LaMI-GO: Latent Mixture Integration for Goal-Oriented Communications Achieving High Spectrum Efficiency.

Abstract

The recent rise of semantic-style communications has fostered the development of goal-oriented communications (GO-COMs), facilitating remarkably efficient multimedia information transmissions. The concept of GO-COMs leverages advanced artificial intelligence (AI) tools to address the rising demand for bandwidth efficiency in applications, such as edge computing and the Internet of Things (IoT). Unlike traditional communication systems focusing on source data accuracy, GO-COMs provide intelligent message delivery catering to the special needs critical to accomplishing downstream tasks at the receiver. In this work, we present a novel GO-COM framework, namely LaMI-GO, that utilizes emerging generative AI for better quality of service (QoS) with ultrahigh communication efficiency. Specifically, we design our LaMI-GO system backbone based on a latent diffusion model followed by a vector-quantized generative adversarial network (VQGAN) for efficient latent embedding and information representation. The system trains a common-feature codebook for the receiver side. Our experimental results demonstrate substantial improvement in perceptual quality, accuracy of downstream tasks, and bandwidth consumption over the state-of-the-art GO-COM systems and establish the power of our proposed LaMI-GO communication framework.