LaMI-GO: Latent Mixture Integration for Goal-Oriented Communications Achieving High Spectrum Efficiency
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| Publicado en: | arXiv.org (Dec 18, 2024), p. n/a |
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| Autor principal: | |
| Otros Autores: | , , , , |
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Cornell University Library, arXiv.org
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| Acceso en línea: | Citation/Abstract Full text outside of ProQuest |
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| 001 | 3149106957 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 3149106957 | ||
| 045 | 0 | |b d20241218 | |
| 100 | 1 | |a Wijesinghe, Achintha | |
| 245 | 1 | |a LaMI-GO: Latent Mixture Integration for Goal-Oriented Communications Achieving High Spectrum Efficiency | |
| 260 | |b Cornell University Library, arXiv.org |c Dec 18, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a 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. | |
| 653 | |a Accuracy | ||
| 653 | |a Communications systems | ||
| 653 | |a Internet of Things | ||
| 653 | |a Communication | ||
| 653 | |a Quality of service architectures | ||
| 653 | |a Software | ||
| 653 | |a Efficiency | ||
| 653 | |a Generative artificial intelligence | ||
| 653 | |a Edge computing | ||
| 653 | |a Generative adversarial networks | ||
| 653 | |a Bandwidths | ||
| 700 | 1 | |a Suchinthaka Wanninayaka | |
| 700 | 1 | |a Wang, Weiwei | |
| 700 | 1 | |a Yu-Chieh, Chao | |
| 700 | 1 | |a Zhang, Songyang | |
| 700 | 1 | |a Ding, Zhi | |
| 773 | 0 | |t arXiv.org |g (Dec 18, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3149106957/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2412.17839 |