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Wibowo Hardjawana
Researcher at University of Sydney
Publications - 92
Citations - 1509
Wibowo Hardjawana is an academic researcher from University of Sydney. The author has contributed to research in topics: Wireless network & MIMO. The author has an hindex of 14, co-authored 80 publications receiving 1000 citations.
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Journal ArticleDOI
Ultra-Reliable Low Latency Cellular Networks: Use Cases, Challenges and Approaches
He Chen,Rana Abbas,Peng Cheng,Mahyar Shirvanimoghaddam,Wibowo Hardjawana,Wei Bao,Yonghui Li,Branka Vucetic +7 more
TL;DR: In this article, the authors investigate the various sources of end-to-end delay of current wireless networks by taking 4G LTE as an example and propose and evaluate several techniques to reduce the end to end latency from the perspectives of error control coding, signal processing, and radio resource management.
Journal ArticleDOI
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn From a Digital Twin
TL;DR: In this paper, the authors proposed a deep learning (DL) architecture, where a digital twin of the real network environment is used to train the DL algorithm off-line at a central server.
Journal ArticleDOI
Baseband Processing Units Virtualization for Cloud Radio Access Networks
TL;DR: A BBUs virtualization scheme that minimizes the power consumption with a linear computational complexity order is proposed, based on a heuristic simulated annealing (HSA) algorithm, which combines a bin packing algorithm with SA.
Journal ArticleDOI
Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks
Changyang She,Rui Dong,Zhouyou Gu,Zhanwei Hou,Yonghui Li,Wibowo Hardjawana,Chenyang Yang,Lingyang Song,Branka Vucetic +8 more
TL;DR: In this paper, a multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC is proposed, where deep transfer learning is adopted in the architecture to fine-tune the pre-trained DNNs in non-stationary networks.
Proceedings ArticleDOI
Inter-cell interference coordination through adaptive soft frequency reuse in LTE networks
TL;DR: An iterative algorithm that can adaptively vary the number of major subcarriers and adjust the transmit power for each cell according to wireless traffic loads is proposed and outperforms the existing Reuse 1, FFR and static SFR schemes in both system throughput and cell edge user performance.