Intelligent Tactical Wireless Networks for Challenging Environments

近年来，在机器学习(ML)的推动下，人工智能(AI)给许多技术带来了革命性的变化，推动了它们的性能和智能能力超出预期。因此，旨在提高无线网络的健壮性、可靠性、吞吐量等特性的新型智能无线解决方案不断涌现。这些解决方案对科技行业很有吸引力，他们一直在寻求设计在每一项性能指标上都出类拔萃的突破性产品，特别是在民防、安全、紧急情况和战术任务等领域的救生技术方面。 该项目将ML引入战术无线网络(TWN)，这是一种不同形式的商业通信网络，其特点是在不存在预先存在的基础设施的情况下，对不断变化的战术情况和变化的环境具有健壮性。TWN应用的敏感性迫使这些公司独立开发自己的技术，以确保安全和可靠性标准不受影响。因此，战术通信行业竞争激烈，需要公司做出强有力的研发努力，以保持其产品的创新性和竞争力。在这方面，Ultra Electronics TCS和ETS之间的合作，旨在将ML的最新突破引入战术通信，对于保持该公司在市场上的主要参与者至关重要。 该项目的长期目标是开发一个主动、自我感知、自适应和预测的战术无线网络，利用ML和优化工具来自动化其部署、操作，并随后在具有挑战性的环境中提高其性能。 设想的将这种智能能力添加到TWN的方法是在三个网络级别设计新的ML方案，节点级别用于环境分类，链路级别用于两个节点之间的决策，以及簇级用于MAC和路由决策。 这一研究项目将有助于巩固加拿大作为人工智能和无线网络领先者的地位。

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.