Inferensys

Glossary

Power-Added Efficiency (PAE)

Power-Added Efficiency (PAE) is the metric quantifying a power amplifier's conversion of DC supply power to net RF output power, calculated as the ratio of (RF output power minus RF input power) to DC input power.
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RF METRIC

What is Power-Added Efficiency (PAE)?

Power-Added Efficiency (PAE) is the definitive metric for evaluating a power amplifier's ability to convert DC supply power into useful RF output power, accounting for the RF input drive power.

Power-Added Efficiency (PAE) is calculated as the ratio of the net RF power added by the amplifier (RF output power minus RF input power) to the total DC power consumed from the supply. This metric is distinct from drain efficiency because it subtracts the RF input drive signal, providing a true measure of the amplifier's power conversion effectiveness, especially critical in high-gain or multi-stage designs where input power is non-negligible.

PAE is the primary figure of merit for optimizing Doherty amplifier architectures and envelope tracking systems, where the goal is maximizing efficiency at significant output power back-off. High PAE directly reduces thermal dissipation and operational expenditure in base stations, making it a key performance indicator when evaluating the effectiveness of digital predistortion and linearization techniques that allow operation closer to compression without violating spectral mask requirements.

EFFICIENCY METRICS

Key Characteristics of PAE

Power-Added Efficiency (PAE) is the definitive metric for evaluating a power amplifier's ability to convert DC supply power into useful RF output, accounting for the RF input drive power. It provides a holistic view of amplifier performance critical for thermal management and energy-conscious design.

01

The Fundamental PAE Equation

PAE is mathematically defined as the ratio of the net RF power added by the amplifier to the total DC power consumed from the supply.

  • Formula: PAE = (P_RF_out - P_RF_in) / P_DC
  • P_RF_out: The total RF output power delivered to the load.
  • P_RF_in: The RF input drive power, which is subtracted to isolate the amplifier's true contribution.
  • P_DC: The total DC power drawn from the power supply (V_dc × I_dc).

This metric is superior to simple drain efficiency because it penalizes low-gain amplifiers that require significant input drive power.

02

Relationship with Drain Efficiency

While often confused, PAE and drain efficiency (η_D) are distinct metrics that converge only under specific conditions.

  • Drain Efficiency (η_D): Defined simply as P_RF_out / P_DC, ignoring input power.
  • Key Distinction: For a high-gain amplifier where P_RF_in is negligible compared to P_RF_out, PAE ≈ η_D.
  • Low-Gain Scenario: In mmWave or broadband amplifiers with 10 dB gain, P_RF_in is 10% of P_RF_out, causing a significant divergence between PAE and η_D.
  • Design Implication: PAE is the more honest metric for evaluating overall transmitter line-up efficiency.
03

PAE vs. Output Power Back-Off

PAE is not a single fixed value; it varies dramatically with the amplifier's operating point relative to its saturated output power (P_sat).

  • At Saturation (P_sat): PAE peaks as the amplifier operates in compression, but linearity is severely degraded.
  • At Back-Off (e.g., 6-8 dB OBO): PAE drops significantly, often to 15-25% for a Class-AB amplifier, which is the typical operating region for signals with high Peak-to-Average Power Ratio (PAPR).
  • Doherty Advantage: The Doherty architecture is specifically designed to maintain a high PAE plateau over a wide back-off range, making it essential for modern communication signals.
04

Impact of Gain Compression on PAE

PAE is intricately linked to the amplifier's nonlinear AM-AM distortion characteristics, particularly gain compression.

  • 1-dB Compression Point (P1dB): As input drive increases toward P1dB, the gain begins to compress, causing P_RF_out to increase more slowly.
  • PAE Peak Location: Maximum PAE typically occurs 1-2 dB beyond the P1dB point, deep in the compression region where linearity is poor.
  • Linearization Requirement: To operate near peak PAE while meeting Error Vector Magnitude (EVM) and Adjacent Channel Leakage Ratio (ACLR) specifications, Digital Predistortion (DPD) is mandatory to correct the resulting nonlinear distortion.
05

Thermal Dependence and Memory Effects

PAE is not a static, isothermal parameter; it degrades dynamically due to self-heating and thermal memory effects.

  • Channel Temperature Rise: As the amplifier dissipates power (P_DC - P_RF_out), the junction temperature increases, reducing carrier mobility and gain.
  • Dynamic PAE Collapse: Under sustained high-power transmission, the instantaneous PAE can sag by several percentage points due to thermal time constants.
  • GaN HEMT Resilience: Gallium Nitride transistors exhibit superior PAE retention at elevated temperatures compared to GaAs or LDMOS, a key reason for their adoption in high-power density Doherty amplifiers.
06

PAE in Envelope Tracking Systems

Envelope Tracking (ET) power supplies dynamically modulate the drain voltage of the amplifier in sync with the instantaneous signal envelope, dramatically boosting average PAE.

  • Fixed Supply Limitation: With a constant drain voltage, the amplifier wastes significant DC power as heat during low-envelope periods.
  • ET Operation: By reducing V_dc during low-power moments, ET minimizes the P_DC term in the PAE equation, improving efficiency at deep back-off by 10-15 percentage points.
  • DPD Co-optimization: The dynamic supply modulation introduces new nonlinearities that require a joint ET-DPD linearization strategy to maintain both high PAE and signal fidelity.
EFFICIENCY METRIC COMPARISON

PAE vs. Drain Efficiency vs. Total Efficiency

Comparison of the three primary efficiency metrics used to characterize power amplifier performance, highlighting what each captures and omits.

FeaturePower-Added Efficiency (PAE)Drain Efficiency (ηD)Total Efficiency (ηT)

Definition

Ratio of (RF output power minus RF input power) to DC input power

Ratio of RF output power to DC input power

Ratio of RF output power to total input power (DC + RF)

Formula

PAE = (Pout_RF - Pin_RF) / Pdc

ηD = Pout_RF / Pdc

ηT = Pout_RF / (Pdc + Pin_RF)

Accounts for RF drive power

Accounts for DC power consumption

Accounts for gain of the amplifier

Typical value for Doherty PA at 6 dB OBO

45-55%

50-60%

48-58%

Relevance for low-gain stages

Critical metric; low gain inflates discrepancy

Overestimates useful efficiency

Provides complete picture but rarely cited

Industry standard for PA datasheets

POWER-ADDED EFFICIENCY

Frequently Asked Questions

Clear, technically precise answers to the most common questions about Power-Added Efficiency (PAE), the definitive metric for evaluating power amplifier performance in wireless communication systems.

Power-Added Efficiency (PAE) is the metric that quantifies a power amplifier's effectiveness at converting DC supply power into useful RF output power, while accounting for the RF input power required to drive the amplifier. It is calculated as: PAE = (Pout_RF - Pin_RF) / Pdc_DC × 100%, where Pout_RF is the RF output power, Pin_RF is the RF input power, and Pdc_DC is the total DC power consumed from the supply. This formula distinguishes PAE from simple drain efficiency by subtracting the RF drive power, making it the most honest measure of an amplifier's true contribution to a transmitter chain. For a Doherty amplifier operating at 6 dB back-off with 40 dBm output, 30 dBm input, and 50 W DC consumption, the PAE would be approximately 20%.

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.