Application of End-to-End Deep Learning in Wireless Communications Systems
This work addresses resource allocation challenges for wireless communications systems, but it appears incremental as it applies existing deep learning methods to this domain.
The paper tackles the problem of resource allocation in wireless communications systems by applying end-to-end deep learning, verifying the optimality and feasibility of a deep neural network-based scheme through simulation.
Deep learning is a potential paradigm changer for the design of wireless communications systems (WCS), from conventional handcrafted schemes based on sophisticated mathematical models with assumptions to autonomous schemes based on the end-to-end deep learning using a large number of data. In this article, we present a basic concept of the deep learning and its application to WCS by investigating the resource allocation (RA) scheme based on a deep neural network (DNN) where multiple goals with various constraints can be satisfied through the end-to-end deep learning. Especially, the optimality and feasibility of the DNN based RA are verified through simulation. Then, we discuss the technical challenges regarding the application of deep learning in WCS.