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

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

Deadline for manuscript submissions: 25 April 2026 | Viewed by 2903

Special Issue Editors

Dr. Pere L. Gilabert

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals
Dr. Anna Piacibello

E-Mail Website
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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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 (3 papers)

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Research

20 pages, 29995 KB  
Article
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
Viewed by 739
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) [...] Read more.
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  
Article
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 632
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 [...] Read more.
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  
Article
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
Viewed by 921
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. [...] Read more.
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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