Inferensys

Glossary

ET-DPD for Doherty PAs

The specialized application of envelope tracking digital predistortion to Doherty power amplifiers, addressing the unique impedance modulation and nonlinearity profiles of the carrier and peaking amplifier stages.
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LINEARIZATION ARCHITECTURE

What is ET-DPD for Doherty PAs?

ET-DPD for Doherty PAs is a specialized linearization architecture that combines envelope tracking digital predistortion with Doherty power amplifier topology to simultaneously maximize energy efficiency and correct the compounded nonlinearities arising from dynamic supply modulation and load modulation.

ET-DPD for Doherty PAs is the joint application of envelope tracking supply modulation and digital predistortion to the Doherty amplifier architecture. This technique addresses the unique distortion profile created when the Doherty's active load modulation—where the peaking amplifier dynamically changes the impedance seen by the carrier amplifier—interacts with the supply-dependent gain variations introduced by envelope tracking. The resulting nonlinear behavior cannot be corrected by conventional static DPD.

The core challenge lies in modeling the supply-dependent load-pull effects inherent to the Doherty topology. As the supply voltage tracks the envelope, the optimal load impedance for the carrier amplifier shifts, altering the Doherty combiner's efficiency and linearity. A dual-input behavioral model or augmented Volterra series is required to capture the interaction between instantaneous input power, dynamic drain voltage, and the impedance modulation state, enabling the predistorter to invert this multidimensional nonlinear transfer function.

LINEARIZATION ARCHITECTURE

Key Features of ET-DPD for Doherty PAs

Envelope tracking digital predistortion for Doherty power amplifiers addresses the unique nonlinearity profiles of carrier and peaking amplifier stages under dynamic supply modulation.

01

Dual-Input Doherty Behavioral Model

A specialized modeling framework that captures the distinct nonlinear behaviors of the carrier and peaking amplifier paths under dynamic supply modulation. Unlike single-input models, this approach treats the Doherty PA as a two-branch system with:

  • Carrier amplifier: Operates in Class-AB with supply-dependent gain compression
  • Peaking amplifier: Operates in Class-C with turn-on threshold nonlinearity
  • Impedance modulation: Load-pulling effects between branches vary with envelope tracking voltage

The model accepts both the RF input signal and the dynamic supply voltage as independent variables, accurately predicting the composite output distortion including AM/AM and AM/PM components.

< -55 dBc
ACLR Improvement
2-3x
Model Coefficients vs Single-Input
02

Load Modulation-Aware Linearization

Doherty PAs rely on active load modulation between the carrier and peaking amplifiers to achieve high efficiency at back-off power levels. Under envelope tracking, the dynamic supply voltage alters the impedance presented to each amplifier, creating supply-dependent load trajectories that must be characterized and inverted.

Key considerations include:

  • Carrier impedance shift: Varies with both input drive and instantaneous drain voltage
  • Peaking turn-on point: Shifts with supply voltage, altering the Doherty transition region
  • Phase discontinuity: Abrupt phase changes occur at the peaking amplifier activation threshold

The DPD must compensate for these modulation-dependent impedance variations to maintain linearity across the full ET operating range.

40-50%
Efficiency at 6 dB Back-off
03

Asymmetric Memory Effect Compensation

Doherty PAs exhibit asymmetric memory effects due to the different thermal and trapping characteristics of the carrier and peaking amplifier devices. Under envelope tracking, these effects become more pronounced:

  • Carrier thermal memory: Continuous conduction creates long-term thermal time constants
  • Peaking trapping effects: GaN HEMT devices exhibit gate-lag and drain-lag under pulsed Class-C operation
  • ET-induced memory: Supply modulator dynamics introduce additional frequency-dependent memory

The ET-DPD model must incorporate multi-branch memory polynomial structures with separate memory depth allocations for each amplifier path, capturing both short-term (nanosecond) and long-term (microsecond) memory effects.

3-5
Memory Taps per Branch
04

ET-DPD 3D Look-Up Table for Doherty

A three-dimensional predistortion LUT indexed by instantaneous input power, supply voltage, and the Doherty operating region (back-off vs. saturation). This structure compensates for the static nonlinearities unique to ET-Doherty systems:

  • Back-off region: Carrier amplifier dominates; LUT corrects supply-dependent gain compression
  • Transition region: Both amplifiers active; LUT compensates for impedance modulation nonlinearity
  • Saturation region: Peaking amplifier fully on; LUT addresses combined compression characteristics

The 3D LUT provides memoryless correction with fast lookup times suitable for real-time implementation, while augmented Volterra terms handle residual memory effects.

1024-4096
LUT Entries per Dimension
05

Peaking Amplifier Turn-On Linearization

The Class-C peaking amplifier introduces a hard nonlinearity at its turn-on threshold that creates severe distortion in the Doherty transition region. Under envelope tracking, this threshold becomes supply-dependent, complicating linearization:

  • Gain expansion: Peaking amplifier gain increases rapidly after turn-on, creating AM/AM kinks
  • Phase jump: Abrupt phase shift of 30-60 degrees occurs at peaking activation
  • ET interaction: Dynamic supply voltage shifts the turn-on point, requiring adaptive compensation

The ET-DPD must apply pre-distortion gain expansion in the transition region to cancel the peaking amplifier's nonlinear turn-on characteristic while maintaining stability.

30-60°
Phase Jump at Turn-on
06

Augmented Volterra for ET-Doherty

An extension of the Volterra series that incorporates dynamic supply voltage terms and Doherty-specific cross-terms to capture the complex nonlinear interactions between the carrier and peaking paths under envelope tracking:

  • Supply-dependent kernels: Model gain variation as a function of instantaneous drain voltage
  • Cross-branch terms: Capture intermodulation products from carrier-peaking interaction
  • ET-Doherty cross-terms: Account for the combined effect of supply modulation and load modulation

The augmented Volterra model provides high-fidelity linearization for wideband signals where memory effects dominate, though at the cost of increased computational complexity compared to memory polynomial approaches.

O(K³)
Computational Complexity
ET-DPD FOR DOHERTY PAS

Frequently Asked Questions

Addressing the most common technical questions about integrating envelope tracking digital predistortion with Doherty power amplifier architectures, including impedance modulation effects, dual-branch modeling, and efficiency-linearity co-optimization.

ET-DPD for Doherty PAs is a specialized linearization technique that combines envelope tracking (ET) dynamic supply modulation with digital predistortion (DPD) to simultaneously maximize efficiency and linearity in Doherty power amplifier architectures. It is necessary because the Doherty amplifier's inherent load modulation mechanism—where the peaking amplifier actively modifies the impedance seen by the carrier amplifier—creates a complex, signal-dependent nonlinearity profile. When envelope tracking is added, the dynamic drain voltage further modulates the amplifier's gain and phase response. These two nonlinear mechanisms interact multiplicatively, producing distortion that cannot be adequately corrected by conventional static DPD or ET alone. The combined ET-DPD approach models the amplifier as a dual-input system (RF input and supply voltage), capturing the supply-dependent gain compression and the load-dependent impedance modulation simultaneously to meet the stringent ACLR and EVM requirements of modern 5G NR waveforms.

Prasad Kumkar

About the author

Prasad Kumkar

CEO & MD, Inference Systems

Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.

His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.