Augmented twin-nonlinear two-box behavioral models for multicarrier LTE power amplifiers.

Area of Science:

• Electrical Engineering
• Signal Processing
• Microwave Engineering

Background:

• Doherty power amplifiers (DPAs) are crucial for efficient wireless communication.
• Strong memory effects in DPAs complicate behavioral modeling.
• Existing models struggle to balance accuracy and complexity.

Purpose of the Study:

• To introduce a novel class of behavioral models for LTE-driven DPAs.
• To address the challenge of strong memory effects in DPA modeling.
• To achieve both enhanced accuracy and reduced complexity.

Main Methods:

• Developed augmented twin-nonlinear two-box (ATNNTB) models.
• Cascaded a nonlinear memoryless function with a nonlinear memory polynomial.
• Incorporated cross terms to capture complex interactions.
• Validated models on Gallium Nitride (GaN) based DPAs.

Main Results:

• ATNNTB models significantly improve accuracy, reducing normalized mean square error (NMSE) by up to 3 dB.
• Achieved comparable performance to existing models with up to 80% fewer coefficients.
• Demonstrated enhanced accuracy and complexity reduction.

Conclusions:

• ATNNTB models offer a superior solution for modeling DPAs with strong memory effects.
• The proposed models provide a more efficient and accurate alternative for DPA design.
• This advancement facilitates improved performance in wireless communication systems.