[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122050-en":3,"doc-seo-122050-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122050,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Training from Zero - Radio Frequency Machine Learning Data Quantity Forecasting","Training data volume directly determines deployed system performance in machine learning, motivating the practical question of how much data is truly needed to reach a target quality level. This work studies modulation classification in the radio-frequency domain and evaluates how training quantity affects achievable performance, while keeping the procedure adaptable to other classification tasks across modalities. The approach aims to minimize initial data collection by using a small target dataset and extracting metrics that guide a more complete future collection plan. It also enables numerical comparison of dataset quality and quantity tied to architecture performance in the RFML setting.","Article  \nTraining from Zero: Radio Frequency Machine Learning Data Quantity Forecasting  \nWilliam H. Clark, IV 1, Alan J. Michaels 1  \n.03703v2 [ cs .LG] 14 Jun 2024  \narXiv:2205  \nCitation: Clark, IV, W.H.; Michaels, A..  \nTraining from Zero: Radio Frequency Machine Learning Data Quantity Forecasting. Preprints 2024, 1, 0 . [https://doi.org/](https://doi.org/)  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Virginia Tech National Security Institute, Blacksburg  \n* [Correspondence: bill.clark@vt.edu](Correspondence: bill.clark@vt.edu)  \nAbstract: The data used during training in any given application space is directly tied to the performance of the system once deployed. While there are many other factors that go into producing high performance models within machine learning, there is no doubt that the data used to train a system provides the foundation from which to build. One of the underlying rule of thumb heuristics used within the machine learning space is that more data leads to better models, but there is no easy answer for the question,“How much data is needed?” This work examines a modulation classification problem in the Radio Frequency domain space, attempting to answer the question of how much training data is required to achieve a desired level of performance, but the procedure readily applies to classification problems across modalities. The ultimate goal is determining an approach that requires the least amount of data collection to better inform a more thorough collection effort to achieve the desired performance metric. While this approach will require an initial dataset that is germane to the problem space to act as a target dataset on which metrics are extracted, the goal is to allow for the initial data to be orders of magnitude smaller than what is required for delivering a system that achieves the desired performance. An additional benefit of the techniques presented here is that the quality of different datasets can be numerically evaluated and tied together with the quantity of data, and ultimately, the performance of the architecture in the problem domain.  \nKeywords: data analysis, data collection, machine learning, neural networks, pattern recognition, physical layer, RF signals, RFML, signal synthesis, software radio, wireless communication  \n1. Introduction  \nMachine Learning (ML) is “the capacity of computers to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyse and infer from patterns in data” [1] . No matter the field, ML begins and ends on the data available to use during training. Without relevant data to learn from, ML is effectively a“garbage in, garbage out” system [2] . The application of ML to problems within the Radio Frequency (RF) domain is no exception to this rule, yet within the scope of intentional man-made emissions, data is easier to synthesize than within more prolific domains such as image processing [3] . Due to the readily available tools for developing ML-based algorithms (Tensorflow [4], PyTorch [5], etc.), and this ease of synthesis for establishing comprehensive datasets, there has been an explosion of published work in the field. Adding to the ease of training models, the RF domain has the availability of open source toolsets for synthesizing RF waveforms such as GNU Radio [6] and Liquid-DSP [7] to name a few. However, going from a purely synthetic environment to a functional application running in the real world has a number of considerations that must be addressed. A brief explanation about the gaps from synthetic data to functional application data is discussed in Section 1.2.  \nThis paper focuses o","cbCaisuwmGbuoPSg","https://ap.wps.com/l/cbCaisuwmGbuoPSg","pdf",2918747,1,20,"English","en",105,"# Introduction\n## Motivation: data quantity and model performance\n## RFML scope and physical-layer focus\n## Core questions and dataset challenges\n## Existing heuristics and dataset quality concepts","[{\"question\":\"Why does training data quantity matter for machine learning in deployed systems?\",\"answer\":\"Training data used during model development becomes the foundation for what the system learns, and therefore strongly influences performance after deployment.\"},{\"question\":\"What problem does the paper focus on in the radio frequency domain?\",\"answer\":\"It examines modulation classification using radio-frequency machine learning, with the goal of determining how much training data is required for a desired performance level.\"},{\"question\":\"How does the approach reduce the amount of data that must be collected upfront?\",\"answer\":\"It requires an initial dataset germane to the problem space to serve as a target dataset for metric extraction, allowing the initial dataset to be orders of magnitude smaller than what would be needed for final desired performance.\"}]","Training from Zero - Radio Frequency Machine Learning Data Quantity Forecasting | PDF",1785808574,50,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"training-from-zero-radio-frequency-machine-learning-data-quantity-forecasting","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/training-from-zero-radio-frequency-machine-learning-data-quantity-forecasting/122050/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why does training data quantity matter for machine learning in deployed systems?","Question",{"text":75,"@type":76},"Training data used during model development becomes the foundation for what the system learns, and therefore strongly influences performance after deployment.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What problem does the paper focus on in the radio frequency domain?",{"text":80,"@type":76},"It examines modulation classification using radio-frequency machine learning, with the goal of determining how much training data is required for a desired performance level.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the approach reduce the amount of data that must be collected upfront?",{"text":84,"@type":76},"It requires an initial dataset germane to the problem space to serve as a target dataset for metric extraction, allowing the initial dataset to be orders of magnitude smaller than what would be needed for final desired performance.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":29,"slug":113},6,"Technology","technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":21,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":127,"show_sort_weight":21,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":106,"slug":136},19,"General","general"]