Reliable Data Transmission in Wireless Ad Hoc Networks using Advanced Deep Learning Techniques

Wireless ad hoc networks face substantial hurdles regarding reliable data delivery because of their dynamic topology structures and mobile network components and fluctuating interference conditions. Recently, there has been emergence of advanced deep learning technologies that offer better solutions that solve the performance related challenges of transmission in WANETs. In optimisation of routing alongside error detection and forwarding data in ad hoc networks, scientists examine applicability of deep learning models through the use of convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The study proves the effectiveness of deep learning processes that are integrated into the conventional network protocols to establish superior network survivability and less data loss as compared to the enhancement of the throughput performance. The primary highlight of this inquiry is on the development of flexible models so as to facilitate a real time prediction of the network status so as to allow automatic adjustments of transmission plans. Both simulation strata and actual experimentation is engaged by researchers who test the product of the techniques to compare them with the standard procedures. Advanced methods that incorporate deep learning techniques show better performance than traditional methods in their better reliability along with lower latency and better power/energy consumption. The application of deep learning in solving complicated WANET issues eliminates major handicaps of the communication systems especially in places that are deep in the land and need stable communications once disasters have hit the regions.