Zobrazeno 1 - 10
of 217
pro vyhledávání: '"Zhang Ruichen"'
Autor:
Liu, Yuan, Zhang, Ruichen, Jiang, Ruihong, Zhu, Yongdong, Hu, Huimin, Ni, Qiang, Fei, Zesong, Niyato, Dusit
This paper delves into an integrated sensing and communication (ISAC) system bolstered by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). Within this system, a base station (BS) is equipped with communicati
Externí odkaz:
http://arxiv.org/abs/2411.09154
Autor:
Zhang, Ruichen, He, Jiayi, Luo, Xiaofeng, Niyato, Dusit, Kang, Jiawen, Xiong, Zehui, Li, Yonghui, Sikdar, Biplab
The rapid development of generative AI technologies, including large language models (LLMs), has brought transformative changes to various fields. However, deploying such advanced models on mobile and edge devices remains challenging due to their hig
Externí odkaz:
http://arxiv.org/abs/2411.09148
Autor:
Guo, Yangbo, Fan, Jianhui, Zhang, Ruichen, Chang, Baofang, Ng, Derrick Wing Kwan, Niyato, Dusit, Kim, Dong In
This paper investigates intelligent reflecting surface (IRS)-assisted multiple-input single-output (MISO) visible light communication (VLC) networks utilizing the rate-splitting multiple access (RSMA) scheme. {In these networks,} an eavesdropper (Eve
Externí odkaz:
http://arxiv.org/abs/2411.09146
Autor:
Zhang, Ruichen, Yao, Yuguang, Tan, Zhen, Li, Zhiming, Wang, Pan, Liu, Huan, Hu, Jingtong, Liu, Sijia, Chen, Tianlong
Image generation is a prevailing technique for clinical data augmentation for advancing diagnostic accuracy and reducing healthcare disparities. Diffusion Model (DM) has become a leading method in generating synthetic medical images, but it suffers f
Externí odkaz:
http://arxiv.org/abs/2410.22551
Autor:
Liu, Jian, Xiao, Ming, Wen, Jinbo, Kang, Jiawen, Zhang, Ruichen, Zhang, Tao, Niyato, Dusit, Zhang, Weiting, Liu, Ying
Mobile Artificial Intelligence-Generated Content (AIGC) networks enable massive users to obtain customized content generation services. However, users still need to download a large number of AIGC outputs from mobile AIGC service providers, which str
Externí odkaz:
http://arxiv.org/abs/2409.17506
Autor:
Zheng, Jiakang, Zhang, Jiayi, Du, Hongyang, Zhang, Ruichen, Niyato, Dusit, Dobre, Octavia A., Ai, Bo
Cell-free (CF) massive multiple-input multipleoutput (MIMO) provides a ubiquitous coverage to user equipments (UEs) but it is also susceptible to interference. Ratesplitting (RS) effectively extracts data by decoding interference, yet its effectivene
Externí odkaz:
http://arxiv.org/abs/2409.14702
In this letter, we present a diffusion model method for signal detection in near-field communication with unknown noise characteristics. We consider an uplink transmission of a near-filed MIMO communication system consisting of multiple mobile termin
Externí odkaz:
http://arxiv.org/abs/2409.14031
Autor:
Zhang, Ruichen, Du, Hongyang, Niyato, Dusit, Kang, Jiawen, Xiong, Zehui, Zhang, Ping, Kim, Dong In
In the continued development of next-generation networking and artificial intelligence content generation (AIGC) services, the integration of multi-agent systems (MAS) and the mixture of experts (MoE) frameworks is becoming increasingly important. Mo
Externí odkaz:
http://arxiv.org/abs/2405.12472
By integrating Artificial Intelligence (AI) with the Internet of Things (IoT), Artificial Intelligence of Things (AIoT) has revolutionized many fields. However, AIoT is facing the challenges of energy consumption and carbon emissions due to the conti
Externí odkaz:
http://arxiv.org/abs/2404.18077
Graph neural networks (GNNs) have been regarded as the basic model to facilitate deep learning (DL) to revolutionize resource allocation in wireless networks. GNN-based models are shown to be able to learn the structural information about graphs repr
Externí odkaz:
http://arxiv.org/abs/2404.11858