Advances in mobile communication capabilities open the door for closer
integration of pre-hospital and in-hospital care processes. For example,
medical specialists can be enabled to guide on-site paramedics and can, in
turn, be supplied with live vitals or visuals. Consolidating such
performance-critical applications with the highly complex workings of mobile
communications requires solutions both reliable and efficient, yet easy to
integrate with existing systems. This paper explores the application of Deep
Deterministic Policy Gradient~(\ddpg) methods for learning a communications
resource scheduling algorithm with special regards to priority users. Unlike
the popular Deep-Q-Network methods, the \ddpg is able to produce
continuous-valued output. With light post-processing, the resulting scheduler
is able to achieve high performance on a flexible sum-utility goal.

通过应用深加性策略梯度方法，学习一种针对优先用户的通信资源调度算法，旨在实现移动通信能力与医疗专家的紧密集成，提高预院急救与院内治疗协调一致性，以及整合现有系统。

利用深度确定性策略梯度进行高优先级用户学习资源调度

Learning Resource Scheduling with High Priority Users using Deep  Deterministic Policy Gradients

Recently, along with the rapid development of mobile communication
technology, edge computing theory and techniques have been attracting more and
more attentions from global researchers and engineers, which can significantly
bridge the capacity of cloud and requirement of devices by the network edges,
and thus can accelerate the content deliveries and improve the quality of
mobile services. In order to bring more intelligence to the edge systems,
compared to traditional optimization methodology, and driven by the current
deep learning techniques, we propose to integrate the Deep Reinforcement
Learning techniques and Federated Learning framework with the mobile edge
systems, for optimizing the mobile edge computing, caching and communication.
And thus, we design the "In-Edge AI" framework in order to intelligently
utilize the collaboration among devices and edge nodes to exchange the learning
parameters for a better training and inference of the models, and thus to carry
out dynamic system-level optimization and application-level enhancement while
reducing the unnecessary system communication load. "In-Edge AI" is evaluated
and proved to have near-optimal performance but relatively low overhead of
learning, while the system is cognitive and adaptive to the mobile
communication systems. Finally, we discuss several related challenges and
opportunities for unveiling a promising upcoming future of "In-Edge AI".

本文提出了一种针对移动边缘计算的 “In-Edge AI” 框架，结合深度强化学习和联邦学习技术，利用设备和边缘节点之间的协作来优化移动边缘计算、缓存和通信，从而在减少系统通信负载的同时进行动态的系统级优化和应用级增强，具有近乎最优的性能。

基于联邦学习的智能化移动边缘计算、缓存与通信

In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and  Communication by Federated Learning

Mobile communication systems now constitute an essential part of life
throughout the world. Fourth generation "Long Term Evolution" (LTE) mobile
communication networks are being deployed. The LTE suite of specifications is
considered to be significantly better than its predecessors not only in terms
of functionality but also with respect to security and privacy for subscribers.
We carefully analyzed LTE access network protocol specifications and
uncovered several vulnerabilities. Using commercial LTE mobile devices in real
LTE networks, we demonstrate inexpensive, and practical attacks exploiting
these vulnerabilities. Our first class of attacks consists of three different
ways of making an LTE device leak its location: A semi-passive attacker can
locate an LTE device within a 2 sq.km area within a city whereas an active
attacker can precisely locate an LTE device using GPS co-ordinates or
trilateration via cell-tower signal strength information. Our second class of
attacks can persistently deny some or all services to a target LTE device. To
the best of our knowledge, our work constitutes the first publicly reported
practical attacks against LTE access network protocols.
We present several countermeasures to resist our specific attacks. We also
discuss possible trade-offs that may explain why these vulnerabilities exist
and recommend that safety margins introduced into future specifications to
address such trade-offs should incorporate greater agility to accommodate
subsequent changes in the trade-off equilibrium.

LTE 移动通信网络具有显着的功能优势，但研究发现其协议规范存在漏洞，攻击者可以利用这些漏洞实施多种攻击，作者提出了多种应对方案并建议未来规范引入安全保障机制