Neural Network Approaches to Wireless Network Optimization

Wireless communication networks are widely used in today's communication systems and hence required proper optimization to achieve the desired response. Today the use of neural networks has become ubiquitous in improving the wireless networks since it offers the latter the need of modeling complex relations and the ability to respond to the complexities surrounding the environments. This research aims at exploring novel algorithms of learning for the neural network which include Deep Reinforcement Learning as a means of enhancing the wireless network. Among the features of implemented is a resource allocation task that pre-process the input data by clustering which then aids in the normalization process for improving the precision and convergence rate of the model. As seen from the findings of the last assessment, improvement in the bandwidth of the network, the delay and the utilisation rates are all demonstrated as competent as the call for the neural network analysis technique in the enhancement of wireless networks.