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Spectrally Efficient Cognitive Relaying for IoT Networks

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Abstract

Current research emphasis on IoT (Internet-of-Things) to enable physical objects to connect, co-ordinate among themselves in a ubiquitous manner. Specifically, for this type of applications, the quality of wireless communication is indeed considered vital to enhance the reliability of battery operated IoT devices. Moreover, these devices operate on unlicensed bands creating heavy congestion on spectrum. Interestingly operating these devices on Cognitive Radio band avoids the spectrum shortage problem by exploiting the dynamic spectrum access capabilities. Cognitive relaying for IoT applications ensures reliable communication through interference mitigation among co-existing IoT devices. In this paper, we therefore propose spectrally efficient two-path decode and amplify relaying technique in three time slots through network-coded co-operation of Primary user and Secondary User. To address the interference issues, optimum power allocation is formulated as a convex optimization problem solved through a modeling system named CVX. Thereby we achieve co-operative diversity without the need for additional antennas for IoT networks. Performance evaluation compares the Bit Error Rate for different relaying schemes with and without optimum power allocation. Our simulation results ensure that the probability of error is minimized and rate is maximized by our proposed algorithm.

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The authors did not receive support from any organization for the submitted work.

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All authors contributed to the study conception and design. Concept preparation and analysis were performed by [Bala Vishnu J], [Lavanya S] and [Bhagyaveni M.A]. The first draft of the manuscript was written by [Lavanya S] and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to J. Bala Vishnu.

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Vishnu, J.B., Lavanya, S. & Bhagyaveni, M.A. Spectrally Efficient Cognitive Relaying for IoT Networks. Wireless Pers Commun 122, 3433–3443 (2022). https://doi.org/10.1007/s11277-021-09093-9

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