no code implementations • 21 Feb 2024 • Siyu Wang, Bo Yang, Zhiwen Yu, Xuelin Cao, Yan Zhang, Chau Yuen
In this paper, we investigate a multi-user offloading problem in the overlapping domain of a multi-server mobile edge computing system.
1 code implementation • 2 Sep 2023 • Ruihuai Liang, Bo Yang, Zhiwen Yu, Xuelin Cao, Derrick Wing Kwan Ng, Chau Yuen
To improve the MEC performance, it is required to design an optimal offloading strategy that includes offloading decision (i. e., whether offloading or not) and computational resource allocation of MEC.
no code implementations • 9 Aug 2022 • Bo Yang, Xuelin Cao, Jindan Xu, Chongwen Huang, George C. Alexandropoulos, Linglong Dai, M'erouane Debbah, H. Vincent Poor, Chau Yuen
The envisioned sixth-generation (6G) of wireless networks will involve an intelligent integration of communications and computing, thereby meeting the urgent demands of diverse applications.
no code implementations • 3 Sep 2021 • Bo Yang, Xuelin Cao, Chongwen Huang, Yong Liang Guan, Chau Yuen, Marco Di Renzo, Dusit Niyato, Merouane Debbah, Lajos Hanzo
In the sixth-generation (6G) era, emerging large-scale computing based applications (for example processing enormous amounts of images in real-time in autonomous driving) tend to lead to excessive energy consumption for the end users, whose devices are usually energy-constrained.
no code implementations • 19 Apr 2021 • Bo Yang, Omobayode Fagbohungbe, Xuelin Cao, Chau Yuen, Lijun Qian, Dusit Niyato, Yan Zhang
In this paper, we propose a transfer learning (TL)-enabled edge-CNN framework for 5G industrial edge networks with privacy-preserving characteristic.
no code implementations • 2 Mar 2021 • Bo Yang, Xuelin Cao, Chongwen Huang, Chau Yuen, Lijun Qian, Marco Di Renzo
Reconfigurable intelligent surface (RIS) has become a promising technology for enhancing the reliability of wireless communications, which is capable of reflecting the desired signals through appropriate phase shifts.
no code implementations • 18 Aug 2020 • Bo Yang, Xuelin Cao, Chau Yuen, Lijun Qian
This motivates us to consider offloading this type of deep learning (DL) tasks to a mobile edge computing (MEC) server due to limited computational resource and energy budget of the UAV, and further improve the inference accuracy.
no code implementations • 29 Jun 2020 • Bo Yang, Xuelin Cao, Joshua Bassey, Xiangfang Li, Timothy Kroecker, Lijun Qian
Multi-access edge computing (MEC) has already shown the potential in enabling mobile devices to bear the computation-intensive applications by offloading some tasks to a nearby access point (AP) integrated with a MEC server (MES).
no code implementations • 27 Jun 2020 • Bo Yang, Xuelin Cao, Xiangfang Li, Chau Yuen, Lijun Qian
This letter proposes an edge learning-based offloading framework for autonomous driving, where the deep learning tasks can be offloaded to the edge server to improve the inference accuracy while meeting the latency constraint.