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

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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.