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Electronics and Telecommunications Research Institute (한국전자통신연구원)
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Efficient power allocation algorithm in downlink cognitive radio networks

  • Abdulghafoor, Omar (Electronic & Telecommunications Department, College of Engineering, The American University of Kurdistan) ;
  • Shaat, Musbah (CTTC) ;
  • Shayea, Ibraheem (Faculty of Electrical and Electronics Engineering, Istanbul Technical University) ;
  • Mahmood, Farhad E. (Electrical Engineering Department, College of Engineering, University of Mosul) ;
  • Nordin, Rosdiadee (EES Department, Faculty of Engineering and Built Environment, The National University of Malaysia) ;
  • Lwas, Ali Khadim (R&D, Ministry of Industry and Minerals)
  • Received : 2021.03.11
  • Accepted : 2021.09.13
  • Published : 2022.06.10
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Abstract

In cognitive radio networks (CRNs), the computational complexity of resource allocation algorithms is a significant problem that must be addressed. However, the high computational complexity of the optimal solution for tackling resource allocation in CRNs makes it inappropriate for use in practical applications. Therefore, this study proposes a power-based pricing algorithm (PPA) primarily to reduce the computational complexity in downlink CRN scenarios while restricting the interference to primary users to permissible levels. A two-stage approach reduces the computational complexity of the proposed mathematical model. Stage 1 assigns subcarriers to the CRN's users, while the utility function in Stage 2 incorporates a pricing method to provide a power algorithm with enhanced reliability. The PPA's performance is simulated and tested for orthogonal frequency-division multiplexing-based CRNs. The results confirm that the proposed algorithm's performance is close to that of the optimal algorithm, albeit with lower computational complexity of O(M log(M)).

Keywords

Acknowledgement

The authors would like to thank The American University of Kurdistan for their kind support. Musbah Shaat's work was supported by Ministerio de Ciencia e Innovacion (MICINN) under project IRENE PID2020-115323RB-C31 (AEI/FEDER, UE).

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