Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future
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NAWAZ, Syed, SHARMA, Shree, WYNE, Shurjeel, PATWARY, Mohammad and ASADUZZAMAN, Md (2019) Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future. IEEE Access. ISSN 2169-3536
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Accepted_Manuscript_Nawaz_et_al_2019_IEEE_Access.pdf - AUTHOR'S ACCEPTED Version (default) Available under License All Rights Reserved. Download (6MB) | Preview |
Abstract or description
The upcoming 5th Generation (5G) of wireless networks is expected to lay a foundation of intelligent networks with the provision of some isolated Artificial Intelligence (AI) operations. However, fully-intelligent network orchestration and management for providing innovative services will only be realized in Beyond 5G (B5G) networks. To this end, we envisage that the 6th Generation (6G) of wireless networks will be driven by on-demand self-reconfiguration to ensure a many-fold increase in the network performance and service types. The increasingly stringent performance requirements of emerging networks may finally trigger the deployment of some interesting new technologies such as large intelligent surfaces, electromagnetic-orbital angular momentum, visible light communications, and cell-free communications – to name a few. Our vision for 6G is – a massively connected complex network capable of rapidly responding to the users’ service calls through real-time learning of the network state as described by the network-edge (e.g., base-station locations, cache contents, etc.), air interface (e.g., radio spectrum, propagation channel, etc.), and the user-side (e.g., battery-life, locations, etc.). The multi-state, multi-dimensional nature of the network state, requiring real-time knowledge, can be viewed as a quantum uncertainty problem. In this regard, the emerging paradigms of Machine Learning (ML), Quantum Computing (QC), and Quantum ML (QML) and their synergies with communication networks can be considered as core 6G enablers. Considering these potentials, starting with the 5G target services and enabling technologies, we provide a comprehensive review of the related state-of-the-art in the domains of ML (including deep learning), QC and QML, and identify their potential benefits, issues and use cases for their applications in the B5G networks. Subsequently, we propose a novel QC-assisted and QML-based framework for 6G communication networks while articulating its challenges and potential enabling technologies at the network-infrastructure, network-edge, air interface, and user-end. Finally, some promising future research directions for the quantum- and QML-assisted B5G networks are identified and discussed.
| Item Type: | Article |
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| Additional Information: | (c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. |
| Uncontrolled Keywords: | 6G; B5G; Machine Learning; Quantum Communications; Quantum Machine Learning |
| Faculty: | School of Creative Arts and Engineering > Engineering |
| Depositing User: | Md ASADUZZAMAN |
| Date Deposited: | 12 Apr 2019 13:07 |
| Last Modified: | 24 Feb 2023 13:55 |
| Related URLs: | |
| URI: | https://eprints.staffs.ac.uk/id/eprint/5526 |
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