Zobrazeno 1 - 10
of 1 741
pro vyhledávání: '"Chen, Xiaojing"'
Autor:
Chen, Xiaojing, Li, Zhenyuan, Ni, Wei, Wang, Xin, Zhang, Shunqing, Sun, Yanzan, Xu, Shugong, Pei, Qingqi
Federated learning (FL) is a viable technique to train a shared machine learning model without sharing data. Hierarchical FL (HFL) system has yet to be studied regrading its multiple levels of energy, computation, communication, and client scheduling
Externí odkaz:
http://arxiv.org/abs/2406.14910
In order to provide design guidelines for energy efficient 6G networks, we propose a novel radial basis function (RBF) based optimization framework to maximize the integrated relative energy efficiency (IREE) metric. Different from the conventional e
Externí odkaz:
http://arxiv.org/abs/2405.12664
Extended Reality (XR) is an important service in the 5G network and in future 6G networks. In contrast to traditional video on demand services, real-time XR video is transmitted frame-by-frame, requiring low latency and being highly sensitive to netw
Externí odkaz:
http://arxiv.org/abs/2405.07689
To address the issue of increased bit error rates during the later stages of linear search in denoising diffusion error correction codes, we propose a novel method that optimizes denoising diffusion error correction codes (ECC) using cosine annealing
Externí odkaz:
http://arxiv.org/abs/2405.03638
Extended reality (XR) is one of the most important applications of beyond 5G and 6G networks. Real-time XR video transmission presents challenges in terms of data rate and delay. In particular, the frame-by-frame transmission mode of XR video makes r
Externí odkaz:
http://arxiv.org/abs/2404.09905
Extended reality (XR) is one of the most important applications of 5G. For real-time XR video transmission in 5G networks, a low latency and high data rate are required. In this paper, we propose a resource allocation scheme based on frame-priority s
Externí odkaz:
http://arxiv.org/abs/2402.01180
Autor:
Zheng, Yuqi, Yan, Ruidong, Jia, Bin, Jiang, Rui, TAPUS, Adriana, Chen, Xiaojing, Zheng, Shiteng, Shang, Ying
In autonomous driving, the hybrid strategy of deep reinforcement learning and cooperative adaptive cruise control (CACC) can fully utilize the advantages of the two algorithms and significantly improve the performance of car following. However, it is
Externí odkaz:
http://arxiv.org/abs/2312.15993
The theory of entanglement-assisted quantum error-correcting codes (EAQECCs) is a generalization of the standard stabilizer quantum error-correcting codes, which can be possibly constructed from any classical codes by relaxing the duality condition a
Externí odkaz:
http://arxiv.org/abs/2305.08517
The high computational complexity and energy consumption of artificial intelligence (AI) algorithms hinder their application in augmented reality (AR) systems. However, mobile edge computing (MEC) makes it possible to solve this problem. This paper c
Externí odkaz:
http://arxiv.org/abs/2301.01010
As a core performance metric for green communications, the conventional energy efficiency definition has successfully resolved many issues in the energy efficient wireless network design. In the past several generations of wireless communication netw
Externí odkaz:
http://arxiv.org/abs/2210.02883