Task-Oriented JSCC with Adaptive Deep Compressed Sensing

In this letter, we propose a Task-oriented Joint Source-Channel Coding framework based on Compressed Sensing (TCS-JSCC), designed to enable end-to-end wireless image transmission for downstream tasks without requiring full image reconstruction. The framework integrates a rate-adaptive CS module with the unique ability to adaptively encode semantically meaningful features informed by both image contents and channel conditions. Specifically, semantic feature representations and signal-to-noise ratio (SNR) are jointly exploited to enable dynamic rate allocation and enhance robustness against channel impairments. A lightweight decoder then processes the received features to perform task-specific inference, such as face detection and image classification, directly on the compressed data. Experimental results demonstrate that TCS-JSCC achieves competitive task accuracy under varying channel conditions, offering a scalable and effective solution for edge-intelligent, bandwidth-constrained applications.