## 1 Introduction
In addition to these advancements, we enhance MMS-DSR by integrating a CNN-LSTM based beamforming algorithm. This enhancement is designed to optimize the beamforming vectors dynamically, a critical aspect in 6G networks to adapt to rapid spatialtemporal variations in the channel. By taking advantage of the capabilities of Convolutional Neural Networks (CNNs) to extract spatial features and Long Short-Term Memory (LSTM) networks to account for temporal dependencies, our approach adapts the beamforming vectors in real-time based on the evolving network conditions. This adaptive beamforming capability is particularly important in the context of 6G's use of Massive MIMO systems, where the ability to dynamically direct beams can significantly enhance signal quality and network performance. The integration of this beamforming algorithm into MMS-DSR means that the protocol is not only making intelligent decisions based on multi-metric scores but also considering the optimal beamforming directions to improve link reliability and data rates in the complex urban features of smart cities. The main contributions of the present proposal are synthesized as follows:  
1. **Development of an Advanced Multi-Metric Scoring Mechanism:** we introduce a novel scoring algorithm that uses the properties of Massive MIMO and the 802.11 ax standard. This algorithm evaluates routes based on a comprehensive set of QoS metrics, providing a nuanced and context-aware route selection process optimized for urban environments. 2. **Introduction of Machine Learning Techniques:** by incorporating machine learning algorithms, MMS-DSR is able to predict future route qualities based on historical data, enabling proactive route optimization. This is particularly useful in smart cities where predicting and adapting to changing conditions can significantly improve network performance. 3. **Integration of a CNN-LSTM Based Beamforming Algorithm:** we enhance MMS-DSR with a beamforming optimization algorithm that dynamically adjusts beamforming vectors. This integration helps in managing the spatial-temporal variations in the channel, supporting the complex beamforming needs of Massive MIMO systems in 6G. 4. **Empirical Validation in a Smart City Simulation Environment:** we validate the performance of MMS-DSR through extensive simulations using the _inet_ framework along with the OSG Earth visualizer in OMNeT++. This simulation environment provides a realistic backdrop that highlights the protocol's potential benefits for smart city applications, demonstrating its robustness and efficiency in a variety of challenging urban 6G scenarios. The remainder of this paper is structured as follows: Section 2 discusses the most relevant related works illustrating the conceptual gaps that our research aims to fill. Sections 3 and 4 present the MMS-DSR architectural overview and mathematical proof, respectively. Section 5 presents CNN-LSTM model architecture. Section 6 explains how the proposed approach could be adapted for VANET while Section 7 showcases and discusses the main findings obtained by numerical evaluation. Finally, Section 8 concludes the paper and outlines the roadmap for future research. Table 1 includes the list of main symbols used in the paper. \begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Description** \\ \hline \(\alpha\) & Weighting factor for the summation real-time term \\ \(\lambda\) & Weighting factor for the beamforming score \\ \(\delta\) & Weighting factor integrating the predictive reliability score \\ \(w_{i}(t)\) & Time-dependent weights \\ \(m_{i}(r)\) & Route metrics for MANET \\ \(v_{i}(r)\) & Route metrics for VANET \\ \(P_{prediction}(r,t)\) & Predictive reliability score \\ \(BF(r,t)\) & Beamforming score \\ \(L(r,t)\) & Latency \\ \(B(r,t)\) & Bandwidth \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of main Symbols and Acronyms used in the paper.