Automatic Test Stimulus Generation for Diagnosis of RF Transceivers Using Model Parameter Estimation

In this brief, an optimized test stimulus generation technique is proposed for model parameter computation-based diagnosis and testing, which can provide a very compact deterministic test signal and results in significant reduction in test time. The proposed test stimulus generation algorithm maximizes the accuracy with which a nonlinear solver can determine RF transceiver model parameters from raw downconverted test response data. The simulation results show that using optimized test signals, a comprehensive range of model parameters can be computed accurately using a single data acquisition. Data from experiments performed on a hardware prototype are shown to validate the proposed methodology.