  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.
