Deep Learning for Wireless Network Distribution (5G/LTE)

As a result of using machine learning and deep learning in the network, we were able to more efficiently manage the allocation of network resources and the availability of the network overall. Key performance indicators are used to analyze incoming traffic and make predictions to allocate network resources to unknown devices. High load balancing and efficient use of available resources on current network portions are made possible with intelligent resource allocation. The underlying model is smart enough to determine which piece of the network is most suited for the challenge at present. 5G mobile networks, mobile communications, and the latency of modern and future wireless networks are affected by strict regulations about how long it takes for data transmission to reach its destination, dependability, how much data can be transmitted without a problem, capacity, how much data can be transmitted securely, and speed of user connection. Mobile providers are always looking for new ways to make it possible for multiple, autonomous users to share a single network. Slicing is a method that uses network fragmentation to share the same physical infrastructure among multiple networks that have competing needs. Future communication networks will need to make decisions based on data in order to keep up with the exponential rise in traffic.