Zobrazeno 1 - 10
of 541
pro vyhledávání: '"Cai Yunlong"'
Autor:
Cai Yunlong
Publikováno v:
Redai dili, Vol 44, Iss 1, Pp 13-15 (2024)
The differentiation of physical geography and human geography is consistent with the differentiation of science, which is always in progress. Both have their advantages, and both have made contributions to scientific cognition and practical applicati
Externí odkaz:
https://doaj.org/article/75829e32fce049809253354d83968a02
Semantic communication (SC) is emerging as a pivotal innovation within the 6G framework, aimed at enabling more intelligent transmission. This development has led to numerous studies focused on designing advanced systems through powerful deep learnin
Externí odkaz:
http://arxiv.org/abs/2412.18876
Developing channel-adaptive deep joint source-channel coding (JSCC) systems is a critical challenge in wireless image transmission. While recent advancements have been made, most existing approaches are designed for static channel environments, limit
Externí odkaz:
http://arxiv.org/abs/2412.08211
Extremely large-scale arrays (XL-arrays) and ultra-high frequencies are two key technologies for sixth-generation (6G) networks, offering higher system capacity and expanded bandwidth resources. To effectively combine these technologies, it is necess
Externí odkaz:
http://arxiv.org/abs/2410.08318
In modern wireless network architectures, such as Open Radio Access Network (O-RAN), the operation of the radio access network (RAN) is managed by applications, or apps for short, deployed at intelligent controllers. These apps are selected from a gi
Externí odkaz:
http://arxiv.org/abs/2410.00150
Recent advances in deep learning-based joint source-channel coding (DJSCC) have shown promise for end-to-end semantic image transmission. However, most existing schemes primarily focus on optimizing pixel-wise metrics, which often fail to align with
Externí odkaz:
http://arxiv.org/abs/2409.02597
In this paper, we introduce an innovative hierarchical joint source-channel coding (HJSCC) framework for image transmission, utilizing a hierarchical variational autoencoder (VAE). Our approach leverages a combination of bottom-up and top-down paths
Externí odkaz:
http://arxiv.org/abs/2408.16340
Movable antenna (MA) technology can flexibly reconfigure wireless channels by adjusting antenna positions in a local region, thus owing great potential for enhancing communication performance. This letter investigates MA technology enabled multiuser
Externí odkaz:
http://arxiv.org/abs/2407.17841
Autor:
Hou, Qiushuo, Zecchin, Matteo, Park, Sangwoo, Cai, Yunlong, Yu, Guanding, Chowdhury, Kaushik, Simeone, Osvaldo
In modern wireless network architectures, such as O-RAN, artificial intelligence (AI)-based applications are deployed at intelligent controllers to carry out functionalities like scheduling or power control. The AI "apps" are selected on the basis of
Externí odkaz:
http://arxiv.org/abs/2406.15819
Deep learning-based joint source-channel coding (JSCC) is emerging as a potential technology to meet the demand for effective data transmission, particularly for image transmission. Nevertheless, most existing advancements only consider analog transm
Externí odkaz:
http://arxiv.org/abs/2406.10838