Throughput Optimization in Ambient Backscatter-Based Energy Constraint Cognitive Radio Networks

Author(s)
Shah, Syed Tariq
Fazal, Maheen
Shawky, Mahmoud A
Sohaib, Rana M
Hasan, Syed Faraz
Imran, M Ali
Abbasi, Qammer H
Publication Date
2024
Abstract
<p>Efficient utilisation of scarce resources, mainly radio and power, are the key research issues in the future Internet of Things (IoT) networks. This paper proposes an ambient energy harvesting and backscatter-enabled, energy-constrained cognitive IoT network. In our proposed scheme, the nodes in the secondary network efficiently utilise the primary network. More specifically, depending on the communication states (i.e. between busy or idle) of the primary network, the secondary nodes chose to operate either in energy harvesting mode (EHM), backscattering mode (BSM), or radio-frequency transmission mode (RFM). Furthermore, to maximise the sum-throughput of the secondary network, an optimisation problem is formulated and solved. The simulation results show that the proposed scheme outperforms the existing scheme regarding network sum-throughput.</p>
Citation
Proceedings of the International Conference on Communications Workshops, ICC Workshops 2024, p. 2029-2033
ISBN
9798350304053
9798350304060
ISSN
2694-2941
2164-7038
Link
Publisher
Institute of Electrical and Electronics Engineers
Title
Throughput Optimization in Ambient Backscatter-Based Energy Constraint Cognitive Radio Networks
Type of document
Conference Publication
Entity Type
Publication

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