Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Chu, Nam H."'
Autor:
Hassan, Md Arif, Jamshidi, Mohammad Behdad, Manh, Bui Duc, Chu, Nam H., Nguyen, Chi-Hieu, Hieu, Nguyen Quang, Nguyen, Cong T., Hoang, Dinh Thai, Nguyen, Diep N., Van Huynh, Nguyen, Alsheikh, Mohammad Abu, Dutkiewicz, Eryk
Web 3.0 represents the next stage of Internet evolution, aiming to empower users with increased autonomy, efficiency, quality, security, and privacy. This evolution can potentially democratize content access by utilizing the latest developments in en
Externí odkaz:
http://arxiv.org/abs/2401.10901
Autor:
Chu, Nam H., Van Huynh, Nguyen, Nguyen, Diep N., Hoang, Dinh Thai, Gong, Shimin, Shu, Tao, Dutkiewicz, Eryk, Phan, Khoa T.
This article introduces a novel lightweight framework using ambient backscattering communications to counter eavesdroppers. In particular, our framework divides an original message into two parts: (i) the active-transmit message transmitted by the tr
Externí odkaz:
http://arxiv.org/abs/2308.02242
Autor:
Chu, Nam H., Nguyen, Diep N., Hoang, Dinh Thai, Phan, Khoa T., Dutkiewicz, Eryk, Niyato, Dusit, Shu, Tao
This work proposes a novel framework to dynamically and effectively manage and allocate different types of resources for Metaverse applications, which are forecasted to demand massive resources of various types that have never been seen before. Speci
Externí odkaz:
http://arxiv.org/abs/2302.13445
Autor:
Nguyen, Hai M., Chu, Nam H., Nguyen, Diep N., Hoang, Dinh Thai, Nguyen, Van-Dinh, Ha, Minh Hoang, Dutkiewicz, Eryk, Krunz, Marwan
Federated Learning (FL) with quantization and deliberately added noise over wireless networks is a promising approach to preserve user differential privacy (DP) while reducing wireless resources. Specifically, an FL process can be fused with quantize
Externí odkaz:
http://arxiv.org/abs/2211.07166
Autor:
Chu, Nam H., Hoang, Dinh Thai, Nguyen, Diep N., Phan, Khoa T., Dutkiewicz, Eryk, Niyato, Dusit, Shu, Tao
Creating and maintaining the Metaverse requires enormous resources that have never been seen before, especially computing resources for intensive data processing to support the Extended Reality, enormous storage resources, and massive networking reso
Externí odkaz:
http://arxiv.org/abs/2205.11087
Autor:
Chu, Nam H., Nguyen, Diep N., Hoang, Dinh Thai, Pham, Quoc-Viet, Phan, Khoa T., Hwang, Won-Joo, Dutkiewicz, Eryk
Integrated Communications and Sensing (ICS) has recently emerged as an enabling technology for ubiquitous sensing and IoT applications. For ICS application to Autonomous Vehicles (AVs), optimizing the waveform structure is one of the most challenging
Externí odkaz:
http://arxiv.org/abs/2202.11508
Autor:
Van Huynh, Nguyen, Hieu, Nguyen Quang, Chu, Nam H., Nguyen, Diep N., Hoang, Dinh Thai, Dutkiewicz, Eryk
Unlike conventional anti-eavesdropping methods that always require additional energy or computing resources (e.g., in friendly jamming and cryptography-based solutions), this work proposes a novel anti-eavesdropping solution that comes with mostly no
Externí odkaz:
http://arxiv.org/abs/2201.06204
Unmanned aerial vehicle (UAV)-assisted data collection has been emerging as a prominent application due to its flexibility, mobility, and low operational cost. However, under the dynamic and uncertainty of IoT data collection and energy replenishment
Externí odkaz:
http://arxiv.org/abs/2106.10423
Autor:
Nguyen, Cong T., Van Huynh, Nguyen, Chu, Nam H., Saputra, Yuris Mulya, Hoang, Dinh Thai, Nguyen, Diep N., Pham, Quoc-Viet, Niyato, Dusit, Dutkiewicz, Eryk, Hwang, Won-Joo
With outstanding features, Machine Learning (ML) has been the backbone of numerous applications in wireless networks. However, the conventional ML approaches have been facing many challenges in practical implementation, such as the lack of labeled da
Externí odkaz:
http://arxiv.org/abs/2102.07572
Unmanned Aerial Vehicles (UAVs) have been emerging as an effective solution for IoT data collection networks thanks to their outstanding flexibility, mobility, and low operation costs. However, due to the limited energy and uncertainty from the data
Externí odkaz:
http://arxiv.org/abs/2011.06134