This paper presents the postprocessing results of a dual-polarized multiple-input-multiple-output (MIMO) channel measurement campaign, investigating two land mobile satellite link scenarios in urban environments. Basic channel evaluation metrics are examined to facilitate the selection of the appropriate communication technique. The ergodic channel capacity is found between 4.1 and 6.1 b/s/Hz and varies significantly with the elevation angle. The capacity is highly correlated with the terminal trajectory and signal reception. The cross-polarization discrimination (XPD) is found between 14.2 and 22.5 dB, whereas the cross-polarization isolation (XPI) is found between 12.8 and 21 dB, respectively. Furthermore, the correlation statistics and channel capacities are well approximated by existing stochastic channel models. Finally, from the channel stationarity analysis, large stationarity regions are found, the existence of which is also correlated with the terminal trajectory. The mean quasi-stationarity time is found between 41 and 66 s for different environments.

Unmanned aerial systems (UAS) have recently gained popularity, and they are envisioned as an integral parts of the current and future wireless and mobile-radio networks. Despite the exhaustive research on air-to-ground channels, there are insufficient studies, experimental campaigns and general channel models related to air-to-space (A2S) and air-to-air (A2A) wireless links. This paper presents a comprehensive review of the available channel models and path-loss prediction for A2S and A2A communications. Specific case studies attempting to extend current models’ parameters and provide important knowledge of the channel behavior in combination with UAV flight characteristics are also provided. A time-series rain-attenuation synthesizer is also presented that describes quite accurately the impact of the troposphere at frequencies above 10 GHz. This specific model can be also applied to both A2S and A2A wireless links. Finally, scientific challenges and gaps that can be used for future research on the upcoming 6G networks are highlighted.

This letter assesses different ensemble models, exploited to forecast propagation loss in rural environments. Stacking, voting, bagging, and gradient boosted trees ensemble techniques are evaluated using path loss data recorded in various rural locations all over Greece. According to the numerical results all the introduced ensemble models increase their prediction efficacy and outperform all the single-model-based methods. Furthermore, stacking technique with 5 base learners and a customized deep neural network (DNN), as meta-learner, exhibits the best performance. Finally, it is shown that the forecast precision is gradually enhanced by increasing the number of the training features, yet under penalty of time complexity.