Adaptive-order polynomial methods for power amplifier model estimation

Linearization techniques are required to reduce the undesirable effects of power amplifier nonlinearities in the wideband power-efficient radio communication systems. The polynomial model and its extension to the memory-polynomial are commonly used to model power amplifier nonlinearities. Determination of the appropriate order of the nonlinearity model is an actual issue to achieve effective methods for subsequent linearization techniques. Having in mind these facts, we compare two adaptive methods employing the determination of the appropriate order of the nonlinearity model. The first algorithm is the adaptive order recursive (ORLS) method, while the proposed solution is based on the recursive least squares method with the sequential updates. In the first stage the models were simulated in MATLAB. Depending on the mean square error estimate we decide on the appropriateness of the nonlinearity order increase. The parameters of the power amplifier models are based on the data experimentally obtained using the Universal Software Radio Peripheral (USRP) device with WBX front-end driven with an OFDM signal. After further performance evaluation, the order adaptation method will be used for efficient digital adaptive predistorter design.