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

TL;DR

LaMI-GO is a novel goal-oriented communication framework that leverages generative AI, including latent diffusion models and VQGAN, to enhance perceptual quality, task accuracy, and bandwidth efficiency in multimedia transmissions.

Contribution

This work introduces LaMI-GO, a new AI-driven GO-COM system utilizing latent diffusion and VQGAN for improved efficiency and quality in goal-oriented communications.

Findings

Significant improvement in perceptual quality over existing systems

Enhanced accuracy of downstream tasks

Reduced bandwidth consumption

Abstract

The recent rise of semantic-style communications includes the development of goal-oriented communications (GOCOMs) 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 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 ultra-high 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 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 GOCOM systems and establish the power of our proposed LaMI-GO communication framework.