Exploring the 6G Potentials: Immersive, Hyper Reliable, and Low-Latency Communication

TL;DR

This paper explores the potentials of 6G networks by proposing a new immersive, hyper reliable, and low-latency communication service class, and suggests innovative network architectures and enabling technologies to meet stringent KPIs.

Contribution

It introduces a novel 6G service class (IHRLLC) and a corresponding network architecture utilizing advanced technologies like umMIMO, THz, RIS, and NTNs, along with computational methods such as AI and quantum computing.

Findings

Proposes a new 6G service class: IHRLLC.

Suggests network architecture with umMIMO, THz, RIS, NTNs.

Identifies computational technologies to optimize latency.

Abstract

The transition towards the sixth-generation (6G) wireless telecommunications networks introduces significant challenges for researchers and industry stakeholders. The 6G technology aims to enhance existing usage scenarios through supporting innovative applications that require stringent key performance indicators (KPIs). In some critical use cases of 6G, multiple KPIs, including immersive throughput, with an envisioned peak data rate of $1$ Tbps, hyper-reliability, in the range of $10^{-5}$ to $10^{-7}$, and hyper low-latency, between $0.1$ and $1$ ms, must be achieved simultaneously to deliver the expected service experience. However, this is challenging due to the conflicting nature of these KPIs. This article proposes a new service class of 6G as immersive, hyper reliable, and low-latency communication (IHRLLC), and introduces a potential network architecture to achieve the associated KPIs. Specifically, enhanced technologies, such as ultra-massive multiple-input multiple-output (umMIMO)-aided terahertz (THz) communications, reconfigurable intelligent surfaces (RIS), and non-terrestrial networks (NTN), are viewed as the key enablers for achieving immersive data rates and hyper reliability. Given the computational complexity involved in employing these technologies, we propose mathematical and computational enabling technologies, such as learn-to-optimize (L2O), generative-AI (GenAI), quantum computing, and network digital twin (NDT), to complement the proposed architecture and optimize the latency.