This paper introduces a deep learning approach to dynamic spectrum access, leveraging the synergy of multi-modal image and spectrum data for the identification of potential transmitters. We consider an edge device equipped with a camera that is taking images of potential objects such as vehicles that may harbor transmitters. Recognizing the computational constraints and trust issues associated with on-device computation, we propose a collaborative system wherein the edge device communicates selectively processed information to a trusted receiver acting as a fusion center, where a decision is made to identify whether a potential transmitter is present, or not. To achieve this, we employ task-oriented communications, utilizing an encoder at the transmitter for joint source coding, channel coding, and modulation. This architecture efficiently transmits essential information of reduced dimension for object classification. Simultaneously, the transmitted signals may reflect off objects and return to the transmitter, allowing for the collection of target sensing data. Then the collected sensing data undergoes a second round of encoding at the transmitter, with the reduced-dimensional information communicated back to the fusion center through task-oriented communications. On the receiver side, a decoder performs the task of identifying a transmitter by fusing data received through joint sensing and task-oriented communications. The two encoders at the transmitter and the decoder at the receiver are jointly trained, enabling a seamless integration of image classification and wireless signal detection. Using AWGN and Rayleigh channel models, we demonstrate the effectiveness of the proposed approach, showcasing high accuracy in transmitter identification across diverse channel conditions while sustaining low latency in decision making.

翻译：本文提出一种利用多模态图像与频谱数据协同识别潜在发射源的深度学习动态频谱接入方法。我们考虑在边缘设备上安装摄像头，用于拍摄可能搭载发射源的车辆等潜在目标物体。针对设备端计算能力受限及信任问题，提出协作系统框架：边缘设备选择性地传输经处理的信息至作为融合中心的可信接收端，由该中心决策是否存在潜在发射源。为实现该目标，我们采用面向任务的通信机制，在发射端部署编码器实现联合信源编码、信道编码与调制。该架构高效传输用于目标分类的降维关键信息。同时，发射信号经目标反射回发射端，可用于采集目标感知数据。随后采集的感知数据在发射端进行二次编码，降维后的信息再次通过面向任务的通信传输至融合中心。接收端解码器通过融合联合感知与面向任务通信的数据，完成发射源识别任务。发射端双编码器与接收端解码器通过联合训练实现图像分类与无线信号检测的深度耦合。基于AWGN与瑞利信道模型的仿真实验表明，所提方法在不同信道条件下均能保持高精度发射源识别与低延迟决策性能。