Robust rate maximization for OFDM-based cognitive radio networks

In this paper, we propose a novel robust resource allocation method for an OFDM-based cognitive radio (CR) system. Considering noise plus interference uncertainty, the proposed method aims to maximize data rate of each CR user while the interference introduced to primary user and achievable rate of CR user remain within certain probability thresholds. With the assumption of uniform distribution of error, the robust rate maximization problem is transformed to a deterministic one solved by dual decomposition theory in a distributed way. To accelerate convergence speed, an iteration update strategy with forgetting factor is introduced instead of traditional subgradient update method. Simulation results demonstrate that the proposed algorithm has good convergence and robustness performance.