Probabilistical Robust Power Control for Cognitive Radio Networks under Interference Uncertainty Conditions

Authors

  • Lingling Chen School of Computer Science, Jilin University, Changchun, Jilin, 130012, China School of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin, Jilin, 130022, China
  • Zhiyi Fang School of Computer Science, Jilin University, Changchun, Jilin, 130012, China
  • Xiaohui Zhao Key Laboratory of Information Science, School of Communication Engineering, Jilin University, Changchun, Jilin, 130012, China

DOI:

https://doi.org/10.3991/ijoe.v14i07.8967

Keywords:

Cognitive radio networks, robust power control, probability constraints, orthogonal frequency-division multiplexing (OFDM)

Abstract


The focus of this paper is to find a robust power control strategy with uncertain noise plus interference (NI) in cognitive radio networks (CRNs)in an under orthogonal frequency-division multiplexing (OFDM) framework. The optimization problem is formulated to maximize the data rate of secondary users (SUs) under the constraints of transmission power of each SU, probabilistic the transmit rate of each SU at each subcarrier and robust interference constraint of primary user. In consideration of the feedback errors from the quantization due to uniform distribution, the probabilistic constraint is transformed into closed forms. By using Lagrange relaxation of the coupling constraints method and subgradient iterative algorithm in a distributed way, we solve this dual problem. Numerical simulation results show that our proposed algorithm is superior to the robust power control scheme based on interference gain worst case approach and non-robust algorithm without quantization error in perfect channels in the improvement of data rate of each SU, convergence speed and computational complexity.

Downloads

Published

2018-07-27

How to Cite

Chen, L., Fang, Z., & Zhao, X. (2018). Probabilistical Robust Power Control for Cognitive Radio Networks under Interference Uncertainty Conditions. International Journal of Online and Biomedical Engineering (iJOE), 14(07), pp. 90–107. https://doi.org/10.3991/ijoe.v14i07.8967

Issue

Section

Papers