Inferensys

Glossary

Dual-Band Doherty DPD

Digital predistortion specifically designed to linearize a dual-band Doherty power amplifier, accounting for the architecture's unique nonlinear characteristics and load modulation behavior.
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LINEARIZATION ARCHITECTURE

What is Dual-Band Doherty DPD?

A specialized digital predistortion technique designed to linearize a Doherty power amplifier that is concurrently transmitting two independent carrier signals at different frequencies, accounting for the architecture's unique load modulation behavior and cross-band interactions.

Dual-Band Doherty DPD is a digital predistortion architecture that simultaneously compensates for nonlinear distortion in a Doherty power amplifier amplifying two concurrent signals at separate carrier frequencies. It extends conventional dual-band DPD by incorporating the Doherty amplifier's load modulation dynamics, where the main and peaking amplifiers interact through an impedance inverter, creating envelope-dependent nonlinear characteristics distinct from balanced amplifier designs.

The predistorter model must capture both cross-band intermodulation distortion and the Doherty-specific AM-AM/AM-PM asymmetries caused by the peaking amplifier's turn-on transition. Implementation typically employs a 2D memory polynomial augmented with Doherty-aware terms that model the carrier amplifier's gain compression and the peaking amplifier's phase discontinuities, enabling efficient linearization of this high-efficiency architecture in multi-standard base stations.

LINEARIZATION ARCHITECTURE

Key Characteristics of Dual-Band Doherty DPD

Digital predistortion specifically designed to linearize a dual-band Doherty power amplifier, accounting for the architecture's unique nonlinear characteristics and load modulation behavior.

01

Load Modulation Dynamics

The Doherty amplifier achieves high efficiency through active load modulation between its carrier and peaking amplifiers. In a dual-band Doherty configuration, this load modulation becomes frequency-dependent, creating a nonlinear interaction surface that varies with both the instantaneous envelope of each band and their relative phase. The DPD model must capture these dynamic impedance trajectories to accurately predict and invert the distortion.

02

Cross-Band AM/AM and AM/PM

Unlike single-band Doherty PAs, the gain and phase response in one band is modulated by the instantaneous power level of the other band. This results in a two-dimensional distortion surface where:

  • AM/AM distortion in Band 1 depends on |x1| and |x2|
  • AM/PM distortion in Band 1 depends on |x1| and |x2| The DPD must implement a 2D correction function to compensate for this envelope coupling.
03

Efficiency vs. Linearity Trade-off

The Doherty architecture is biased deeply into Class-AB (carrier) and Class-C (peaking) to maximize power-added efficiency. This aggressive biasing introduces strong gain compression and phase distortion near saturation. In dual-band operation, the composite signal's peak-to-average ratio is higher, pushing the amplifier deeper into compression more frequently. The DPD must provide sufficient expansion gain to linearize without sacrificing the efficiency gains of the Doherty topology.

04

2D Memory Polynomial Modeling

A 2D Memory Polynomial (2D-MMP) is the foundational model for dual-band Doherty DPD. It extends the standard memory polynomial by including cross-terms that are functions of the envelope magnitudes of both bands:

  • y1(n) = Σ a_k,m * x1(n-m) * |x1(n-m)|^k * |x2(n-m)|^l
  • These cross-terms capture the intermodulation coupling and cross-band memory effects inherent in the shared Doherty combiner network.
05

Combiner Network Nonlinearity

The Doherty output combiner—typically an impedance inverter and quarter-wave transformer—is designed for a single frequency. In dual-band operation, the combiner's frequency-dependent impedance creates additional nonlinear reflections and standing waves. The DPD model must implicitly learn to compensate for these passive network nonlinearities that manifest as memory effects and band-specific impedance mismatches at the transistor current source plane.

06

Indirect Learning with 2D Post-Distorter

The Indirect Learning Architecture (ILA) is adapted for dual-band Doherty DPD by using a 2D post-distorter in the feedback path. The post-distorter estimates the inverse model using the attenuated PA output and the original baseband signals from both bands. The coefficient extraction solves a least-squares problem on the 2D basis matrix. Once converged, the coefficients are copied to the forward predistorter. This architecture handles the non-commutativity of the Doherty nonlinearity and the DPD function.

DUAL-BAND DOHERTY DPD

Frequently Asked Questions

Addressing the most critical engineering questions on linearizing dual-band Doherty power amplifiers, from load modulation interactions to real-time coefficient extraction.

Dual-Band Doherty DPD is a specialized digital predistortion technique designed to linearize a Doherty power amplifier that is concurrently transmitting two independent carrier signals at different frequencies. It is necessary because the Doherty architecture's intrinsic load modulation mechanism, which provides high efficiency at power back-off, creates a unique, signal-dependent nonlinear profile. When a Doherty PA is driven by a dual-band signal, the interaction between the peaking and carrier amplifiers generates complex cross-band distortion and intermodulation products that standard single-band or memoryless DPD cannot correct. This specialized DPD must model not only the AM/AM and AM/PM distortions of each band but also the dynamic impedance interactions caused by the instantaneous composite envelope of both signals, ensuring compliance with stringent Adjacent Channel Leakage Ratio (ACLR) and Error Vector Magnitude (EVM) requirements for modern base stations.

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.