Zobrazeno 1 - 10
of 316
pro vyhledávání: '"Lambotharan, Sangarapillai"'
Autor:
Omid, Yasaman, Aristodemou, Marios, Lambotharan, Sangarapillai, Derakhshani, Mahsa, Hanzo, Lajos
The integration of low earth orbit (LEO) satellites with terrestrial communication networks holds the promise of seamless global connectivity. The efficiency of this connection, however, depends on the availability of reliable channel state informati
Externí odkaz:
http://arxiv.org/abs/2410.21489
Federated Learning (FL) is a decentralized approach for collaborative model training on edge devices. This distributed method of model training offers advantages in privacy, security, regulatory compliance, and cost-efficiency. Our emphasis in this r
Externí odkaz:
http://arxiv.org/abs/2410.17792
A Hybrid Training-time and Run-time Defense Against Adversarial Attacks in Modulation Classification
Autor:
Zhang, Lu, Lambotharan, Sangarapillai, Zheng, Gan, Liao, Guisheng, Demontis, Ambra, Roli, Fabio
Motivated by the superior performance of deep learning in many applications including computer vision and natural language processing, several recent studies have focused on applying deep neural network for devising future generations of wireless net
Externí odkaz:
http://arxiv.org/abs/2407.06807
Deep learning algorithms have been shown to be powerful in many communication network design problems, including that in automatic modulation classification. However, they are vulnerable to carefully crafted attacks called adversarial examples. Hence
Externí odkaz:
http://arxiv.org/abs/2407.06796
In this study, we explore the integration of satellites with ground-based communication networks. Specifically, we analyze downlink data transmission from a constellation of satellites to terrestrial users and address the issue of delayed channel sta
Externí odkaz:
http://arxiv.org/abs/2406.06392
Autor:
Rahulamathavan, Yogachandran, Herath, Charuka, Liu, Xiaolan, Lambotharan, Sangarapillai, Maple, Carsten
The federated learning (FL) technique was developed to mitigate data privacy issues in the traditional machine learning paradigm. While FL ensures that a user's data always remain with the user, the gradients are shared with the centralized server to
Externí odkaz:
http://arxiv.org/abs/2306.05112
Autor:
Liu, Xiaolan, Yu, Jiadong, Liu, Yuanwei, Gao, Yue, Mahmoodi, Toktam, Lambotharan, Sangarapillai, Tsang, Danny H. K.
The cloud-based solutions are becoming inefficient due to considerably large time delays, high power consumption, security and privacy concerns caused by billions of connected wireless devices and typically zillions bytes of data they produce at the
Externí odkaz:
http://arxiv.org/abs/2208.00545
Autor:
Hoang, Tiep M., Duong, Trung Q., Tuan, Hoang Duong, Lambotharan, Sangarapillai, Garcia-Palacios, Emi, Nguyen, Long D.
This paper presents a framework for converting wireless signals into structured datasets, which can be fed into machine learning algorithms for the detection of active eavesdropping attacks at the physical layer. More specifically, a wireless communi
Externí odkaz:
http://arxiv.org/abs/2003.01048
Autor:
Zhang, Juping, Xia, Wenchao, You, Minglei, Zheng, Gan, Lambotharan, Sangarapillai, Wong, Kai-Kit
This paper studies fast downlink beamforming algorithms using deep learning in multiuser multiple-input-single-output systems where each transmit antenna at the base station has its own power constraint. We focus on the signal-to-interference-plus-no
Externí odkaz:
http://arxiv.org/abs/2002.12589
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