# Intelligent Transmission Control Scheme for 5G mmWave Networks Employing Hybrid Beamforming
## 6 Conclusions  
In conclusion, the main concerns regarding time, beamforming efficiency, channel estimate accuracy, and overall network throughput are addressed by the suggested \"intelligent transmission control scheme for 5G mmWave networks employing hybrid beamforming\" technique. To fulfill the high needs of 5G mmWave networks, the method includes some novel techniques, such as utilizing CODel queue management to reduce latency and enhancing classic TCP performance with mmS-TCP. With less training overhead, the new \"row compression two-stage learning-based accurate multi-path processing network (RCTS-AMP-RSSI-AS)\" framework guarantees accurate channel estimations for both direct and cascaded channels, improving the performance of the system. For dynamic multi-user situations, beamforming maximizes technology performance and spatial accuracy by integrating digital and analog approaches with the Mu-MATD3. The refinement of the beamforming process is improved by the \"digital and analog beam training with LSTM (DAH-BT-LSTM)\" network, which boosts noise resilience, decreases training overhead, and guarantees the real-time optimization of beam alignment. Finally, \"low latency congestion control schemes (LLCCS)\" adapt data transmission rates in real-time to manage congestion  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No of User (x-Axis)**} & \multicolumn{4}{c}{**Beamforming Accuracy (\%) (y-Axis)**} \\ \cline{2-5}  & **SIM-ADMT** & **Deep IA** & **UL-SCH + PUSCH** & **Proposed** \\ \hline
20 & 15 & 20 & 40 & 70 \\ \hline
40 & 25 & 30 & 45 & 75 \\ \hline
60 & 35 & 40 & 50 & 68 \\ \hline
80 & 55 & 60 & 70 & 81 \\ \hline
100 & 65 & 70 & 80 & 98 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Numerical outcomes of beamforming accuracy (%).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Metric**} & \multicolumn{4}{c}{**Proposed vs. Existing Methods**} \\ \cline{2-5}  & **FP-TCP** & **SIM-ADMT** & **UL-SCH + PUSCH** & **Deep IA** & **Proposed** \\ \hline Throughput (Mbps) & 40 & 32 & 60 & - & 90 \\ \hline Round-trip time (ms) & 150 & 180 & 175 & - & 120 \\ \hline Packet loss (\%) & 80 & 85 & 70 & - & 50 \\ \hline SNR (dB) & 8.0 & 7.0 & 7.0 & - & 9.0 \\ \hline End-to-end delay (ms) & 90 & 80 & 98 & - & 70 \\ \hline Beamforming accuracy (\%) & - & 65 & 80 & 70 & 98 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Performance comparison of proposed vs. existing methods.  
effectively, guaranteeing both high throughput and low latency in fluctuating traffic conditions. The suggested approach guarantees reliable, high-performance communication with reduced latency and enhanced reliability in modern 5G mmWave networks by merging advanced techniques. Our results show that Mu-MATD3 and DAH-BT-LSTM deliver greater throughput (90 Mbps vs. 32 Mbps) and beamforming accuracy (98% vs. 65%) using real-world hardware, even if SIM offers theoretical benefits under ideal circumstances. Although SIM hybridization for terahertz bands may be investigated in future research, the existing system effectively satisfies 5G criteria.  
Conceptualization, H.H. and A.M.A.A.; data curation, R.M.A.A. and T.J.B.A.; formal analysis, H.H., A.M.A.A. and O.R.A.A.; investigation, R.M.A.A. and O.R.A.A.; methodology, T.J.B.A. and R.M.A.A.; resources, T.J.B.A. and R.M.A.A.; software, H.H., A.M.A.A. and O.R.A.A. supervision, A.M.A.A. and H.H.; validation, A.M.A.A. and H.H.; visualization, A.M.R.; writing--original draft, A.M.A.A. and H.H.; writing--review and editing, R.M.A.A., O.R.A.A. and T.J.B.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data will be made available upon request.  
The authors declare no conflict of interest.