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

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Xuất bản năm:	arXiv.org (Dec 18, 2024), p. n/a
Tác giả chính:	Wijesinghe, Achintha
Tác giả khác:	Suchinthaka Wanninayaka, Wang, Weiwei, Yu-Chieh, Chao, Zhang, Songyang, Ding, Zhi
Được phát hành:	Cornell University Library, arXiv.org
Những chủ đề:	Accuracy Communications systems Internet of Things Communication Quality of service architectures Software Efficiency Generative artificial intelligence Edge computing Generative adversarial networks Bandwidths
Truy cập trực tuyến:	Citation/Abstract Full text outside of ProQuest
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Miêu tả
Bài tóm tắt:	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.
số ISSN:	2331-8422
Nguồn:	Engineering Database