Implementation of "Iterative Learning Control for RF Power Amplifier Linearization", both in MATLAB and Python
For MATLAB file: This code is the implementation of a simplified ILC algorithm. There are two parts: 1- Model the PA by the function "Find_out_PA(in_PA)": y = \alpha_0 + \alpha_1 x + \alpha_2 x |x|^2 + \alpha_3 x |x|^4 + \alpha_4 x |x|^6 The \alpha_i are estimated using the measured data from "weblab".
2- Implementation of ILC based on Section III.D of "Iterative Learning Control for RF Power Amplifier Linearization" Instead of doing measurements to implement ILC, we assumed the estimated model is perfect!
Notes about weblab: 1- "weblab" is used to access output data from a real power amplifier. Random data input is fed into the PA, and the output is read from "weblab". Then, least square estimation is done to find the \alpha_i. 2- To generate random constellation we used "randconst" function. You should either write your own code to generate random constellaion or ask "RFWebLab@dpdcompetition.com" to access the code. For more info about "weblab" please see http://www.dpdcompetition.com/