The optimal RLS parameter tracking algorithm for a power amplifier feed-forward linearizer

Digital signal processing (DSP) techniques have been proposed in recent years to adaptively track the control parameters of a power amplifier (PA) feed-forward linearizer. In most of the propositions, gradient-based searching algorithms are applied to the parameter tracking. In this paper, we propose an optimal RLS (recursive least square) parameter tracking algorithm, which significantly accelerates the convergence speed and eliminates the gradient noise. There exists two problems for the RLS algorithm. First, the least square solution is not the optimal solution because of the nonlinearity of the PA. Second, the vector modulator (VM) which introduces the control parameters into the linearizer circuit may not be accurate enough to provide a precise power gain and phase shift calculated by the DSP. We solve both problems, respectively, by rearranging the circuit components and by constraining the VM characteristics. We also present simulation results to verify the performance improvement of the proposed algorithm