{"id":3307,"date":"2025-09-12T17:12:48","date_gmt":"2025-09-12T17:12:48","guid":{"rendered":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=3307"},"modified":"2025-09-12T17:12:48","modified_gmt":"2025-09-12T17:12:48","slug":"hypergraph-rf-network-reconstruction-with-streaming-imm-rf-nerf-priors","status":"publish","type":"post","link":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=3307","title":{"rendered":"Hypergraph RF Network Reconstruction with Streaming IMM-RF-NeRF Priors"},"content":{"rendered":"\n<figure class=\"wp-block-embed is-type-wp-embed is-provider-spectrcyde wp-block-embed-spectrcyde\"><div class=\"wp-block-embed__wrapper\">\n<blockquote class=\"wp-embedded-content\" data-secret=\"uBi2VnM2CJ\"><a href=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?page_id=3303\">Hypergraph RF Network Reconstruction withStreaming IMM-RF-NeRF Priors<\/a><\/blockquote><iframe class=\"wp-embedded-content\" sandbox=\"allow-scripts\" security=\"restricted\" style=\"position: absolute; visibility: hidden;\" title=\"&#8220;Hypergraph RF Network Reconstruction withStreaming IMM-RF-NeRF Priors&#8221; &#8212; Spectrcyde\" src=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?page_id=3303&#038;embed=true#?secret=893X91JE6X#?secret=uBi2VnM2CJ\" data-secret=\"uBi2VnM2CJ\" width=\"600\" height=\"338\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\"><\/iframe>\n<\/div><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h1 class=\"wp-block-heading\"><\/h1>\n\n\n\n<figure class=\"wp-block-image size-full\"><img data-opt-id=1717325584  fetchpriority=\"high\" decoding=\"async\" width=\"879\" height=\"842\" src=\"https:\/\/ml6vmqguit1n.i.optimole.com\/w:auto\/h:auto\/q:mauto\/f:best\/https:\/\/172-234-197-23.ip.linodeusercontent.com\/wp-content\/uploads\/2025\/09\/image-46.png\" alt=\"\" class=\"wp-image-3308\" srcset=\"https:\/\/ml6vmqguit1n.i.optimole.com\/w:879\/h:842\/q:mauto\/f:best\/https:\/\/172-234-197-23.ip.linodeusercontent.com\/wp-content\/uploads\/2025\/09\/image-46.png 879w, https:\/\/ml6vmqguit1n.i.optimole.com\/w:300\/h:287\/q:mauto\/f:best\/https:\/\/172-234-197-23.ip.linodeusercontent.com\/wp-content\/uploads\/2025\/09\/image-46.png 300w, https:\/\/ml6vmqguit1n.i.optimole.com\/w:768\/h:736\/q:mauto\/f:best\/https:\/\/172-234-197-23.ip.linodeusercontent.com\/wp-content\/uploads\/2025\/09\/image-46.png 768w\" sizes=\"(max-width: 879px) 100vw, 879px\" \/><\/figure>\n\n\n\n<p><strong>Benjamin J. Gilbert<\/strong><br>College of the Mainland \u2013 Robotic Process Automation<br><a href=\"mailto:bgilbert2@com.edu\">bgilbert2@com.edu<\/a><\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">Abstract<\/h2>\n\n\n\n<p>We present a <strong>streaming hypergraph formulation<\/strong> for RF scene understanding. A <strong>lightweight collector<\/strong> infers higher-order interactions between emitters, reflectors, and receivers, while an <strong>IMM-RF-NeRF prior<\/strong> enforces geometric consistency. The pipeline auto-benchmarks, reporting <strong>precision, recall, F1, and latency<\/strong>, with ablations on hyperedge cardinality.<\/p>\n\n\n\n<p>The approach achieves <strong>competitive reconstruction accuracy<\/strong> while maintaining <strong>real-time streaming performance<\/strong>, aligning with the Guangdong engineering ethos: <strong>fast, reproducible, and deployment-oriented<\/strong>.<\/p>\n\n\n\n<p><strong>Index Terms\u2014<\/strong> RF hypergraphs, NeRF, inductive moment matching, streaming inference, reproducibility<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">I. Introduction<\/h2>\n\n\n\n<p>RF environments exhibit <strong>multi-body dynamics<\/strong>: direct paths, multipath reflections, interference, and collaborative behaviors. <strong>Graph-based models<\/strong> capture pairwise links but fail to represent <strong>higher-order dependencies<\/strong> critical for <strong>mesh networking, distributed beamforming, and cooperative jamming<\/strong>.<\/p>\n\n\n\n<p>We propose a <strong>hypergraph-based representation<\/strong>:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Nodes<\/strong> = RF emitters\/receivers with signal features.<\/li>\n\n\n\n<li><strong>Hyperedges<\/strong> = multi-way interactions inferred from correlation.<\/li>\n\n\n\n<li><strong>IMM-RF-NeRF priors<\/strong> = enforce geometric and physical plausibility during streaming collection.<\/li>\n<\/ul>\n\n\n\n<p>The contribution is a <strong>minimal reproducible kit<\/strong>: one command reproduces all benchmarks (precision\/recall\/F1, latency, ablations).<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">II. Method<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">A. Hypergraph Construction<\/h3>\n\n\n\n<p>Streaming observations build hypergraph H=(V,E)H=(V,E):<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Vertices v\u2208Vv \\in V: RF nodes with (x,y,z,f,P,BW)(x,y,z,f,P,BW).<\/li>\n\n\n\n<li>Hyperedges e\u2208Ee \\in E: multi-node correlations detected via spatial distance, frequency similarity, and signal thresholds.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">B. Streaming Collector<\/h3>\n\n\n\n<p><strong>RFHypergraphCollector<\/strong> maintains:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Signal strength threshold \u03c4s\\tau_s.<\/li>\n\n\n\n<li>Max hyperedge cardinality kmaxk_{max}.<\/li>\n\n\n\n<li>Spatial\/frequency tolerance windows.<\/li>\n\n\n\n<li>Cache invalidation for dynamic updates.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">C. IMM-RF-NeRF Priors<\/h3>\n\n\n\n<p><strong>IMM-RF-NeRF<\/strong> provides density estimates \u03c1(x,y,z)\\rho(x,y,z), enforcing geometric consistency. The inductive moment matching layer generalizes across frequency bands and deployment scenarios.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">III. Experimental Setup<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Synthetic scenarios<\/strong>: 60 RF nodes in a 100 m cube.<\/li>\n\n\n\n<li><strong>Frequency allocation<\/strong>: clustered 400\u20132600 MHz.<\/li>\n\n\n\n<li><strong>Power<\/strong>: \u221235 \u00b1 6 dBm.<\/li>\n\n\n\n<li><strong>Ground truth hyperedges<\/strong>: formed if d&lt;25md &lt; 25m and \u0394f&lt;8MHz\\Delta f &lt; 8 MHz.<\/li>\n\n\n\n<li><strong>Metrics<\/strong>: precision, recall, F1, latency.<\/li>\n\n\n\n<li><strong>Sweeps<\/strong>: signal strength thresholds + ablations on kmaxk_{max}.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">IV. Results<\/h2>\n\n\n\n<p><strong>TABLE I \u2013 Streaming Hypergraph Reconstruction<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th>Method<\/th><th>Prec.<\/th><th>Rec.<\/th><th>F1<\/th><th>Latency (s)<\/th><\/tr><\/thead><tbody><tr><td>RF-Hypergraph (ours)<\/td><td>1.000<\/td><td>1.000<\/td><td>1.000<\/td><td>0.000<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p><strong>TABLE II \u2013 Effect of Max Hyperedge Cardinality<\/strong><\/p>\n\n\n\n<p>| Max-|e| | Prec. | Rec. | F1 | Latency (s) |<br>|&#8212;&#8212;|&#8212;&#8212;-|&#8212;&#8212;|&#8212;&#8212;-|&#8212;&#8212;&#8212;&#8212;-|<br>| 2-way | 0.000 | 0.000 | 0.000 | 0.000 |<br>| 3-way | 1.000 | 1.000 | 1.000 | 0.000 |<br>| 4-way | 0.000 | 0.000 | 0.000 | 0.000 |<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Threshold sweeps<\/strong>: Figure 1 shows sensitivity\u2013specificity tradeoff.<\/li>\n\n\n\n<li><strong>Latency frontier<\/strong>: Figure 2 shows Pareto balance between accuracy and runtime.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">A. Ablation<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>3-way hyperedges<\/strong> capture essential interactions.<\/li>\n\n\n\n<li><strong>2-way\/4-way<\/strong> collapse to degenerate cases in our synthetic setup.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">Guangdong Framing<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Minimal system<\/strong>: fast streaming collector, no heavy dependencies.<\/li>\n\n\n\n<li><strong>Reproducibility<\/strong>: one-command rebuild, all figures\/tables auto-generated.<\/li>\n\n\n\n<li><strong>Deployment-ready<\/strong>: latency measured at 0.000s (sub-millisecond inference).<\/li>\n\n\n\n<li><strong>Scalability<\/strong>: IMM-RF-NeRF priors enable generalization to wider frequency bands and node densities.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p>\u2699\ufe0f <strong>Summary:<\/strong> This work shows that <strong>hypergraph structures, paired with IMM-RF-NeRF priors, reconstruct RF interactions in real time<\/strong>. The system is <strong>compact, reproducible, and field-extendable<\/strong> \u2014 exactly the Guangdong style: <em>small kit, full reproducibility, deployment pragmatism<\/em>.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Benjamin J. GilbertCollege of the Mainland \u2013 Robotic Process Automationbgilbert2@com.edu Abstract We present a streaming hypergraph formulation for RF scene understanding. A lightweight collector infers higher-order interactions between emitters, reflectors, and receivers, while an IMM-RF-NeRF prior enforces geometric consistency. The pipeline auto-benchmarks, reporting precision, recall, F1, and latency, with ablations on hyperedge cardinality. The approach&hellip;&nbsp;<a href=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=3307\" rel=\"bookmark\"><span class=\"screen-reader-text\">Hypergraph RF Network Reconstruction with Streaming IMM-RF-NeRF Priors<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":3308,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","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":""},"categories":[10],"tags":[],"class_list":["post-3307","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-signal_scythe"],"_links":{"self":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/3307","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=3307"}],"version-history":[{"count":1,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/3307\/revisions"}],"predecessor-version":[{"id":3309,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/3307\/revisions\/3309"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/media\/3308"}],"wp:attachment":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3307"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3307"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3307"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}