. and L.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** Project supported by the National Natural Science Foundation of China (grant nos. 62271381, 62005205, 62071359, 61975158, 61901335, and 62001377) and Shaanxi Province Science Foundation for Youth, China (grant no. 2020JQ-329).

**Data Availability Statement:** The DEM data set is provided by Geospatial Data Cloud site, Computer Network Information Center, Chinese Academy of Sciences. (http://www.gscloud.cn). The MODIS data products are archived and available via FTP from the Land Processes Distributed Active Archive Center(DAAC) at EROS Data Center. (https://ladsweb.modaps.eosdis.nasa.gov/).

**Conflicts of Interest:** The authors declare no conflict of interest.
