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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 820

Special Issue Editors


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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
Special Issues, Collections and Topics in MDPI journals

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

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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 (1 paper)

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Research

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 362
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
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