## 5 CNN-LSTM Model Architecture  
The CNN-LSTM model architecture exploited by the MMS-DSR approach can be synthesized by the following points:  
1. **Input Data Preparation:** the input to the CNN-LSTM model consists of various network metrics that are critical for routing and beamforming decisions. These basic metrics include the following: * **Latency (L):** measures the communication delay in the network. * **Bandwidth (B):** reflects the available communication capacity in the network. * **Reliability (R):** indicates the stability of communication links. * **Beamforming Efficacy (BE):** measures how effective beamforming is in improving signal quality. * **Channel State Information (CSI):** provides real-time information about the quality of the communication channels. These metrics represent both current and past network states, forming the input feature set that feeds into the CNN-LSTM model for further processing. 2. **Spatial Feature Extraction with CNN:** the CNN layers first receive normalized input data and are responsible for extracting spatial features. The input metrics, such as \(\hat{L}\), \(\hat{B}\), \(\hat{R}\), \(\bar{BE}\), and \(\bar{CSI}\), are processed using convolutional filters. These filters generate feature maps that capture critical spatial patterns such as traffic congestion zones or signal interference areas, which are crucial for effective route planning in densely populated urban areas. 3. **Temporal Pattern Analysis with LSTM:** after the CNN extracts spatial features, the feature maps are fed into LSTM layers. The LSTM processes these features over time, capturing temporal dependencies in the network. For instance, the LSTM can identify recurring traffic patterns or predict future changes in network topology, which is essential for anticipating and adapting to network fluctuations. 4. **Integration of CNN and LSTM Outputs:** the CNN-derived spatial insights and the LSTM-derived temporal predictions are integrated at this stage. This combined output provides robust predictive insights, allowing for real-time, proactive adjustments to routing and beamforming strategies. The model can forecast which routes are likely to become congested and how beamforming can be adjusted to maintain signal strength. 5. **Route Optimization Outputs:** utilizing the integrated insights, the model outputs recommendations for optimizing routes and beamforming. The predictions guide the MMS-DSR protocol to adjust routes and communication strategies dynamically, particularly in scenarios with high mobility or unpredictable network conditions. The output also predicts the optimal beamforming vectors for maintaining strong communication links in complex urban environments. 6. **Model Training and Real-time Deployment:** the CNN-LSTM model is initially trained offline using a diverse dataset of simulated network scenarios, representing \begin{table}
\begin{tabular}{c c c} \hline \hline
**Path** & **Routes** & **Total SF** \\ \hline A \(\rightarrow\) B \(\rightarrow\) J & r1 + r6 & \(0.612500+0.612500=1.225\) \\ A \(\rightarrow\) C \(\rightarrow\) J & r2 + r7 & \(0.425000+0.425000=0.850\) \\ A \(\rightarrow\) D \(\rightarrow\) J & r3 + r8 & \(0.420000+0.62000=1.040\) \\ A \(\rightarrow\) B \(\rightarrow\) E \(\rightarrow\) J & r1 + r4 + r9 & \(0.612500+0.715833+0.775833=2.104166\) \\ A \(\rightarrow\) C \(\rightarrow\) F \(\rightarrow\) J & r2 + r5 + r10 & \(0.425000+0.453333+0.333333=1.211666\) \\ \hline \end{tabular}
\end{table} 
Table 6: Paths, Routes, and Total Scoring Functions (SF). varied urban traffic conditions and network configurations. The training dataset captures long-term network behaviors, enabling the model to learn both spatial and temporal patterns. Once trained, the model is integrated into the MMS-DSR protocol for real-time operation, continuously refining routing decisions based on real-time network data and its predictions.