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Energy Efficient Cognitive Radio Network Using High Altitude Platform Station

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Advances in Information and Communication Networks (FICC 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 886))

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Abstract

To meet the demand of future communication, it is utmost important to efficiently utilize various communication resources. Cognitive radio network is one of the most promising area for future communication, which aims to increase the frequency utilization. With the cell size growing smaller with each generation, the number of base stations and the time to develop the required infrastructure will increase exponentially. As a “last mile” solution, this paper considers a high altitude platform station (HAPS) to implement the cognitive radio network where the optimization problem is formulated to maximize the energy efficiency (EE) of the overall network. Two primary objectives are addressed: increase the EE of the network and decrease the interference to the primary users. The non-convex optimization problem is transformed to semi-definite problem. The results obtained are promising for HAPS as a future radio platform.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Oakland University, Rochester, MI, 48309, USA

    Shital Joshi

  2. Department of Computer Science, University of Tabuk, Tabuk, Kingdom of Saudi Arabia

    Umar Albalawi

Authors
  1. Shital Joshi
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  2. Umar Albalawi
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Corresponding author

Correspondence to Shital Joshi .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, London, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, UK

    Rahul Bhatia

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Joshi, S., Albalawi, U. (2019). Energy Efficient Cognitive Radio Network Using High Altitude Platform Station. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Advances in Information and Communication Networks. FICC 2018. Advances in Intelligent Systems and Computing, vol 886. Springer, Cham. https://doi.org/10.1007/978-3-030-03402-3_14

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