Distributed Power Control Algorithm in Orthogonal Frequency Division Multiple Cognitive Radio Systems for Fading Channel

Authors

  • Lingling Chen College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin, Jilin, 130022, China Key Laboratory of Information Science, College of Communication Engineering, Jilin University, Changchun, Jilin, 130012, China
  • Feng Qi College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin, Jilin, 130022, China
  • Xingquan Gao College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin, Jilin, 130022, China
  • Huipeng Wang College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin, Jilin, 130022, China
  • Jiwen Liang College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin, Jilin, 130022, China

DOI:

https://doi.org/10.3991/ijoe.v14i03.8416

Keywords:

Cognitive radio networks, distributed power control algorithm, convex optimization theory

Abstract


In order to guarantee the quality of service (QoS) for primary users (PUs) and secondary users (SUs) in fading channel, a distributed power control algorithm is proposed based on convex optimization theory in underlay OFDM cognitive radio networks (CRNs). Our purpose obtains the maximum transmit data rate of each SU at all subcarriers under three constraints of the maximum allowable transmission power, the minimum signal to interference plus ratio (SINR) of each SU and the maximum allowable interference generated from SUs to PU at each subcarrier. Simulation results show that the performance of the proposed algorithms (m2) are superior to the geometric programming algorithm (m1) in fading channel environment.

Downloads

Published

2018-03-30

How to Cite

Chen, L., Qi, F., Gao, X., Wang, H., & Liang, J. (2018). Distributed Power Control Algorithm in Orthogonal Frequency Division Multiple Cognitive Radio Systems for Fading Channel. International Journal of Online and Biomedical Engineering (iJOE), 14(03), pp. 107–117. https://doi.org/10.3991/ijoe.v14i03.8416

Issue

Vol. 14 No. 03 (2018)

Section

Papers