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.

翻译：迄今为止，通信系统的主要设计目标在于实现数字序列（比特）的可靠传输。下一代通信系统正开始探索将这一设计范式转向可靠执行特定任务，例如任务导向通信。本文以无线信号分类作为下一代无线接入网（RAN）的任务场景：边缘设备收集用于频谱感知的无线信号，并与需要识别信号标签的下一代基站（gNodeB）进行通信。边缘设备可能因处理能力不足或不可信而无法执行信号分类任务，同时将信号传输至gNodeB可能因严格的延迟、速率及能量限制而不可行。本文通过将发射机、接收机和分类功能联合训练为边缘设备与gNodeB的编码器-解码器对，实现任务导向通信。与先传输信号后分类的分离方案相比，该方法提升了分类精度。对抗性机器学习对任务导向通信中深度学习的使用构成重大安全威胁。研究表明，当后门（木马）攻击和对抗（逃逸）攻击分别针对任务导向通信的训练与测试过程时，系统性能将出现显著下降。