Digital Predistortion | Jose Alejandro Galaviz-Aguilar

Subband Digital Predistortion for 6G Millimeter-Wave Systems

Mon, 01 Dec 2025 00:00:00 +0000

Overview

As an RF R&D Engineer at Télécom Paris (Palaiseau, France), I contributed to the Docte6G national research project in collaboration with NXP Semiconductors, targeting next-generation 6G wireless systems operating at millimeter-wave (mmW) frequencies.

Key Contributions

Developed a subband digital predistortion (DPD) theoretical framework tailored for wideband mmW 6G signals, addressing the unique nonlinear distortion challenges at frequencies above 24 GHz.
Designed and validated DPD algorithms through hardware measurement campaigns, bridging the gap between simulation-based models and real-world PA behavior.
Advanced linearization strategies that account for frequency-dependent memory effects in mmW power amplifiers, improving adjacent channel leakage ratio (ACLR) and error vector magnitude (EVM) performance.

Tools & Technologies

MATLAB, Python, Keysight ADS, RF instrumentation (signal generators, spectrum analyzers, NVNA), NXP mmW PA testbeds.

Impact

This work contributes to the foundational RF signal processing layer required for future 6G deployments, where wideband operation at mmW bands demands highly linear and power-efficient transmitter architectures.

Spectral Optimization and DPD for 5G Massive MIMO Systems

Sat, 01 Oct 2022 00:00:00 +0000

Overview

During my postdoctoral tenure at Tecnológico de Monterrey (2018–2026), supported by a CONACyT research fellowship, I led the development of DPD algorithms and spectral modeling techniques for 5G massive MIMO transmitter arrays, where per-antenna PA nonlinearity and crosstalk effects degrade system-level spectral efficiency.

Key Contributions

Developed surrogate behavioral models for PA linearization under high sparse data conditions, comparing polynomial, spline, and machine learning approaches (regression trees, random forests, CNNs) for wideband nonlinear modeling.
Designed DPD algorithms optimized for massive MIMO architectures, addressing the challenge of scaling linearization across large antenna arrays while managing computational complexity.
Performed system-level spectral efficiency analysis quantifying the impact of PA nonlinearity on massive MIMO capacity, beam pattern distortion, and adjacent channel interference.
Published results in Sensors (2022), IEEE ISCAS (2022), and contributed a book chapter to Machine Learning for Complex and Unmanned Systems (CRC Press, Taylor & Francis, 2024).
Advised 3 M.Sc. students and served on Ph.D. thesis committees related to this research line.

Tools & Technologies

MATLAB, Python (scikit-learn, TensorFlow), Keysight ADS, RF measurement testbed, statistical modeling frameworks.

Impact

This work bridges classical RF linearization theory with modern machine learning, demonstrating that data-driven surrogate models can match or exceed conventional polynomial DPD approaches — particularly in scenarios with limited or irregularly sampled training data, which is common in multi-band and multi-antenna deployments.

Automated Driving of GaN Chireix Power Amplifier for the Digital Predistortion Linearization

Tue, 01 Jun 2021 00:00:00 +0000

Coefficients Estimation of MPM Through LSE, ORLS and SLS for RF-PA Modeling and DPD

Mon, 01 Jan 2018 00:00:00 +0000

Modeling memory effects in RF power amplifiers applied to a digital pre-distortion algorithm and emulated on a DSP-FPGA board

Sun, 01 Mar 2015 00:00:00 +0000