LaMI-GO: Latent Mixture Integration for Goal-Oriented Communications Achieving High Spectrum Efficiency
Journal:
arXiv
Published Date:
Dec 18, 2024
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.
