PAPR Reduction of OFDM Signals using Radial Basis Function Neural

In this paper we investigate a novel peak-to-average power ratio (PAPR) reduction method based on radial basis function network (RBFN). The RBFN can be regarded as a method of adaptive curve-fitting interpolator and is used to generate optimum mapping pattern to reduce the PAPR in this paper. Our simulation results show that our proposed method has significant performance advantages with low computational complexity compared to the conventional methods.