Application of End-to-End Deep Learning in Wireless Communications Systems

TL;DR

This paper explores how end-to-end deep learning can revolutionize wireless communication systems by enabling autonomous resource allocation schemes that adapt to multiple constraints, verified through simulations.

Contribution

It introduces a deep neural network-based resource allocation scheme for wireless systems, demonstrating its optimality and feasibility through simulation results.

Findings

DNN-based RA can satisfy multiple goals with constraints

Simulation verifies the optimality of the proposed scheme

Discusses technical challenges of applying deep learning in WCS

Abstract

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.