Bouhafs, F, Raschella, A, MacKay, M, Hashem Eiza, M and Hartog, FD Optimizing Radio Access for Massive IoT in 6G Through Highly Dynamic Cooperative Software-Defined Sharing of Network Resources. Future Internet. ISSN 1999-5903 (Accepted)
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Optimizing Radio Access for Massive IoT in 6G Through Highly Dynamic Cooperative Software-Defined Sharing of Network Resources.pdf - Accepted Version Available under License Creative Commons Attribution. Download (1MB) | Preview |
Abstract
6G will provide an opportunity to build on and expand the vertical use cases currently supported by 5G. The Internet of Things (IoT) has been a major component for many of these use cases. Moving forward, 6G will need to connect more IoT devices than 5G, often densely deployed in urban areas. This makes simply accessing the wireless medium an issue as current generation networks are not designed to support many thousands of devices in each other’s vicinity, attempting to send/receive data simultaneously. In this paper, we present a model and a 6G system architecture for trading wireless network resources in massive IoT scenarios inspired by the concept of Sharing Economy, using the novel concept of spectrum programming. We simulated a truly massive IoT network and evaluated the scalability of the system when managed using our platform compared to standard fifth generation (5G) deployments. The experiments show how the proposed scheme can improve network resource allocation by up to 80% when compared to standard 5G allocation solutions. This is accompanied by similar improvements in interference and device energy consumption. Finally, we performed evaluations that demonstrate how the proposed platform can benefit all the stakeholders that decide to join the scheme.
Item Type: | Article |
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Uncontrolled Keywords: | 6G; Massive IoT; Blockchain; Sharing Economy; Spectrum Access; Spectrum Programming |
Divisions: | Computer Science and Mathematics |
Publisher: | MDPI |
SWORD Depositor: | A Symplectic |
Date Deposited: | 22 Nov 2024 15:37 |
Last Modified: | 22 Nov 2024 15:45 |
URI: | https://researchonline.ljmu.ac.uk/id/eprint/24844 |
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