Fifth generation (5G) network and beyond envision massive Internet of Things
(IoT) rollout to support disruptive applications such as extended reality (XR),
augmented/virtual reality (AR/VR), industrial automation, autonomous driving,
and smart everything which brings together massive and diverse IoT devices
occupying the radio frequency (RF) spectrum. Along with the spectrum crunch and
throughput challenges, such a massive scale of wireless devices exposes
unprecedented threat surfaces. RF fingerprinting is heralded as a candidate
technology that can be combined with cryptographic and zero-trust security
measures to ensure data privacy, confidentiality, and integrity in wireless
networks. Motivated by the relevance of this subject in the future
communication networks, in this work, we present a comprehensive survey of RF
fingerprinting approaches ranging from a traditional view to the most recent
deep learning (DL)-based algorithms. Existing surveys have mostly focused on a
constrained presentation of the wireless fingerprinting approaches, however,
many aspects remain untold. In this work, however, we mitigate this by
addressing every aspect - background on signal intelligence (SIGINT),
applications, relevant DL algorithms, systematic literature review of RF
fingerprinting techniques spanning the past two decades, discussion on
datasets, and potential research avenues - necessary to elucidate this topic to
the reader in an encyclopedic manner.

本文探讨了将 RF 指纹技术与密码学和零信任安全措施相结合以保障无线网络中数据隐私、机密性和完整性的可行性，并综述了 RF 指纹技术的历史、应用、相关的深度学习算法，并探讨了该领域未来的发展方向。

无线电频率指纹识别综述：传统方法、深度学习及开放挑战

A Comprehensive Survey on Radio Frequency (RF) Fingerprinting: Traditional Approaches, Deep Learning, and Open Challenges

The 5G network connecting billions of Internet-of-Things (IoT) devices will
make it possible to harvest an enormous amount of real-time mobile data.
Furthermore, the 5G virtualization architecture will enable cloud computing at
the (network) edge. The availability of both rich data and computation power at
the edge has motivated Internet companies to deploy artificial intelligence
(AI) there, creating the hot area of edge-AI. Edge learning, the theme of this
project, concerns training edge-AI models, which endow on IoT devices
intelligence for responding to real-time events. However, the transmission of
high-dimensional data from many edge devices to servers can result in excessive
communication latency, creating a bottleneck for edge learning. Traditional
wireless techniques deigned for only radio access are ineffective in tackling
the challenge. Attempts to overcome the communication bottleneck has led to the
development of a new class of techniques for intelligent radio resource
management (RRM), called data-importance aware RRM. Their designs feature the
interplay of active machine learning and wireless communication. Specifically,
the metrics that measure data importance in active learning (e.g.,
classification uncertainty and data diversity) are applied to RRM for efficient
acquisition of distributed data in wireless networks to train AI models at
servers. This article aims at providing an introduction to the emerging area of
importance-aware RRM. To this end, we will introduce the design principles,
survey recent advancements in the area, discuss some design examples, and
suggest some promising research opportunities.

本文主要介绍了一个名为数据重要性感知无线资源管理的新技术，通过将主动学习中用于度量数据重要性的指标应用于无线资源管理以进行有效的分布式数据获取来解决边缘学习中的通信瓶颈问题。该技术通过 5G 网络连接数十亿的物联网设备，实现边缘计算并部署人工智能产生了广泛应用。

边缘机器学习数据重要性感知无线资源管理综述

An Overview of Data-Importance Aware Radio Resource Management for Edge  Machine Learning

Driven by the emergence of new compute-intensive applications and the vision
of the Internet of Things (IoT), it is foreseen that the emerging 5G network
will face an unprecedented increase in traffic volume and computation demands.
However, end users mostly have limited storage capacities and finite processing
capabilities, thus how to run compute-intensive applications on
resource-constrained users has recently become a natural concern. Mobile edge
computing (MEC), a key technology in the emerging fifth generation (5G)
network, can optimize mobile resources by hosting compute-intensive
applications, process large data before sending to the cloud, provide the cloud
computing capabilities within the radio access network (RAN) in close proximity
to mobile users, and offer context-aware services with the help of RAN
information. Therefore, MEC enables a wide variety of applications, where the
real-time response is strictly required, e.g., driverless vehicles, augmented
reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G
could become a reality with the advent of new technological concepts. The
successful realization of MEC in the 5G network is still in its infancy and
demands for constant efforts from both academic and industry communities. In
this survey, we first provide a holistic overview of MEC technology and its
potential use cases and applications. Then, we outline up-to-date researches on
the integration of MEC with the new technologies that will be deployed in 5G
and beyond. We also summarize testbeds and experimental evaluations, and open
source activities, for edge computing. We further summarize lessons learned
from state-of-the-art research works as well as discuss challenges and
potential future directions for MEC research.

该研究论文概述了移动边缘计算技术的现状和潜在应用，并提出与 5G 及更高版本的新技术相结合的 MEC 方案，最终总结了当前研究现状和挑战，并探讨了潜在未来方向。