Abstract
It is envisioned that 6G, unlike its predecessor 5G, will depart from connected machines and connected people to connected intelligence. The main goal of 6G networks is to support massive connectivity for time-sensitive and computation-sensitive services in mission-critical applications. The creation of real-time optimisation (RTO) enabled by the fast growing data analytic and machine learning will seize the opportunities for 6G wireless networks to support such immersive services such as virtual reality (VR), augmented reality (AR), mixed reality (MR), and tactile Internet. Recently, with the rapid development of quantum computers, quantum-inspired optimisation and machine learning algorithms have been exploited as efficient solutions for future wireless networks. In this article, we provide a comprehensive view on the new concept of quantum-inspired RTO and its application to the optimal resource allocation for 6G wireless networks. Our main contributions are to introduce some of the initial research results and introduce the potentiality of quantum-inspired RTO on some 6G emerging technologies. Not only do we review the fundamental principles; we also explore the challenges and opportunities of this exciting research direction.
Original language | English |
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Pages (from-to) | 1347-1359 |
Number of pages | 13 |
Journal | IEEE Open Journal of the Communications Society |
Volume | 3 |
DOIs | |
Publication status | Published - 03 Aug 2022 |
Keywords
- 6G mobile communication
- 6G networks
- Computers
- Optimization
- Quantum communications
- Quantum computing
- Quantum mechanics
- Qubit
- real-time optimisation
- Real-time systems
- resource allocation
ASJC Scopus subject areas
- Computer Networks and Communications
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Dive into the research topics of 'Quantum-inspired real-time optimization for 6G networks: opportunities, challenges, and the road ahead'. Together they form a unique fingerprint.Student theses
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Ultra-reliable and near zero-latency communications for industrial automation
Huynh, D. V. (Author), Duong, Q. (Supervisor) & Mai, T. S. (Supervisor), Dec 2023Student thesis: Doctoral Thesis › Doctor of Philosophy
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