An auto-tuning controller (351, 450) for improving a performance of a Digital Doherty Power-Amplifier, DDPA, system, comprising: an interface including input terminals and output terminals, the interface being configured to acquire input signal conditions of power amplifiers, PAs, included in a multi-input PA module of the DDPA system, wherein the input signal conditions include at least frequencies, signal power levels, and signal modulation formats of the PAs; a training circuit including a processor and a memory for running and storing a Digital Doherty amplifier, DDA, controller, a Digital Pre-Distortion, DPD, controller and a DDA-DPD neural network, NN, wherein the training circuit is configured to perform the steps of: sampling the input signal conditions; selecting (41011, 410112) a DPD model for the DPD controller; selecting (41011, 410113) a set of DDA optimization variables for the DDA controller, wherein the DDA optimization variables include an input power ratio for the PAs, a phase difference between the PAs, an attenuation difference between the PAs, a main bias voltage of the PAs, and a peak bias voltage of the PAs; performing an offline optimization for the selected DPD model and the selected set of DDA optimization variables based on the sampled input signal conditions and a predetermined optimization method; collecting optimized DPD coefficients and optimized DDA optimization variables provided by performing the offline optimization; for any new input signal conditions, generating online optimal DDA optimization variables and optimal DPD coefficients using a trained DDA-DPD NN, which has been offline-trained based on the sampled input signal conditions, the optimized DPD coefficients and the optimized DDA optimization variables; and transmitting the online-generated optimal DDA optimization variables and optimal DPD coefficients via the output terminals of the interface. an interface including input terminals and output terminals, the interface being configured to acquire input signal conditions of power amplifiers, PAs, included in a multi-input PA module of the DDPA system, wherein the input signal conditions include at least frequencies, signal power levels, and signal modulation formats of the PAs; a training circuit including a processor and a memory for running and storing a Digital Doherty amplifier, DDA, controller, a Digital Pre-Distortion, DPD, controller and a DDA-DPD neural network, NN, wherein the training circuit is configured to perform the steps of: sampling the input signal conditions; selecting (41011, 410112) a DPD model for the DPD controller; selecting (41011, 410113) a set of DDA optimization variables for the DDA controller, wherein the DDA optimization variables include an input power ratio for the PAs, a phase difference between the PAs, an attenuation difference between the PAs, a main bias voltage of the PAs, and a peak bias voltage of the PAs; performing an offline optimization for the selected DPD model and the selected set of DDA optimization variables based on the sampled input signal conditions and a predetermined optimization method; collecting optimized DPD coefficients and optimized DDA optimization variables provided by performing the offline optimization; for any new input signal conditions, generating online optimal DDA optimization variables and optimal DPD coefficients using a trained DDA-DPD NN, which has been offline-trained based on the sampled input signal conditions, the optimized DPD coefficients and the optimized DDA optimization variables; and transmitting the online-generated optimal DDA optimization variables and optimal DPD coefficients via the output terminals of the interface. sampling the input signal conditions; selecting (41011, 410112) a DPD model for the DPD controller; selecting (41011, 410113) a set of DDA optimization variables for the DDA controller, wherein the DDA optimization variables include an input power ratio for the PAs, a phase difference between the PAs, an attenuation difference between the PAs, a main bias voltage of the PAs, and a peak bias voltage of the PAs; performing an offline optimization for the selected DPD model and the selected set of DDA optimization variables based on the sampled input signal conditions and a predetermined optimization method; collecting optimized DPD coefficients and optimized DDA optimization variables provided by performing the offline optimization; for any new input signal conditions, generating online optimal DDA optimization variables and optimal DPD coefficients using a trained DDA-DPD NN, which has been offline-trained based on the sampled input signal conditions, the optimized DPD coefficients and the optimized DDA optimization variables; and transmitting the online-generated optimal DDA optimization variables and optimal DPD coefficients via the output terminals of the interface.