# Machine Learning-Based Solutions for Handover Decisions in Non-Terrestrial Networks
## 3 Proposed Method
### Proposed Clustering Algorithm  
Machine learning algorithms improve the performance and efficiency of the handover process in wireless networks by providing more accurate and efficient handover decisions, optimizing the handover process, and mobility management. However, it is worth noting that the implementation and deployment of ML-based handover algorithms in NTN can be complex and require a significant amount of data and computational resources. Therefore, we propose ML-based solutions for Handover decision-making using the K-Means clus \begin{table}
\begin{tabular}{c c} \hline
**Feature** & **Description** \\ \hline CellType & Serving Cell or Target Cell, or Candidate Cell \\ \hline Latitude [deg] & Latitude of a specific UE in degrees \\ \hline Longitude [deg] & Longitude of a specific UE in degrees \\ \hline Time [s] & Time the UE stays in the cell \\ \hline Azimuth [deg] & Azimuth between satellite and UE \\ \hline Elevation [deg] & Elevation between satellite and UE \\ \hline Range [km] & Distance between satellite and UE \\ \hline Free Space Loss [dB] & Loss due to propagation through free space \\ \hline Prop Loss [dB] & The total propagation loss computed across all enabled \\  & propagation models \\ \hline Rcvd. Iso. Power [dBW] & Received isotropic power is the power at the receiver before the pre-receive gains/losses and the receiver antenna gain added (in dBW). \\ \hline Carrier Power at Rcvr Input [dBW] & Carrier Power at Rcvr Input is the power at the receiver after the receiver antenna gain is added (in dBW). \\ \hline Flux Density [dBW/m\({}^{2}\)] & The power from the desired transmitter crossing a unit area \\  & normal to the direction of wave propagation. \\ \hline g/T: G/T = (Receiver Gain)/(System Temperature at the Receiver) [dB/K] & The ratio of the receive antenna gain G to the total system temperature T is the “figure of merit” for the receiver (in dB/K). \\ \hline C/No [dB*MHZ] & The carrier-to-noise density ratio (C/No) where C is the carrier power and No = kT ([PERSON]’s constant \(\times\) system temperature) is the noise density. It is equivalent to C/N with a normalized Bandwidth (B = 1). \\ \hline C/N [dB] & The carrier-to-noise ratio (C/N) where C is the carrier power and N = kTB ([PERSON]’s constant \(\times\) system temperature \(\times\) bandwidth) is the noise power \\ \hline Eb/No [dB] & The energy per bit to noise ratio (Eb/No) where Eb is the energy per bit and No = kT ([PERSON]’s constant \(\times\) system temperature) \\ \hline Propagation Delay [s] & The amount of time required for a signal to propagate through the physical link medium. This will vary depending on the propagation distance and the type of medium \\ \hline Propagation Distance [km] & The distance of the physical link medium between a transmitter and a receiver for which a signal will travel \\ \hline Spectral Flux Density [dBW \(\times\) m\({}^{2}\)\(\times\) Hz\({}^{-1}\)] & The dimensions are watts/(m\({}^{2}\)\(\times\) Hz). The power is computed across the receiver’s bandwidth (as seen by the receiver’s RF front end). The bandwidth is the receiver’s total bandwidth \\ \hline \end{tabular}
\end{table} 
Table 1: User Equipment Features. tering algorithm, an unsupervised machine learning algorithm, so that UE that has been grouped due to particular similarities are in the same cluster. This algorithm calculates the distance between each UE and its cluster center in each iteration [33]. Moreover, ML-based algorithms can also be used to improve mobility management in NTN by predicting the location of the mobile and providing accurate handover decisions. In this study, the major clustering algorithm component uses a two-step procedure known as expectation maximization. Consequently, each UE can be paired with its closest centroid in the expectation stage; then the new centroid is set after the maximizing step computes the mean of all the UE for each cluster. The k-means algorithm is described below in Algorithm 1.