Convex combination of quantized kernel least mean square algorithm

In this paper, we propose an new kernel adaptive filter, namely convex combination of quantized kernel least mean square algorithm (CC-QKLMS). By applying the convex combination idea to QKLMS, the CC-QKLMS takes the kernel sizes as the combined variables, which can achieve a fast convergence rate and a low steady-state mean-square error (MSE). In addition, since the quantization method is incorporated in CC-QKLMS, a linear growing network structure is naturally avoided. Simulation results on channel equalization validate the better performance of the CC-QKLMS in terms of the convergence rate and steady-state MSE.