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This paper introduces a deep learning approach to dynamic spectrum access,leveraging the synergy of multi-modal image and spectrum data for theidentification of potential transmitters. We consider an edge device equippedwith a camera that is taking images of potential objects such as vehicles thatmay harbor transmitters. Recognizing the computational constraints and trustissues associated with on-device computation, we propose a collaborative systemwherein the edge device communicates selectively processed information to atrusted receiver acting as a fusion center, where a decision is made toidentify whether a potential transmitter is present, or not. To achieve this,we employ task-oriented communications, utilizing an encoder at the transmitterfor joint source coding, channel coding, and modulation. This architectureefficiently transmits essential information of reduced dimension for objectclassification. Simultaneously, the transmitted signals may reflect off objectsand return to the transmitter, allowing for the collection of target sensingdata. Then the collected sensing data undergoes a second round of encoding atthe transmitter, with the reduced-dimensional information communicated back tothe fusion center through task-oriented communications. On the receiver side, adecoder performs the task of identifying a transmitter by fusing data receivedthrough joint sensing and task-oriented communications. The two encoders at thetransmitter and the decoder at the receiver are jointly trained, enabling aseamless integration of image classification and wireless signal detection.Using AWGN and Rayleigh channel models, we demonstrate the effectiveness of theproposed approach, showcasing high accuracy in transmitter identificationacross diverse channel conditions while sustaining low latency in decisionmaking.

Joint Sensing and Task-Oriented Communications with Image and Wireless Data Modalities for Dynamic Spectrum Access