In this tutorial paper, we look into the evolution and prospect of network
architecture and propose a novel conceptual architecture for the 6th generation
(6G) networks. The proposed architecture has two key elements, i.e., holistic
network virtualization and pervasive artificial intelligence (AI). The holistic
network virtualization consists of network slicing and digital twin, from the
aspects of service provision and service demand, respectively, to incorporate
service-centric and user-centric networking. The pervasive network intelligence
integrates AI into future networks from the perspectives of networking for AI
and AI for networking, respectively. Building on holistic network
virtualization and pervasive network intelligence, the proposed architecture
can facilitate three types of interplay, i.e., the interplay between digital
twin and network slicing paradigms, between model-driven and data-driven
methods for network management, and between virtualization and AI, to maximize
the flexibility, scalability, adaptivity, and intelligence for 6G networks. We
also identify challenges and open issues related to the proposed architecture.
By providing our vision, we aim to inspire further discussions and developments
on the potential architecture of 6G.

本文研究网络架构的演进和前景，并提出了一种新的概念架构用于 6G 网络，该架构具有两个关键元素，即整体网络虚拟化和普及的人工智能。提出的架构可以促进三种类型的相互作用，即数字孪生和网络切片范式之间的相互作用，面向模型驱动和数据驱动的网络管理方法之间的相互作用和虚拟化和人工智能之间的相互作用，以最大化 6G 网络的灵活性，可扩展性，适应性和智能性。

面向 6G 的全面网络虚拟化和普适网络智能

Holistic Network Virtualization and Pervasive Network Intelligence for 6G

Network intelligence is a discipline that builds on the capabilities of
network systems to act intelligently by the usage of network resources for
delivering high-quality services in a changing environment. Wide area network
intelligence is a class of network intelligence in wide area network which
covers the core and the edge of Internet. In this paper, we propose a system
based on machine learning for wide area network intelligence. The whole system
consists of a core machine for pre-training and many terminal machines to
accomplish faster responses. Each machine is one of dual-hemisphere models
which are made of left and right hemispheres. The left hemisphere is used to
improve latency by terminal response and the right hemisphere is used to
improve communication by data generation. In an application on multimedia
service, the proposed model is superior to the latest deep feed forward neural
network in the data center with respect to the accuracy, latency and
communication. Evaluation shows scalable improvement with regard to the number
of terminal machines. Evaluation also shows the cost of improvement is longer
learning time.

本文提出了一种基于机器学习的针对广域网的智能系统，系统由用于预训练的核心机器和众多用于快速响应的终端机器构成，每个终端机器都是由左右半球构成的双半球模型，其中左半球用于通过终端响应来提高延迟，右半球则通过数据生成来提高通信，该模型在多媒体服务应用中具有较高的准确性、低延迟和高通信性能，与最新的前馈深度神经网络相比更优秀，同时随着终端机器数量的增加，系统性能也可扩展，并且成本是较长的学习时间。

应用于多媒体服务的广域网络智能

Wide Area Network Intelligence with Application to Multimedia Service

The next generation of cellular networks will be characterized by
softwarized, open, and disaggregated architectures exposing analytics and
control knobs to enable network intelligence. How to realize this vision,
however, is largely an open problem. In this paper, we take a decisive step
forward by presenting and prototyping OrchestRAN, a novel orchestration
framework that embraces and builds upon the Open RAN paradigm to provide a
practical solution to these challenges. OrchestRAN has been designed to execute
in the non-real-time RAN Intelligent Controller (RIC) and allows Network
Operators (NOs) to specify high-level control/inference objectives (i.e., adapt
scheduling, and forecast capacity in near-real-time for a set of base stations
in Downtown New York). OrchestRAN automatically computes the optimal set of
data-driven algorithms and their execution location to achieve intents
specified by the NOs while meeting the desired timing requirements. We show
that the problem of orchestrating intelligence in Open RAN is NP-hard, and
design low-complexity solutions to support real-world applications. We
prototype OrchestRAN and test it at scale on Colosseum. Our experimental
results on a network with 7 base stations and 42 users demonstrate that
OrchestRAN is able to instantiate data-driven services on demand with minimal
control overhead and latency.

本文提出并原型描述了一种基于 Open RAN 范例的新型编排框架 OrchestRAN，旨在通过自动计算最优数据驱动算法和其执行位置，实现网络智能控制和推理目标，以应对下一代软件化、开放式、分解式网络架构面临的挑战。在 Colosseum 平台上的实验结果表明，OrchestRAN 能够以最小的控制开销和延迟按需实例化数据驱动服务。