Zobrazeno 1 - 10
of 8 657
pro vyhledávání: '"ZHANG Hongwei"'
Autor:
Nadim, Md, Islam, Taimoor Ul, Reddy, Salil, Zhang, Tianyi, Meng, Zhibo, Afzal, Reshal, Babu, Sarath, Ahmad, Arsalan, Qiao, Daji, Arora, Anish, Zhang, Hongwei
Time synchronization is a critical component in network operation and management, and it is also required by Ultra-Reliable, Low-Latency Communications (URLLC) in next-generation wireless systems such as those of 5G, 6G, and Open RAN. In this context
Externí odkaz:
http://arxiv.org/abs/2410.03583
In this paper, for the problem of heteroskedastic general linear hypothesis testing (GLHT) in high-dimensional settings, we propose a random integration method based on the reference L2-norm to deal with such problems. The asymptotic properties of th
Externí odkaz:
http://arxiv.org/abs/2409.12066
Autor:
Islam, Taimoor Ul, Boateng, Joshua Ofori, Nadim, Md, Zu, Guoying, Shahid, Mukaram, Li, Xun, Zhang, Tianyi, Reddy, Salil, Xu, Wei, Atalar, Ataberk, Lee, Vincent, Chen, Yung-Fu, Gosling, Evan, Permatasari, Elisabeth, Somiah, Christ, Meng, Zhibo, Babu, Sarath, Soliman, Mohammed, Hussain, Ali, Qiao, Daji, Zheng, Mai, Boyraz, Ozdal, Guan, Yong, Arora, Anish, Selim, Mohamed, Ahmad, Arsalan, Cohen, Myra B., Luby, Mike, Chandra, Ranveer, Gross, James, Zhang, Hongwei
To address the rural broadband challenge and to leverage the unique opportunities that rural regions provide for piloting advanced wireless applications, we design and implement the ARA wireless living lab for research and innovation in rural wireles
Externí odkaz:
http://arxiv.org/abs/2408.00913
Autor:
Shahid, Mukaram, Das, Kunal, Islam, Taimoor Ul, Somiah, Christ, Qiao, Daji, Ahmad, Arsalan, Song, Jimming, Zhu, Zhengyuan, Babu, Sarath, Guan, Yong, Chakraborty, Tusher, Jog, Suraj, Chandra, Ranveer, Zhang, Hongwei
Due to factors such as low population density and expansive geographical distances, network deployment falls behind in rural regions, leading to a broadband divide. Wireless spectrum serves as the blood and flesh of wireless communications. Shared wh
Externí odkaz:
http://arxiv.org/abs/2407.04561
Autor:
Haldar, Malay, Zhang, Hongwei, Bellare, Kedar, Chen, Sherry, Banerjee, Soumyadip, Wang, Xiaotang, Abdool, Mustafa, Gao, Huiji, Tapadia, Pavan, He, Liwei, Katariya, Sanjeev
As a two-sided marketplace, Airbnb brings together hosts who own listings for rent with prospective guests from around the globe. Results from a guest's search for listings are displayed primarily through two interfaces: (1) as a list of rectangular
Externí odkaz:
http://arxiv.org/abs/2407.00091
Autor:
Zhang, Tianyi, Boateng, Joshua Ofori, Islam, Taimoor UI, Ahmad, Arsalan, Zhang, Hongwei, Qiao, Daji
As wireless networks evolve towards open architectures like O-RAN, testing, and integration platforms are crucial to address challenges like interoperability. This paper describes ARA-O-RAN, a novel O-RAN testbed established through the NSF Platforms
Externí odkaz:
http://arxiv.org/abs/2407.10982
Restricting bus voltage deviation is crucial for normal operation of multi-bus DC microgrids, yet it has received insufficient attention due to the conflict between two main control objectives in DC microgrids, i.e., voltage regulation and current sh
Externí odkaz:
http://arxiv.org/abs/2405.13476
Autor:
Zhang, Tianyi, Zu, Guoying, Islam, Taimoor Ul, Gossling, Evan, Babu, Sarath, Qiao, Daji, Zhang, Hongwei
The study of wireless channel behavior has been an active research topic for many years. However, there exists a noticeable scarcity of studies focusing on wireless channel characteristics in rural areas. With the advancement of smart agriculture pra
Externí odkaz:
http://arxiv.org/abs/2404.17434
Backdoor attacks become a significant security concern for deep neural networks in recent years. An image classification model can be compromised if malicious backdoors are injected into it. This corruption will cause the model to function normally o
Externí odkaz:
http://arxiv.org/abs/2403.07463
In this work, we discover a phenomenon of community bias amplification in graph representation learning, which refers to the exacerbation of performance bias between different classes by graph representation learning. We conduct an in-depth theoretic
Externí odkaz:
http://arxiv.org/abs/2312.04883