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

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

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

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.</p