Efficient, Sustainable and Intelligent Resource Allocation Approaches for the Next Generation of Wireless Communication Systems

工业界和学术界的研究人员正在研究未来无线通信系统（WCS）的驱动应用，使能技术和挑战。已经设想了几个开创性的和多样化的应用程序和用例，它们在数据速率，延迟，可靠性和大规模访问方面需要严格的性能。此外，能源效率被视为未来WCS的主要设计标准。未来WCS预期的性能和能效提升只能通过具有挑战性的技术变革来实现，例如可重新配置的智能表面、转向更高频段、以用户为中心的无蜂窝大规模多输入多输出架构以及无人机无线网络。 目标-为了使技术能够实现WCS的严格性能要求，需要引入人工智能（称为“智能化”），并将其视为未来WCS的主要范式转变。特别是，资源规划和管理应该利用机器学习和知识共享技术的进步，在未来高度复杂/动态的WCS中变得更加动态和认知。因此，发现补助金（DG）研究计划的主要长期目标是探索和提出创新的智能（基于学习），高性能和节能的资源分配和规划方法和解决方案，为未来的WCS。我们详尽的文献调查允许指定几个开放的问题，以解决在未来的无线系统中的新兴无线技术的有效利用，并定义特定的短期目标。方法-我们将开发有效和易于处理的无线系统模型进行研究，并制定数学优化问题。最优和次优算法解决方案将根据集中式或分布式方法设计。接下来，我们将专注于为公式化的问题设计基于机器学习的解决方案。特别是，我们将广泛利用强化学习、联邦学习和迁移学习的最新进展来设计我们的解决方案。影响-该DG计划的好处主要是在知识的进步和WCS的工业进步领域。它将建立专业知识，探索新的研究问题，并设计高效的解决方案，以实现节能型卫生控制系统的智能化。理论和模拟结果将有助于基础和实践知识，并提供可利用的见解和更好地了解未来的WCS。该DG计划将通过解决工业问题并为未来的WCS提出实用的智能算法解决方案，为加拿大无线行业做出贡献。它还将允许多名学生的高质量培训，从而满足无线行业几个新兴领域对高素质人才的需求。

The industrial and academic researchers are investigating the driving applications, enabling technologies and challenges of the future wireless communication systems (WCSs). Several pioneering and diverse applications and use-cases have been conceived, and they require stringent performance in terms of data rate, latency, reliability and massive access. Also, energy efficiency is seen as a major design criterion in future WCSs. The performance and energy efficiency gains expected from future WCSs can only be realized through challenging technological changes, such as reconfigurable intelligent surfaces, moving to higher frequency bands, user-centric cell-free massive multiple-input-multiple-output architecture, and drone-based wireless networks. Objectives - To allow the enabling technologies to achieve the stringent performance requirements of WCSs, the introduction of artificial intelligence (named "intelligentisation") is needed and seen as the major paradigm shift towards future WCSs. In particular, resource planning and management should exploit the progress in machine learning and knowledge sharing techniques to become more dynamic and cognitive in the future highly complex/dynamic WCSs. Accordingly, the main long-term objective of the Discovery Grant (DG) research program is to explore and propose innovative intelligent (learning-based), high-performance, and energy-efficient resource allocation and planning approaches and solutions for future WCSs. Our exhaustive literature survey allowed to specify several open problems to be solved for an efficient use of the emerging wireless technologies in future wireless systems and to define specific short-term objectives. Methodology - We will develop valid and tractable models of the wireless systems to be studied and formulate mathematical optimisation problems. Optimal and suboptimal algorithmic solutions will be designed based on centralized or distributed approaches. Next, we will focus on designing machine learning-based solutions for the formulated problems. In particular, we will extensively exploit the recent progress in reinforcement learning, federated learning and transfer learning to design our solutions. Impact - The benefits of this DG program are mainly in the areas of advancement of knowledge and industrial advances in WCSs. It will establish expertise, explore new research issues, and design efficient solutions towards the intelligentisation of energy-efficient WCSs. The theoretical and simulation results will contribute to both fundamental and practical knowledge, and provide exploitable insights and a better understanding of future WCSs. This DG program will contribute to the Canadian wireless industry by solving industrial problems and proposing practical intelligent algorithmic solutions for future WCSs. It will also allow a high-quality training of several students and thus meet the need of highly qualified people in several emerging areas of wireless industry.