Optimizing Real-Time Freshness: Deep Joint Source-Channel Coding Based AoI in Wireless Networks

This paper proposes a deep joint source-channel coding (DJSCC) to minimize the age of information (AoI) for image transmission. A new content-based AoI metric called age of misclassified information (AoMI) is introduced to estimate the freshness of the information in an image classification system. AoMI is a critical metric in timely information delivery, measuring the age of the most recently received and correctly classified image at the receiver. The proposed system leverages a deep neural network at the transmitter to map image pixels directly to channel input symbols, eliminating the need for separate source and channel coding. At the receiver, the channel output is processed to perform image classification. To analyze the AoMI performance of the system, a stochastic hybrid systems (SHS) approach is employed. Closed-form expressions for the average AoMI (AAoMI) are derived, providing insights into the impact of system parameters on the AoMI. Simulation results demonstrate the effectiveness of the proposed DJSCC-based system in achieving lower AoMI compared to traditional separate source and channel coding schemes. The findings highlight the potential of deep learning techniques to maintain the freshness of the information in wireless communication systems. This work paves the way for the design of wireless communication systems that prioritize the freshness of delivered information - this is crucial in applications such as real-time monitoring, surveillance, and control systems.