Performance of NOMA-Enabled Cognitive Satellite-Terrestrial Networks With Non-Ideal System Limitations
Rabie, Khaled M.
Permanent link to this recordhttp://hdl.handle.net/10754/668025
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AbstractSatellite-terrestrial networks (STNs) have received significant attention from research and industry due to their capability of providing a stable connection to rural and distant areas, where the allocation of terrestrial infrastructures is uneconomical or difficult. Moreover, the STNs are considered as a promising enabler of fifth-generation communication networks. However, expected massive connectivity in future communication networks will face issues associated with spectrum scarcity. In this regard, the integration of cognitive radio and non-orthogonal multiple access (NOMA) techniques into STNs is considered as a promising remedy. Thereafter, in this article, we investigate NOMA-assisted cognitive STN under practical system conditions, such as transceiver hardware impairments, channel state information mismatch, imperfect successive interference cancellation, and interference noises. Generalized coverage probability formulas for NOMA users in both primary and secondary networks are derived considering the impact of interference temperature constraint and its correctness is verified through Monte Carlo simulation. Furthermore, to achieve performance fairness among the users, power allocation factors based on coverage fairness for primary and secondary NOMA users are provided. Moreover, the numerical results demonstrate superior performance compared to the ones obtained from an orthogonal multiple access scheme and examine the imperfection’s impact on the system performance in terms of coverage and throughput.
CitationAkhmetkaziyev, Y., Nauryzbayev, G., Arzykulov, S., Eltawil, A. M., Rabie, K. M., & Li, X. (2021). Performance of NOMA-Enabled Cognitive Satellite-Terrestrial Networks With Non-Ideal System Limitations. IEEE Access, 9, 35932–35946. doi:10.1109/access.2021.3061278
SponsorsThis work was supported in part by the Nazarbayev University Social Policy Grant and in part by the Nazarbayev University Faculty Development Competitive Research Program under Grant 240919FD3935.
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