Combined kernel with parameter optimization for least squares support vector machine postdistorter in visible light communications

Abstract

The nonlinearity and memory effects induced by light-emitting diode distort the transmitted signal, which significantly limits the performance of visible light communication (VLC) system. With the characteristics of low computational complexity, fast convergence speed, and strong generalization ability, least squares support vector machine (LSSVM) is investigated in this paper and employed to alleviate the nonlinear effect. Since the performance of LSSVM is largely determined by the type of the adopted kernel functions, we utilize Gaussian and Laplace functions and propose a new combined kernel, which can increase the reproducing kernel Hilbert space and thus enhance the global expressive power compared with conventionally used Gaussian kernel. To further improve the performance, a hybrid parameter optimization algorithm is exploited to optimize the proposed kernel, based on genetic algorithm and particle swarm optimization. The property of the proposed postdistortion method is verified by numerical simulations, and better performance is obtained in VLC systems compared with the Gaussian-kernel LSSVM and conventional memory polynomial method.