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https://dspace.iiti.ac.in/handle/123456789/5097
Title: | Performance Analysis of an EH-CRN under Alpha-Mu Fading scenario |
Authors: | Kumar, Deepak |
Keywords: | Cognitive radio;Energy harvesting;Signal processing;Co-operative spectrum sensing;Cognitive radio network;Fading distribution;General fading distributions;Mathematical expressions;Outage performance;Outage probability;Performance analysis;Fading (radio) |
Issue Date: | 2020 |
Publisher: | Institute of Electrical and Electronics Engineers Inc. |
Citation: | Talukdar, B., Kumar, D., Kundu, A., & Arif, W. (2020). Performance analysis of an EH-CRN under alpha-mu fading scenario. Paper presented at the International Conference on Advanced Communication Technologies and Signal Processing, ACTS 2020, doi:10.1109/ACTS49415.2020.9350426 |
Abstract: | In this work, the mathematical modelling of a prediction based cooperative spectrum sensing (CSS) technique incorporated in an energy harvesting cognitive radio network is done. The performance of the system under a general fading distribution i.e., the alpha-mu fading model is investigated. The performance is analysed with respect to the energy harvested, energy penalty and the outage probability (OP). The effect of prediction error, energy splitting factor and the fading severity factor on the harvested energy, energy penalty and the OP is shown. The closed-form mathematical expressions for the outage probability in an alpha-mu fading environment is derived. The effect of various fading scenarios on the outage performance of the CR receiver is demonstrated. The expression obtained is general and holds true for several important fading distributions like the Exponential, Rayleigh, Nakagami-m and Gamma distributions. The impact of channel gain on the harvested energy over distinct fading scenarios is investigated and it is found that the system performance is the least affected in a Nakagami-m fading environment. © 2020 IEEE. |
URI: | https://doi.org/10.1109/ACTS49415.2020.9350426 https://dspace.iiti.ac.in/handle/123456789/5097 |
ISBN: | 9781728170978 |
Type of Material: | Conference Paper |
Appears in Collections: | Department of Electrical Engineering |
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