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Jaiswal, A, Kumar, S, Kaiwartya, O 
ORCID: https://orcid.org/0000-0001-9669-8244, Kashyap, PK, Kanjo, E ORCID: https://orcid.org/0000-0002-1720-0661, Kumar, N and Song, H,
2021.
Quantum learning enabled green communication for next generation wireless systems.
IEEE Transactions on Green Communications and Networking.
ISSN 2473-2400
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Abstract
Next generation wireless systems have witnessed significant RandD attention from academia and industries to enable wide range of applications for connected environment around us. The technical design of next generation wireless systems in terms of relay and transmit power control is very critical due to the ever-reducing size of these sensor enabled systems. The growing demand of computation capability in these systems for smart decision making further diversified the significance of relay and transmit power control. Towards harnessing the benefits of Quantum Reinforcement Leaning (QRL) in the design of next generation wireless systems, this paper presents a framework for joint optimal Relay and transmit Power Selection (QRL-RPS). In QRL-RPS, each sensor node learns using its present and past local state’s knowledge to take optimal decision in relay and transmit power selection. Firstly, RPS problem is modelled as a Markov Decision Process (MDP), and then QRL optimization aspect of the MDP problem is formulated focusing on joint optimization of energy consumption and throughput as network utility. Secondly, a QRL-RPS algorithm is developed based on Grover’s iteration to solve the MDP problem. The comparative performance evaluation attests the benefit of the proposed framework as compared to the state-of-the-art techniques.
| Item Type: | Journal article |
|---|---|
| Publication Title: | IEEE Transactions on Green Communications and Networking |
| Creators: | Jaiswal, A., Kumar, S., Kaiwartya, O., Kashyap, P.K., Kanjo, E., Kumar, N. and Song, H. |
| Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
| Date: | 22 March 2021 |
| ISSN: | 2473-2400 |
| Identifiers: | Number Type 10.1109/tgcn.2021.3067918 DOI 1447817 Other |
| Divisions: | Schools > School of Science and Technology |
| Record created by: | Linda Sullivan |
| Date Added: | 28 Jun 2021 14:09 |
| Last Modified: | 28 Jun 2021 14:09 |
| URI: | https://irep.ntu.ac.uk/id/eprint/43269 |
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