Optimized Multitone Test Stimulus Driven Diagnosis of RF Transceivers Using Model Parameter Estimation

Test time and test complexity reduction has become a critical challenge in modern RF testing. Prior “alternative” test methods have achieved fast testing at the cost of using supervised learning algorithms that require “training”. In contrast, behavioral model parameter estimation based test methods require the use of accurate models but no “training” is necessary, reducing test deployment costs. In this work, a new test generation approach is proposed that allows behavioral model parameter estimation to be performed from a single optimized OFDM data frame. A genetic multi-tone test stimulus optimization algorithm is developed to maximize the accuracy with which a nonlinear solver can determine RF transceiver model parameters from raw downconverted test response data. The transceiver model proposed is the most comprehensive to date and includes AM-PM distortion and 5th order nonlinearity effects. Simulation results show that using the optimized multitone test stimulus, all the model parameters can be computed accurately using a single data acquisition (4X-5X faster than prior parameter estimation techniques and comparable to alternative test times). Data from an experiment performed on a hardware prototype validates the proposed concept.