# Learning-Based Traffic Scheduling in Non-Stationary Multipath 5G Non-Terrestrial Networks
## 2 Materials and Methods
### Channel Model
\]  
Therefore, the satellite visibility interval from UE at a given latitude as the elevation angle varies from \(\theta_{i}\) to \(\theta_{j}\) was given in [23] as  
\[T_{\theta_{i},\theta_{j}}=\int_{\theta_{i}}^{\theta_{j}}\frac{2}{\omega_{S}- \omega_{E}cos(i)}cos^{-1}\bigg{(}\frac{cos(\gamma(\theta_{i}))}{cos(\gamma(x) )}\bigg{)}f_{\Theta_{j}}(x)dx. \tag{5}\]  
The satellites move in different orbits at different speeds. According to 3 GPP [4], the LOS probability changes with the changing satellite elevation angle. In general, the LOS probability increases with the elevation, reaching a maximum at _Nadir_ (90\({}^{\circ}\)) when the satellite is above the UE if it is in the orbital plane of the satellite. In dense urban areas, the LOS probability is lower, especially at low altitudes, because the signal is obstructed by and reflected off of high-rise buildings. Consequently, the AC agent must learn whether to schedule traffic transmission on any one link or on both links simultaneously (for redundancy), according to a given QoS requirement and according to the estimate of the LOS probability model of the two links as the satellites change their elevation angles. In this work, for the channel model, we adopted the statistical model for mixed propagation conditions provided by the International Telecommunication Union (ITU) for the design of Earth-space land mobile telecommunications systems [5]. In this ITU recommendation, a communication channel between a satellite and a UAV or any land mobile terminal is characterized by variations in the received signal power due to shadowing from buildings and vegetation, as well as multipath fading as a result of reflections from obstacles and from the ground. The ITU recommendations [5] provide a three-state Markov chain model to characterize the behavior of the land mobile satellite channel: (1) The first state is characterized by the presence of the LOS. This state is modeled by a Ricean fading for unshadowed areas with high received signal power. (2) The second is the state with no LOS due to strong shadowing and blocking from obstacles. This state is modeled with Rayleigh fading. (3) Between these two states, there is a third state known as the transition state, in which the multipath component power increases or decreases linearly [5]. For the purpose of this work, we followed a more simplified Markov chain model known as the Lutz model [24; 25], which approximates the three states into two states: The first is the lossless good state (G) with LOS, and the second is the bad state (B) with no LOS which is characterized by shadowing, blocking and erroneous traffic reception. We now derive the state transition probabilities according to the Lutz model. Following the work in [26], we let  
\[\tau_{s}=\left( \begin{array}{cc}1-b&b\\ g&1-g\end{array} \right)\]  
be the switching matrix of the Lutz model. In this model, the time required to transmit a bit is taken as the channel state transition unit, and \(b\) and \(g\) are the transition probabilities from G to B and from B to G, respectively, with G denoting the good state and B denoting the bad state.