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Advancements in Power Amplifier Design and Linearization Techniques for Wireless Communication Systems

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 7740

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Editors

Dr. Pere L. Gilabert

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Guest Editor

Department of Signal Theory and Communications, Universitat Politècnica de Catalunya, 08860 Castelldefels, Spain
Interests: digital signal processing techniques for emerging wireless and efficient transmitter technologies
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Dr. Anna Piacibello

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Guest Editor

Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy
Interests: high-efficiency power amplifier designs for emerging wireless transmitters
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue, ‘Advancements in Power Amplifier Design and Linearization Techniques for Wireless Communication Systems’, explores cutting-edge developments in power amplifier (PA) technology, focusing on efficiency, linearity, and performance optimization for modern wireless networks. As 5G, 6G, and beyond demand higher data rates, wider bandwidths, and greater energy efficiency, PAs must support these requirements while minimizing distortion and spectral regrowth. This Special Issue covers emerging PA architectures, including Doherty power amplifiers, load-modulated balanced amplifiers (LMBAs), outphasing PAs, and envelope tracking, which enhance their efficiency and bandwidth capabilities. Additionally, advanced linearization techniques such as digital predistortion (DPD) and machine learning-based methods are explored to mitigate nonlinearities. The rise in phased arrays, massive MIMO, and beamforming further emphasizes the need for high-efficiency low-distortion PA designs and fosters the development of dedicated multi-input PA architectures and linearization techniques. Moreover, the transition toward all-digital transmitters (TX) is gaining momentum, offering new possibilities for fully digital signal generation and amplification. By integrating innovative research, this Special Issue aims to advance the development of next-generation high-performance PAs for wireless communication systems.

Dr. Pere L. Gilabert
Dr. Anna Piacibello
Guest Editors

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Keywords

power amplifier design
load-modulated balanced amplifiers (LMBAs)
outphasing power amplifiers
Doherty power amplifiers
all-digital transmitters (TX)
linearization techniques
efficiency enhancement
Digital Pre-Distortion (DPD)
envelope tracking
radio frequency (RF) and microwave engineering
nonlinear distortion compensation
5G and beyond wireless networks
phased arrays and beamforming
MIMO power amplifiers
high-efficiency PA architectures
machine learning for PA optimization

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Published Papers (7 papers)

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Research

24 pages, 11613 KB

Open AccessArticle

Dual RF Input Envelope Tracking Power Amplifier with Enhanced Load Modulation for Power–Efficiency–Linearity Trade-Off

by Marco Badii, Giovanni Lasagni, Monica Righini, Giovanni Collodi, Stefano Maddio and Alessandro Cidronali

Sensors 2026, 26(12), 3897; https://doi.org/10.3390/s26123897 (registering DOI) - 19 Jun 2026

Viewed by 111

Abstract

In this paper, we present an optimized driving strategy for a dual RF input envelope tracking power amplifier (ET PA) exploiting load modulation. The dual-input architecture enables dynamic load modulation (LM), allowing real-time adjustment of the load impedance to enhance performance over the signal dynamics typical of digital modulation schemes. The proposed approach considers a GaN HEMT-based LM-ET PA characterized under pulsed excitation across multiple amplitude and phase conditions of the load modulation control. Optimizing the control parameters yields a suitable shaping function that extends conventional ET supply modulation to include amplitude and phase control of the auxiliary amplifier, thereby improving the efficiency, output power, and linearity of the main amplifier. Experimental data demonstrate that the proposed dual RF input GaN-based LM-ET PA at 3.6 GHz outperforms a conventional ET PA in both efficiency and linearity when tested with high peak-to-average ratio (PAPR) signals. Full article

(This article belongs to the Special Issue Advancements in Power Amplifier Design and Linearization Techniques for Wireless Communication Systems)

20 pages, 1720 KB

Open AccessArticle

Behavioral Modeling of Dynamic Nonlinear Distortions in 5G Wireless Transmitters Using Cascaded Augmented Real-Valued Neural Networks

by Sharafa Bankole, Reem Alnajjar, Majid Ahmed, Souheil Bensmida and Oualid Hammi

Sensors 2026, 26(12), 3832; https://doi.org/10.3390/s26123832 - 16 Jun 2026

Viewed by 142

Abstract

Neural networks are increasingly adopted for performance enhancement in wireless communication infrastructure for 5G and 6G applications. This paper proposes a modular two-box neural network-based system for the behavioral modeling of dynamic nonlinear distortions observed in wireless transmitters. The proposed model, labeled cascaded augmented real-valued artificial neural networks (CAR-VANN), uses a first neural network with an augmented but memoryless input vector feature to model memoryless nonlinear behavior. This model is designed for low-complexity and coarse estimation of the nonlinear distortions. The second neural network, which aims to fine-tune the model output and boost its accuracy, is a conventional augmented real-valued time-delay neural network (ARVTDNN). Experimental validation shows that the CAR-VANN model can achieve the same performance as the ARVTDNN with a significant reduction in the number of parameters (between 35% and 52%). Accordingly, this model can be considered a viable alternative for the computationally efficient modeling of dynamic nonlinear distortions in 5G systems, reducing the computational complexity associated with neural networks-based models without compromising their performance. Full article

(This article belongs to the Special Issue Advancements in Power Amplifier Design and Linearization Techniques for Wireless Communication Systems)

23 pages, 2893 KB

Open AccessArticle

Concurrent Multi-Beam Digital Predistortion Using FFT Beamforming and Virtual Arrays

by Björn Langborn, Christian Fager, Rui Hou and Thomas Eriksson

Sensors 2026, 26(8), 2400; https://doi.org/10.3390/s26082400 - 14 Apr 2026

Viewed by 511

Abstract

A digital predistortion (DPD) scheme for concurrent multi-beam transmission in fully digital multiple-input, multiple-output (MIMO) systems, using Fast Fourier Transform (FFT) beamforming and so-called virtual-array processing, is proposed. In a MIMO array with nonlinear power amplifiers (PAs), transmitting multiple beams concurrently yields intermodulation products that end up in both user and non-user directions. In the setting with few users in a large array, the array dimension will typically be much larger than the number of generated intermodulation products. At the same time, linearization per PA is excessively costly for large arrays. This work shows that it is instead possible to linearize the system by producing predistorted user beams, and non-user intermodulation products, through DPD processing in a virtual array of a much smaller dimension than the physical array. Theoretical derivations and simulation examples show how this approach can lead to manyfold reductions in DPD complexity. Full article

(This article belongs to the Special Issue Advancements in Power Amplifier Design and Linearization Techniques for Wireless Communication Systems)

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18 pages, 3255 KB

Open AccessArticle

Performance Analysis and Coefficient Generation Method of Parallel Hammerstein Model Under Underdetermined Condition

by Nanzhou Hu, Youyang Xiang, Mingyang Li, Xianglu Li and Jie Tian

Sensors 2026, 26(1), 183; https://doi.org/10.3390/s26010183 - 26 Dec 2025

Viewed by 606

Abstract

Nonlinear signal models are widely used in power amplifier predistortion, full-duplex self-interference cancellation, and other scenarios. The parallel Hammerstein (PH) model is a typical nonlinear signal model, but its serial and parallel hybrid architecture brings difficulties in performance analysis and coefficient estimation. This paper focuses on the performance analysis and coefficient estimation of the PH model for nonlinear systems with memory effects, such as power amplifiers. By comparing the PH model with the memory polynomial (MP) model under identical basis functions, we analyze its performance across varying numbers of parallel branches, nonlinear orders, and memory depths. Using singular value decomposition (SVD), we derive a closed-form expression for the PH model’s performance under underdetermined conditions, establishing its relationship to the non-zero singular values of the MP model’s coefficient matrix. Based on this, we propose a coefficient generation method combining SVD and least squares (LS), which directly computes coefficients and assesses performance during execution. Simulations confirm the method’s effectiveness, showing that selecting branches associated with larger singular values achieves near-optimal performance with reduced complexity. Full article

(This article belongs to the Special Issue Advancements in Power Amplifier Design and Linearization Techniques for Wireless Communication Systems)

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20 pages, 29995 KB

Open AccessArticle

Digital Self-Interference Cancellation Strategies for In-Band Full-Duplex: Methods and Comparisons

by Amirmohammad Shahghasi, Gabriel Montoro and Pere L. Gilabert

Sensors 2025, 25(22), 6835; https://doi.org/10.3390/s25226835 - 8 Nov 2025

Cited by 1 | Viewed by 2341

Abstract

In-band full-duplex (IBFD) communication systems offer a promising means of improving spectral efficiency by enabling simultaneous transmission and reception on the same frequency channel. Despite this advantage, self-interference (SI) remains a major challenge to their practical deployment. Among the different SI cancellation (SIC) techniques, this paper focuses on digital SIC methodologies tailored for multiple-input multiple-output (MIMO) wireless transceivers operating under digital beamforming architectures. Two distinct digital SIC approaches are evaluated, employing a generalized memory polynomial (GMP) model augmented with Itô–Hermite polynomial basis functions and a phase-normalized neural network (PNN) to effectively model the nonlinearities and memory effects introduced by transmitter and receiver hardware impairments. The robustness of the SIC is further evaluated under both single off-line training and closed-loop real-time adaptation, employing estimation techniques such as least squares (LS), least mean squares (LMS), and fast Kalman (FK) for model coefficient estimation. The performance of the proposed digital SIC techniques is evaluated through detailed simulations that incorporate realistic power amplifier (PA) characteristics, channel conditions, and high-order modulation schemes. Metrics such as error vector magnitude (EVM) and total bit error rate (BER) are used to assess the quality of the received signal after SIC under different signal-to-interference ratio (SIR) and signal-to-noise ratio (SNR) conditions. The results show that, for time-variant scenarios, a low-complexity adaptive SIC can be realized using a GMP model with FK parameter estimation. However, in time-invariant scenarios, an open-loop SIC approach based on PNN offers superior performance and maintains robustness across various modulation schemes. Full article

(This article belongs to the Special Issue Advancements in Power Amplifier Design and Linearization Techniques for Wireless Communication Systems)

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15 pages, 1420 KB

Open AccessArticle

Discontinuity Characterization and Low-Complexity Smoothing in RF-PA Polynomial Piecewise Modeling

by Carolina Pedrosa, Dang-Kièn Germain Pham, Peter Rashev, Pierre Almairac, Jean-Christophe Nanan and Patricia Desgreys

Sensors 2025, 25(21), 6593; https://doi.org/10.3390/s25216593 - 26 Oct 2025

Viewed by 1109

Abstract

Piecewise modeling of power amplifiers (PAs) typically involves assembling different polynomials to capture nonlinear behavior across different operating regions. However, recombining these sub-models can introduce discontinuities at segment boundaries, degrading prediction accuracy and potentially impacting digital predistortion (DPD) performance. This work addresses this issue by introducing a statistical framework to detect discontinuities through localized variations in the conditional mean and variance of amplitude and phase responses. Using the Vector-Switched Generalized Memory Polynomial (VS-GMP) as a case study, we propose a low-complexity post-processing smoothing technique based on a raised cosine weighting function applied at model transition regions. Unlike structural approaches, the method requires no retraining and integrates seamlessly into existing workflows as a post-processing tool. Experimental validation across two PA architectures (Doherty and Single-Stage) and multiple 5G/LTE signals (20–200 MHz bandwidth, up to 11 dB PAPR, including carrier aggregation) demonstrates consistent improvements: up to a 3 dB NMSE reduction and notable spectral error suppression. Full article

(This article belongs to the Special Issue Advancements in Power Amplifier Design and Linearization Techniques for Wireless Communication Systems)

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17 pages, 6267 KB

Open AccessArticle

Local and Remote Digital Pre-Distortion for 5G Power Amplifiers with Safe Deep Reinforcement Learning

by Christian Spano, Damiano Badini, Lorenzo Cazzella and Matteo Matteucci

Sensors 2025, 25(19), 6102; https://doi.org/10.3390/s25196102 - 3 Oct 2025

Cited by 3 | Viewed by 1767

Abstract

The demand for higher data rates and energy efficiency in wireless communication systems drives power amplifiers (PAs) into nonlinear operation, causing signal distortions that hinder performance. Digital Pre-Distortion (DPD) addresses these distortions, but existing systems face challenges with complexity, adaptability, and resource limitations. This paper introduces DRL-DPD, a Deep Reinforcement Learning-based solution for DPD that aims to reduce computational burden, improve adaptation to dynamic environments, and minimize resource consumption. To ensure safety and regulatory compliance, we integrate an ad-hoc Safe Reinforcement Learning algorithm, CRE-DDPG (Cautious-Recoverable-Exploration Deep Deterministic Policy Gradient), which prevents ACLR measurements from falling below safety thresholds. Simulations and hardware experiments demonstrate the potential of DRL-DPD with CRE-DDPG to surpass current DPD limitations in both local and remote configurations, paving the way for more efficient communication systems, especially in the context of 5G and beyond. Full article

(This article belongs to the Special Issue Advancements in Power Amplifier Design and Linearization Techniques for Wireless Communication Systems)

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