Rui A Ma

20220198253, Jun 23, 2022

Jan 11, 2021

17/145444

- Cambridge MA, US
Rui Ma - Carlisle MA, US
Chouaib KANTANA - Paris, FR
Richard C Waters - Lincoln MA, US

Mitsubishi Electric Research Laboratories, Inc. - Cambridge MA

G06N 3/063
G06N 3/08
H03F 3/20
H03F 3/26
G06K 9/62

An auto-tuning controller for improving a performance of a power amplifier system is provided. The controller includes an interface including input terminals and output terminals, the interface being configured to acquire input signal conditions of power amplifiers (PAs), a training circuit including a processor and a memory running and storing a Digital Doherty amplifier (DDA) controller (module), a DPD controller (module) and a DDA-DPD neural network (NN). The training circuit is configured to perform sampling the input signal conditions, and selecting a DPD model from a set of polynomial models for the DPD controller and a set of DDA optimization variables for the DDA controller, using optimized DPD model and DDA coefficients, wherein the optimized DPD model and DDA coefficients are provided by performing an offline optimization for the DPD model and DDA coefficients based on a predetermined optimization method, collecting the optimized DPD coefficients and optimized DDA optimization variables, generating online-DDA optimal coefficients and DPD optimal coefficients using a trained DDA-DPD NN, and updating the generated optimal DDA and DPD coefficients via the output terminals of the interface.

20220200539, Jun 23, 2022

Jan 11, 2021

17/145441

- Cambridge MA, US
Rui Ma - Carlisle MA, US
Chouaib KANTANA - Paris, FR

Mitsubishi Electric Research Laboratories, Inc. - Cambridge MA

H03F 1/32
H03F 3/21
H03F 1/02

An autotuning controller is provided for improving power efficiency and linearity of digital power amplifiers (DPAs). The controller includes an interface including input and output terminals connected to the DPAs, the interface being configured to acquire input signals and output signals, a digital pre-distortion (DPD)-DPA adaptive controller including a processor and a memory running and storing a DPD algorithm, an efficiency enhancement method and a learning cost function. The DPD adaptive controller is configured to perform steps of computing DPD coefficients to define a learning cost function based on a DPD model by use of a data-driven optimization method, wherein the leaning cost function includes both variables of a DDA performance and a DPD performance, updating the learning cost function based on the DPD performance, optimizing the updated learning cost function by solving the updated learning cost function with respect to the variables of the DDA performance, and providing optimal parameters for DPA and DPD via the interface.

20160099661, Apr 7, 2016

Oct 1, 2014

14/503751

- Cambridge MA, US
Koon Hoo Teo - Lexington MA, US
Rui Ma - Somerville MA, US

H02M 7/537
H02M 1/12

A method generates space vector modulation signals for a multi-level power inverter using space vector pulse width modulation (SVPWM). A reference voltage and a triangle region for the reference voltage are determined. Vertices for a space vector that is closest to the reference voltage is outputted. Then, the vertices are adjusted so that the space vector is in a valid region of the triangle region. Lastly, the space vector modulation signals a, b, and c corresponding to the space vector in the valid region are outputed.