Intelligent Framework for the Future-Proof Heterogeneous Ultra-Dense Networks: a Foresight for the Emergence of 6G

面向未来的异构超密集网络智能框架：对6G出现的预见

• 批准号: 560833-2020
• 负责人: Kaddoum, Georges
• 金额: $ 2.19万
• 依托单位: École de technologie supérieure
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=706994
• 关键词: Intelligent; Framework; Future; Proof; Heterogeneous

We are witnessing a formidable growth in the number of Internet of things (IoT) and the emergence of revolutionary technologies that are essential parts of our daily lives, especially those supported by artificial intelligence (AI). To satisfy the future thirst for high-data-rates, as high as 1TB/s, researchers and industry have started conceptualizing 6G networks. Our original FRQNT-Team project proposes a novel intelligent framework that lays down the foundations for 6G, considering the massive heterogeneous-connectivity, throughput, highly-diverse requirements, and quality-of-service. The proposed solutions leverage the recent developments in graph-representation-learning (GRL), network science, communication theory, and machine-learning techniques for clustering, resource management (RM), and random access techniques in the heterogeneous ultra-dense network (H-UDN), which is the essential 6G paradigm. This proposal is a supplement to the FRQNT-project and complements its objectives by leveraging the concept of intelligent reflecting surfaces (IRSs) to provide ultra-reliable and low latency communication. IRS is one of the key technologies for the 6G mobile communications that significantly improves the performance of wireless communication by reconfiguring the propagation environment using low-cost passive reflecting elements. The IRS system will be an essential part of the H-UDN's resources and will be managed by the techniques proposed in the original FRQNT project. In this supplement, we will expand our H-UDN network design and analysis by including the IRS-aided data transmission aiming to provide high flexibility and superior compatibility. Novel channel estimation techniques will be driven for the proposed architecture. We will investigate different pertinent optimization of the IRS's parameters in H-UDN to improve the network performance metrics such as sum-rate, energy efficiency, or outage probability. Finally, a performance analysis will be conducted for the whole system. This project provides leading-edge training in the most demanded fields of industry 4.0, and contribute to advance knowledge in AI and H-UDN.

我们目睹了物联网数量（物联网）的巨大增长，以及革命性技术的出现，这些技术是我们日常生活中必不可少的部分，尤其是那些受人工智能（AI）支持的部分。为了满足未来对高数据速率的渴望，研究人员和行业已经开始概念化6G网络。我们最初的FRQNT团队项目提出了一个新颖的智能框架，考虑到大量的异质连接性，吞吐量，高度多样性要求和服务质量，该框架为6G基础奠定了基础。拟议的解决方案利用了图形代表性学习（GRL），网络科学，通信理论和机器学习技术的最新发展，用于聚类，资源管理（RM）和随机访问技术在异质性超密集网络中（H） -udn），这是必需的6G范式。 该提案是对FRQNT项目的补充，并通过利用智能反射表面（IRS）的概念来补充其目标，以提供超可靠和低的延迟通信。 IRS是6G移动通信的关键技术之一，可通过使用低成本被动反射元素重新配置传播环境，从而显着提高无线通信的性能。 IRS系统将是H-UDN资源的重要组成部分，并将通过原始FRQNT项目中提出的技术来管理。在此补充剂中，我们将通过包括IRS辅助数据传输来扩展H-UDN网络设计和分析，旨在提供高灵活性和出色的兼容性。新的渠道估计技术将用于拟议的架构。我们将研究H-UDN中IRS参数的不同相关优化，以改善网络性能指标，例如总和率，能源效率或中断概率。最后，将为整个系统进行性能分析。该项目在最高要求的行业4.0领域提供了领先的培训，并有助于提高AI和H-UDN的知识。