A Survey of Deep Learning for Tactical Wireless Networks

Abstract

A tactical wireless network is a military radio communication network supporting mission-critical applications. Hence, a tactical wireless network demands more reliability, availability, robustness, and security than a commercial wireless network. The tactical wireless network must operate in hostile environment, where the environment changes rapidly and is prone to attack. To maintain the required quality of services (QoS), the network must intelligently adapt to the hostile environment. The concept of cognitive radio (CR), in which a radio can sense and adapt to radio environment, could be a solution for modern tactical wireless networks. Machine learning plays an important role in CR to provide sensing and adapting functions. Irrational decision made by a machine learning can lead to flaws in the CR. The introduction of Deep Learning models machine learning on the basis of human brain process; and hence, could make the CR more rational. This paper explores the challenges of tactical wireless networks, the CR functions as the solution for tactical wireless networks and Deep Learning techniques for improving CR functions. The survey presented in this paper should contribute to the development of modern tactical wireless networks by providing the possible applications, benefits and drawbacks of deep learning in CR.