Beyond Speed defines specific social and economic outcomes to which Next G can contribute, key aspects of digital equity, defining a trustworthy 6G network and how Next G connectivity will directly impact economic growth. Challenges in areas including supply chain and equal access are addressed as well as those that may arise when defining QoL metrics. This paper also presents promising areas for future research and issues a call to action for research, development, policies and business models.
A comprehensive survey on quantum computer usage: How many qubits are employed for what purposes? – Arxiv
Most news on quantum computers (QCs) consists of outstanding
experiments, and thereby we may overlook how typical usages of QCs have been. Such oversight,
if exists, could lead to an insufficient understanding of the present situation in the research and development of QCs and may
result in possible obstacles to long-term research design. In other words, we need an alternative perspective based on typical
usages as well as the standard one based on monumental experiments.
Architecture determines what and how the network services are provided, as well as the overall system efficiency, scalability, etc. This article proposes high-level designs of the 6G architecture. Three key innovation motivations are analysed and the gaps of 5G network are identified according to the operator’s experience in real large-scale network deployment. Those observations help to generate the key “Design Principles” about the 6G architecture.
Edge Learning for 6G-enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses – Arxiv
Edge learning is a new and powerful approach to training models across distributed clients while protecting the privacy of their data.
This approach is expected to be embedded within future network infrastructures, including 6G, to solve challenging problems such as resource management and behaviour prediction. However, edge learning in general, and distributed deep learning, in particular, have been discovered to be susceptible to tampering and manipulation. This survey article provides a holistic review of the most recent research focused on edge learning vulnerabilities and defenses for 6G-enabled IoT.
A Survey on Explainable AI for 6G O-RAN: Architecture, Use Cases, Challenges and Research Directions – IEEE
Aided by Artificial Intelligence (AI) and Machine Learning (ML), novel solutions targeting traditionally unsolved RAN management issues can be devised. Nevertheless, the adoption of such smart and autonomous systems is limited by the current inability of human operators to understand the decision process of such AI/ML solutions, affecting their trust in such novel tools. eXplainable AI (XAI) aims at solving this issue, enabling human users to better understand and effectively manage the emerging generation of artificially intelligent schemes, reducing the human-to machine barrier.
6G networks are expected to achieve full autonomous operation through the adoption of intelligent algorithms. This calls for
the introduction of an additional stratum in the network devoted to the management and orchestration of network intelligence. This paper outlines such a layer, its composition and function.
eBPF: A New Approach to Cloud-Native Observability, Networking and Security for Current (5G) and Future Mobile Networks (6G and Beyond) – Rakuten
Modern mobile communication networks and new service applications are deployed on cloud-native platforms.
In this paper, we introduce what eBPF is – referring to some of the most important open-source projects (such as Cilium and Calico), books (e.g. the recent one by Liz Rice) and presentations (e.g., the ones by Thomas Graf and his team members at Isovalent) – its potential for Telco cloud, and review some of the most promising pricing and billing models applied to this revolutionary operating system (OS) technology.
The work presented in this paper outlines the role of Optical Transport Networks (OTNs) in future networking generations. Furthermore, key emerging OTN technologies are discussed. Additionally, the role intelligence will play in the Management and Orchestration (MANO) of next-generation OTNs is discussed. Moreover, a set of challenges and opportunities for innovation to guide the development of future OTNs is considered.