Synesthesia of Machines (SoM)-Based Task-Driven MIMO System for Image Transmission
It addresses the problem of supporting complex cooperative perception tasks for networked mobile agents with digital MIMO systems, representing an incremental advancement over existing MIMO JSCC schemes.
This paper tackles the challenge of efficient and robust image transmission for cooperative perception in networked mobile agents by proposing a Synesthesia of Machines-based task-driven MIMO system, achieving average mAP improvements of 6.30 and 10.48 compared to baseline schemes while maintaining identical communication overhead.
To support cooperative perception (CP) of networked mobile agents in dynamic scenarios, the efficient and robust transmission of sensory data is a critical challenge. Deep learning-based joint source-channel coding (JSCC) has demonstrated promising results for image transmission under adverse channel conditions, outperforming traditional rule-based codecs. While recent works have explored to combine JSCC with the widely adopted multiple-input multiple-output (MIMO) technology, these approaches are still limited to the discrete-time analog transmission (DTAT) model and simple tasks. Given the limited performance of existing MIMO JSCC schemes in supporting complex CP tasks for networked mobile agents with digital MIMO communication systems, this paper presents a Synesthesia of Machines (SoM)-based task-driven MIMO system for image transmission, referred to as SoM-MIMO. By leveraging the structural properties of the feature pyramid for perceptual tasks and the channel properties of the closed-loop MIMO communication system, SoM-MIMO enables efficient and robust digital MIMO transmission of images. Experimental results have shown that compared with two JSCC baseline schemes, our approach achieves average mAP improvements of 6.30 and 10.48 across all SNR levels, while maintaining identical communication overhead.