论文标题
Nextg面向任务的通信：端到端深度学习和AI安全方面
Task-Oriented Communications for NextG: End-to-End Deep Learning and AI Security Aspects
论文作者
论文摘要
迄今为止的通信系统主要是设计为可靠传输数字序列（位）的目标。下一代（NextG）通信系统开始探索转移此设计范式以可靠地执行给定的任务，例如在以任务为导向的通信中。在本文中，无线信号分类被视为NextG无线电访问网络（RAN）的任务，其中Edge设备收集无线信号以获得频谱意识，并与NextG Base Station（GNODEB）进行通信，以确定信号标签。边缘设备可能没有足够的处理能力，并且可能不信任执行信号分类任务，而信号将信号传输到GNODEB可能是由于严格的延迟，速率和能量限制而无法可行的。通过共同训练发射器，接收器和分类器功能作为边缘设备和GNODEB的编码器对来考虑以任务为导向的通信。与分开的信号传递情况随后进行分类相比，这种方法提高了准确性。对抗机器学习对将深度学习用于以任务为导向的通信构成了主要的安全威胁。当后门（特洛伊木马）和对抗（逃避）攻击针对以任务为导向的通信的训练和测试过程时，显示出重大的性能损失。
Communications systems to date are primarily designed with the goal of reliable transfer of digital sequences (bits). Next generation (NextG) communication systems are beginning to explore shifting this design paradigm to reliably executing a given task such as in task-oriented communications. In this paper, wireless signal classification is considered as the task for the NextG Radio Access Network (RAN), where edge devices collect wireless signals for spectrum awareness and communicate with the NextG base station (gNodeB) that needs to identify the signal label. Edge devices may not have sufficient processing power and may not be trusted to perform the signal classification task, whereas the transfer of signals to the gNodeB may not be feasible due to stringent delay, rate, and energy restrictions. Task-oriented communications is considered by jointly training the transmitter, receiver and classifier functionalities as an encoder-decoder pair for the edge device and the gNodeB. This approach improves the accuracy compared to the separated case of signal transfer followed by classification. Adversarial machine learning poses a major security threat to the use of deep learning for task-oriented communications. A major performance loss is shown when backdoor (Trojan) and adversarial (evasion) attacks target the training and test processes of task-oriented communications.
