SparseDPD: A Sparse Neural Network-based Digital Predistortion FPGA Accelerator for RF Power Amplifier Linearization

[2506.16591] SparseDPD: A Sparse Neural Network-based Digital Predistortion FPGA Accelerator for RF Power Amplifier Linearization Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Hardware Architecture arXiv:2506.16591 (cs) [Submitted on 19 Jun 2025] Title: SparseDPD: A Sparse Neural Network-based Digital Predistortion FPGA Accelerator for RF Power Amplifier Linearization Authors: Manno Versluis , Yizhuo Wu , Chang Gao View a PDF of the paper titled SparseDPD: A Sparse Neural Network-based Digital Predistortion FPGA Accelerator for RF Power Amplifier Linearization, by Manno Versluis and 2 other authors View PDF HTML (experimental) Abstract: Digital predistortion (DPD) is crucial for linearizing radio frequency (RF) power amplifiers (PAs), improving signal integrity and efficiency in wireless systems. Neural network (NN)-based DPD methods surpass traditional polynomial models but face computational challenges limiting their practical deployment. This paper introduces SparseDPD, an FPGA accelerator employing a spatially sparse phase-normalized time-delay neural network (PNTDNN), optimized through unstructured pruning to reduce computational load without accuracy loss. Implemented on a Xilinx Zynq-7Z010 FPGA, SparseDPD operates at 170 MHz, achieving exceptional linearization performance (ACPR: -59.4 dBc, EVM: -54.0 dBc, NMSE: -48.2 dB) with only 241 mW dynamic power, using 64 parameters with 74% sparsity. This work demonstrates FPGA-based acceleration, making NN-based DPD practical and efficient for real-time wireless communication applications. Code is publicly available at this https URL . Comments: Accepted to FPL 2025 Subjects: Hardware Architecture (cs.AR) ; Signal Processing (eess.SP) Cite as: arXiv:2506.16591 [cs.AR] (or arXiv:2506.16591v1 [cs.AR] for this version) https://doi.org/10.48550/arXiv.2506.16591 Focus to learn more arXiv-issued DOI via DataCite Journal reference: 2025 35th International Conference on Field-Programmable Logic and Applications (FPL) Related DOI : https://doi.org/10.1109/FPL68686.2025.00031 Focus to learn more DOI(s) linking to related resources Submission history From: Chang Gao [ view email ] [v1] Thu, 19 Jun 2025 20:26:05 UTC (1,107 KB) Full-text links: Access Paper: View a PDF of the paper titled SparseDPD: A Sparse Neural Network-based Digital Predistortion FPGA Accelerator for RF Power Amplifier Linearization, by Manno Versluis and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.AR < prev | next > new | recent | 2025-06 Change to browse by: cs eess eess.SP References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• Optimum design of a new predistortion scheme for high linearity K-band MMIC power amplifiers#638669
• A Neural Hierarchical-Matrix Preconditioner for Real-Time GPU Solves#1077888
• Direct linearization, Cauchy matrix and Sato Grassmannian#971866
• BLAS-based Mixed Precision Block Memory Accessor with Applications to Sparse Direct Solvers#30152
• Digital Predistortion of Power Amplifiers for Quantum Computing#924039