{"id":405,"date":"2021-11-10T06:34:07","date_gmt":"2021-11-10T13:34:07","guid":{"rendered":"https:\/\/faculty.engineering.asu.edu\/michelusi\/?p=405"},"modified":"2021-11-10T06:34:07","modified_gmt":"2021-11-10T13:34:07","slug":"new-tccn-paper-accepted","status":"publish","type":"post","link":"https:\/\/faculty.engineering.asu.edu\/michelusi\/2021\/11\/new-tccn-paper-accepted\/","title":{"rendered":"New TCCN paper accepted!"},"content":{"rendered":"\n<p>Our paper &#8220;<em><em>Learning-based Spectrum Sensing in Cognitive Radio Networks via Approximate POMDPs<\/em><\/em>&#8221; has been accepted for publication at the IEEE Transactions on Cognitive Communications and Networking!<\/p>\n\n\n\n<p>Co-authored by Bharath Keshavamurthy and myself.<\/p>\n\n\n\n<p>A novel LEarning-based Spectrum Sensing and Access (LESSA) framework is proposed, wherein a cognitive radio (CR) learns a time-frequency correlation model underlying spectrum occupancy of licensed users (LUs) in a radio ecosystem; concurrently, it devises an approximately optimal spectrum sensing and access policy under sensing constraints. A Baum-Welch algorithm is proposed to learn a parametric Markov transition model of LU spectrum occupancy based on noisy spectrum measurements. Spectrum sensing and access are cast as a Partially-Observable Markov Decision Process, approximately optimized via randomized point-based value iteration. Fragmentation, Hamming-distance state filters and Monte-Carlo methods are proposed to alleviate the inherent computational complexity, and a weighted reward metric to regulate the trade-off between CR throughput and LU interference. Numerical evaluations demonstrate that LESSA performs within 5 percent of a genie-aided upper bound with foreknowledge of LU spectrum occupancy, and outperforms state-of-the-art algorithms across the entire trade-off region: 71 percent over correlation-based clustering, 26 percent over Neyman-Pearson detection, 6 percent over the Viterbi algorithm, and 9 percent over an adaptive Deep Q-Network. LESSA is then extended to a distributed Multi-Agent setting (MA-LESSA), by proposing novel neighbor discovery and channel access rank allocation. MA-LESSA improves CR throughput by 43 percent over cooperative TD-SARSA, 84 percent over cooperative greedy distributed learning, and 3x over non-cooperative learning via g-statistics and ACKs. Finally, MA-LESSA is implemented on the DARPA SC2 platform, manifesting superior performance over competitors in a real-world TDWR-UNII WLAN emulation; its implementation feasibility is further validated on a testbed of ESP32 radios, exhibiting 96 percent success probability.<\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/abs\/2107.07049\">https:\/\/arxiv.org\/abs\/2107.07049<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Our paper &#8220;Learning-based Spectrum Sensing in Cognitive Radio Networks via Approximate POMDPs&#8221; has been accepted for publication at the IEEE Transactions on Cognitive Communications and Networking! Co-authored by Bharath Keshavamurthy and myself. A novel LEarning-based [&hellip;]<\/p>\n","protected":false},"author":112,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-405","post","type-post","status-publish","format-standard","hentry","category-news"],"_links":{"self":[{"href":"https:\/\/faculty.engineering.asu.edu\/michelusi\/wp-json\/wp\/v2\/posts\/405","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/faculty.engineering.asu.edu\/michelusi\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/faculty.engineering.asu.edu\/michelusi\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/faculty.engineering.asu.edu\/michelusi\/wp-json\/wp\/v2\/users\/112"}],"replies":[{"embeddable":true,"href":"https:\/\/faculty.engineering.asu.edu\/michelusi\/wp-json\/wp\/v2\/comments?post=405"}],"version-history":[{"count":0,"href":"https:\/\/faculty.engineering.asu.edu\/michelusi\/wp-json\/wp\/v2\/posts\/405\/revisions"}],"wp:attachment":[{"href":"https:\/\/faculty.engineering.asu.edu\/michelusi\/wp-json\/wp\/v2\/media?parent=405"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/faculty.engineering.asu.edu\/michelusi\/wp-json\/wp\/v2\/categories?post=405"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/faculty.engineering.asu.edu\/michelusi\/wp-json\/wp\/v2\/tags?post=405"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}