{"id":4812,"date":"2025-11-25T06:05:24","date_gmt":"2025-11-25T06:05:24","guid":{"rendered":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?page_id=4812"},"modified":"2025-11-25T06:05:24","modified_gmt":"2025-11-25T06:05:24","slug":"specialized-models-per-modulation-family-routing-subsets-to-spectralcnn-signallstm-resnetrfand-signaltransformer","status":"publish","type":"page","link":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/?page_id=4812","title":{"rendered":"Specialized Models per Modulation Family: Routing Subsets to SpectralCNN, SignalLSTM, ResNetRF,and SignalTransformer"},"content":{"rendered":"\n<div data-wp-interactive=\"core\/file\" class=\"wp-block-file\"><object data-wp-bind--hidden=\"!state.hasPdfPreview\" hidden class=\"wp-block-file__embed\" data=\"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/11\/Specialized-Models-per-Modulation-Family-Routing-Subsets-to-SpectralCNN-SignalLSTM-ResNetRF-and-SignalTransformer.pdf\" type=\"application\/pdf\" style=\"width:100%;height:600px\" aria-label=\"Embed of Specialized Models per Modulation Family Routing Subsets to SpectralCNN, SignalLSTM, ResNetRF, and SignalTransformer.\"><\/object><a id=\"wp-block-file--media-91b18700-6b90-4b79-aa23-09af02923cad\" href=\"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/11\/Specialized-Models-per-Modulation-Family-Routing-Subsets-to-SpectralCNN-SignalLSTM-ResNetRF-and-SignalTransformer.pdf\">Specialized Models per Modulation Family Routing Subsets to SpectralCNN, SignalLSTM, ResNetRF, and SignalTransformer<\/a><a href=\"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/11\/Specialized-Models-per-Modulation-Family-Routing-Subsets-to-SpectralCNN-SignalLSTM-ResNetRF-and-SignalTransformer.pdf\" class=\"wp-block-file__button wp-element-button\" download aria-describedby=\"wp-block-file--media-91b18700-6b90-4b79-aa23-09af02923cad\">Download<\/a><\/div>\n\n\n\n<p class=\"wp-block-paragraph\">Deep learning-based RF modulation classifiers are<br>often deployed as single, \u201cgeneralist\u201d models trained over a large<br>mix of signal types, bands, and impairments. In practice, however,<br>different architectures excel on different families of modulations:<br>spectral CNNs shine on narrowband constellations, recurrent<br>models track slowly time-varying analog signals, and transformerstyle feature fusion can exploit joint IQ+FFT structure.<br>This paper studies a simple but powerful idea: route each<br>incoming signal to a specialized model chosen for its modulation<br>family, rather than sending every signal through the same<br>generalist. Building on a production-style RF ensemble classifier,<br>we define families (e.g., PSK, QAM, analog), assign each<br>family a specialist drawn from {SpectralCNN, SignalLSTM,<br>ResNetRF, SignalTransformer}, and compare this routing<br>scheme against a flat \u201call-modulations\u201d generalist.<br>On synthetic and replayed RF scenarios, family-specialized<br>models yield up to 3.4, 2.1, and 4.7absolute accuracy points over<br>the best generalist baselines for PSK, QAM, and analog signals<br>respectively, while reusing the same input builders and metric<br>logging already present in the system. We release a benchmark<br>harness and figure-generation pipeline so future specialists can<br>be dropped in without changing the LATEX.<br>Index Terms\u2014Automatic modulation classification, RF machine<br>learning, ensembles, specialization, deep learning.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Repository:<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/github.com\/bgilbert1984\/Routing-Subsets-to-SpectralCNN-SignalLSTM-ResNetRF-and-SignalTransformer\">bgilbert1984\/Routing-Subsets-to-SpectralCNN-SignalLSTM-ResNetRF-and-SignalTransformer: Deep neural networks have become the default approach for RF automatic modulation classification (AMC), with convolu- tional and recurrent architectures delivering robust performance across a wide range of SNRs and channels. Most practical pipelines, however, are optimized around a single generalist model trained over a heterogeneous mix<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Deep learning-based RF modulation classifiers areoften deployed as single, \u201cgeneralist\u201d models trained over a largemix of signal types, bands, and impairments. In practice, however,different architectures excel on different families of modulations:spectral CNNs shine on narrowband constellations, recurrentmodels track slowly time-varying analog signals, and transformerstyle feature fusion can exploit joint IQ+FFT structure.This paper studies a simple&hellip;&nbsp;<\/p>\n","protected":false},"author":2,"featured_media":2058,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"neve_meta_sidebar":"","neve_meta_container":"","neve_meta_enable_content_width":"","neve_meta_content_width":0,"neve_meta_title_alignment":"","neve_meta_author_avatar":"","neve_post_elements_order":"","neve_meta_disable_header":"","neve_meta_disable_footer":"","neve_meta_disable_title":"","footnotes":""},"class_list":["post-4812","page","type-page","status-publish","has-post-thumbnail","hentry"],"_links":{"self":[{"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/pages\/4812","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=4812"}],"version-history":[{"count":0,"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/pages\/4812\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/media\/2058"}],"wp:attachment":[{"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4812"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}