{"id":3491,"date":"2025-09-16T21:56:50","date_gmt":"2025-09-16T21:56:50","guid":{"rendered":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?page_id=3491"},"modified":"2025-09-16T21:56:50","modified_gmt":"2025-09-16T21:56:50","slug":"reinforcement-learning-agents-for-cognitive-radio-spectrum-denoising-2","status":"publish","type":"page","link":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/?page_id=3491","title":{"rendered":"Reinforcement Learning Agents for Cognitive Radio Spectrum Denoising"},"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\/09\/Reinforcement-Learning-Agents-for-Cognitive-Radio-Spectrum-Denoising.pdf\" type=\"application\/pdf\" style=\"width:100%;height:600px\" aria-label=\"Embed of Reinforcement Learning Agents for Cognitive Radio Spectrum Denoising.\"><\/object><a id=\"wp-block-file--media-40bc864a-b3e8-426c-978b-5111b26fa141\" href=\"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/09\/Reinforcement-Learning-Agents-for-Cognitive-Radio-Spectrum-Denoising.pdf\">Reinforcement Learning Agents for Cognitive Radio Spectrum Denoising<\/a><a href=\"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/09\/Reinforcement-Learning-Agents-for-Cognitive-Radio-Spectrum-Denoising.pdf\" class=\"wp-block-file__button wp-element-button\" download aria-describedby=\"wp-block-file--media-40bc864a-b3e8-426c-978b-5111b26fa141\">Download<\/a><\/div>\n\n\n\n<p class=\"wp-block-paragraph\">We present a reinforcement learning (RL) framework that treats RF denoising as a sequential decision-making<br>problem within a cognitive radio environment. Unlike classical<br>static or heuristic filtering, the agent learns policies that adaptively select FFT-domain actions to preserve spectrum health<br>under dynamic conditions, including low-SNR regimes and adversarial jammers. Reward shaping is based on physical-layer<br>metrics\u2014time-difference-of-arrival (TDoA) residual error and<br>correlation entropy\u2014bridging ML objectives with RF system<br>performance. Through simulation, we show that RL agents<br>converge rapidly, achieving up to 35\u201345% reductions in TDoA<br>residuals and consistently outperforming hand-tuned filters. Beyond denoising, we argue the same policy framework generalizes<br>to broader cognitive radio functions such as channel selection,<br>adaptive beamforming, and interference mitigation. This work<br>highlights reinforcement learning as a viable control primitive<br>for autonomous RF sensing and spectrum management.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present a reinforcement learning (RL) framework that treats RF denoising as a sequential decision-makingproblem within a cognitive radio environment. Unlike classicalstatic or heuristic filtering, the agent learns policies that adaptively select FFT-domain actions to preserve spectrum healthunder dynamic conditions, including low-SNR regimes and adversarial jammers. Reward shaping is based on physical-layermetrics\u2014time-difference-of-arrival (TDoA) residual error&hellip;&nbsp;<\/p>\n","protected":false},"author":2,"featured_media":2280,"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-3491","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\/3491","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=3491"}],"version-history":[{"count":0,"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/pages\/3491\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/media\/2280"}],"wp:attachment":[{"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3491"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}