What makes the Accurate Complexity-Reduced Simplified Volterra (ACR-SV) series model a step forward in RF power amplifier technology?

The ACR-SV model improves upon the conventional Simplified Volterra series by treating memoryless nonlinearity and memory effects separately. It integrates a nonlinear memory effect component. This separation enables targeted optimization and efficient coefficient usage, leading to a more accurate and less complex model, particularly for RF power amplifiers.

How does the Accurate Complexity-Reduced Simplified Volterra (ACR-SV) model address the limitations of traditional methods in modeling RF power amplifiers?

The ACR-SV model separates and addresses memoryless nonlinearities and memory effects, unlike traditional methods. Traditional methods treat these as a single entangled problem. The ACR-SV model's approach allows for targeted optimization, leading to better accuracy with fewer coefficients. Traditional models such as the Memory Polynomial model fail to address these effects separately.

How was the Accurate Complexity-Reduced Simplified Volterra (ACR-SV) model tested and validated for effectiveness?

The ACR-SV model was validated using a GaN Class-F PA with complex modulated signals, including WCDMA 1001 and a single-carrier 16QAM signal. It was compared against the Memory Polynomial model, Augmented Complexity-Reduced Generalized Memory Polynomial model and the conventional Simplified Volterra series. The model’s forward modeling accuracy and DPD application effectiveness were evaluated.

What are the specific performance improvements and efficiency gains demonstrated by the Accurate Complexity-Reduced Simplified Volterra (ACR-SV) model compared to other models?

The ACR-SV model achieved a 2.61dB improvement in Normalized Mean Square Error (NMSE) compared to the Memory Polynomial model in forward modeling. It also showed an average Adjacent Channel Power Ratio (ACPR) improvement of 3.7/4.2dB over the Memory Polynomial model in DPD applications. Furthermore, it did this with 13% fewer model coefficients than the Memory Polynomial model. Compared to the Augmented Complexity-Reduced Generalized Memory Polynomial model, the ACR-SV model provided a 1.39 dB NMSE improvement and 0.7/0.6dB ACPR improvement with a comparable number of coefficients. The ACR-SV model maintains a similar accuracy to the Simplified Volterra series but reduces the number of coefficients by approximately 53%.

How does the Accurate Complexity-Reduced Simplified Volterra (ACR-SV) model impact the future of RF power amplifier technology and wireless communication?

The ACR-SV model reduces complexity in digital predistortion (DPD) solutions for RF power amplifiers, leading to more efficient wireless communication. As communication systems evolve, models like the ACR-SV become increasingly important for high-performance wireless communication. The ACR-SV model reduces computational complexity without significant performance loss.

Digital illustration of radio waves converging into a power amplifier.

Revolutionizing RF Power Amplifiers: How the ACR-SV Model Achieves Accuracy and Efficiency

Mira Elwood in Tech & Innovation September 2025 • 4 min read.

"Discover the advanced ACR-SV series technology that simplifies Volterra series for RF power amplifiers, enhancing performance with reduced complexity and superior accuracy."

In today's communication systems, power amplifiers (PAs) are critical, yet they inherently introduce non-linearities that can degrade signal quality. As we push for higher bandwidths and more complex modulation schemes, these distortions become increasingly problematic. To counter these issues, engineers are always innovating new methods for linearization, with digital predistortion (DPD) emerging as a powerful and cost-effective solution.

Digital predistortion relies on creating accurate behavioral models of PAs. One popular approach involves Volterra series, but its complexity can be overwhelming due to the sheer number of coefficients required. To address this, researchers have been exploring simplified Volterra series models. This has led to models like the Memory Polynomial (MP), Generalized Memory Polynomial (GMP), and complexity-reduced versions like the ACR-GMP. Each attempts to balance accuracy and computational efficiency.

Now, there is an introduction to the Accurate Complexity-Reduced Simplified Volterra (ACR-SV) series model which presents a significant step forward. This model enhances the conventional Simplified Volterra (SV) series by carefully considering memoryless nonlinearity and memory effects separately, while integrating a nonlinear memory effect (NME) component. This is designed to boost accuracy without drastically increasing complexity.

What Makes ACR-SV the Superior Choice for RF Power Amplifiers?

Digital illustration of radio waves converging into a power amplifier.

The ACR-SV model distinguishes itself through its innovative approach to modeling RF power amplifiers. Unlike traditional methods that treat nonlinearities and memory effects as a single, intertwined problem, the ACR-SV model separates these phenomena. By addressing memoryless nonlinearities and memory effects individually and then integrating a nonlinear memory effect component, the ACR-SV model achieves a more refined representation of the PA's behavior. This separation allows for targeted optimization and more efficient coefficient usage, ultimately leading to a more accurate and less complex model.

To validate the effectiveness of the ACR-SV model, it was tested using a GaN Class-F PA driven by complex modulated signals, including a WCDMA 1001 signal and a single-carrier 16QAM signal with a 40MHz bandwidth. The performance of the ACR-SV model was then compared against other state-of-the-art models, such as the Memory Polynomial (MP) model, the Augmented Complexity-Reduced Generalized Memory Polynomial (ACR-GMP), and the conventional Simplified Volterra (SV) model. The evaluation focused on both forward modeling accuracy and DPD application effectiveness.

The tests and the ACR-SV model delivered impressive results:

Improved Accuracy: The ACR-SV model demonstrated a 2.61dB improvement in Normalized Mean Square Error (NMSE) compared to the MP model in forward modeling.
Enhanced DPD Performance: The model showed an average Adjacent Channel Power Ratio (ACPR) improvement of 3.7/4.2dB over the MP model in DPD applications.
Reduced Complexity: It achieved these improvements with 13% fewer model coefficients than the MP model.
Competitive with ACR-GMP: Compared to the ACR-GMP model, the ACR-SV model provided a 1.39 dB NMSE improvement and 0.7/0.6dB ACPR improvement with a comparable number of coefficients.
Efficient Design: While maintaining similar accuracy to the SV model, the ACR-SV model reduced the number of coefficients by approximately 53%.

These results demonstrate that the ACR-SV model offers a superior balance of accuracy and complexity, making it a highly attractive option for RF power amplifier modeling and linearization. By significantly reducing the number of coefficients without sacrificing performance, the ACR-SV model addresses a critical challenge in the field, paving the way for more efficient and effective DPD solutions.

The Future of RF Power Amplifier Technology

The ACR-SV model represents a significant advancement in RF power amplifier technology, offering a pathway to more efficient, accurate, and less complex DPD solutions. As communication systems continue to evolve, models like the ACR-SV will play a crucial role in enabling high-performance wireless communication.