Deep Learning Techniques for Accuracy Optimization in Wireless Networks

Nowadays, to improve performance and increase the accuracy of routing in wireless networks, deep learning has been widely concerned and implemented to predict the best routing paths through dynamic alternative path selection to governor the data traffic. However, it is still some challenging to improve the performance of routing using different techniques. In view of this challenge, this paper presents study and evaluation of deep learning techniques this shows how to utilize deep learning algorithms to enhance wireless network performance and accuracy in specific. The paper discusses the deep learning algorithms such as artificial neural network, conventional neural network, recurrent neural network, deep belief network, deep Boltzmann machine, stacked auto encoder. Then select deep ANN as a suitable algorithm to implement. After that, compare it with ANN machine learning algorithm. And then evaluates the implementation At last, dataset applied to verify the accuracy ratio and the effectiveness of our proposed model, we find that deep learning is better than machine learning, based on accuracy and loss since deep learning accuracy ratio was 94% while the accuracy ratio of the machine learning was 68%. Regarding the loss function, its 32% in deep learning, while its 38% for machine learning.