Intelligent Tactical Wireless Networks for Challenging Environments

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In recent years, artificial intelligence (AI), enabled by machine learning (ML), has revolutionized many technologies and pushed their performance and intelligent capabilities beyond expectations. Consequently, new intelligent wireless solutions aiming to increase the robustness, reliability, throughput, and other feature of the wireless networks are emerging. These solutions are appealing to the tech-industry which is continuously seeking to design ground-breaking products that excel in every performance measure, especially when it comes to lives-saving technologies such as in fields of civil defense, security, emergency, and tactical missions. This project introduces ML to tactical wireless networks (TWN), a different form of commercial communication networks, which is characterized by its robustness in constantly changing tactical situations and varying environments, with no pre-existing infrastructure. The sensitive nature of TWN applications forces the companies to independently develop their own technologies to ensure that the security and reliability standards are uncompromised. Thus, the tactical communications industry is highly competitive and requires strong R&D efforts from companies to keep their products innovative and competitive. In this respect, the collaboration between Ultra Electronics TCS and ETS, with the aim of introducing the recent breakthroughs in ML to tactical communications, is vital to keep the company among the major players in the market. The long-term objective of this project is to develop a proactive, self-aware, self-adaptive, and predictive tactical wireless networks that exploit ML and optimization tools to automate its deployment, operations and subsequently enhance its performance in challenging environments. The envisioned approach for adding such intelligent capabilities to the TWN is designing novel ML schemes at three network levels, the node-level for environment classification, link-level for decisions between two nodes, and cluster-level for the MAC and routing decisions. This research project will contribute to consolidating the stature of Canada as a leader in AI and wireless nets.