{"id":55497,"date":"2022-12-01T11:47:14","date_gmt":"2022-12-01T09:47:14","guid":{"rendered":"https:\/\/www.iemn.fr\/?p=55497"},"modified":"2022-12-01T11:47:14","modified_gmt":"2022-12-01T09:47:14","slug":"these-tesfay-a-gerer-les-interferences-des-utilisateurs-dans-les-reseaux-lora","status":"publish","type":"post","link":"https:\/\/www.iemn.fr\/en\/these\/these-2021\/these-tesfay-a-gerer-les-interferences-des-utilisateurs-dans-les-reseaux-lora.html","title":{"rendered":"THESE : TESFAY A. \u2013 G\u00e9rer les interf\u00e9rences des utilisateurs dans les r\u00e9seaux LoRa"},"content":{"rendered":"<div id='layer_slider_1'  class='avia-layerslider main_color avia-shadow  avia-builder-el-0  el_before_av_heading  avia-builder-el-first  container_wrap sidebar_right'  style='height: 261px;'  ><div id=\"layerslider_58_ecqesmbayr65\" data-ls-slug=\"homepageslider\" class=\"ls-wp-container fitvidsignore ls-selectable\" style=\"width:1140px;height:260px;margin:0 auto;margin-bottom: 0px;\"><div class=\"ls-slide\" data-ls=\"duration:6000;transition2d:5;\"><img loading=\"lazy\" decoding=\"async\" width=\"2600\" height=\"270\" src=\"https:\/\/www.iemn.fr\/wp-content\/uploads\/2019\/01\/sliders_news1.jpg\" class=\"ls-bg\" alt=\"\" srcset=\"https:\/\/www.iemn.fr\/wp-content\/uploads\/2019\/01\/sliders_news1.jpg 2600w, https:\/\/www.iemn.fr\/wp-content\/uploads\/2019\/01\/sliders_news1-300x31.jpg 300w, https:\/\/www.iemn.fr\/wp-content\/uploads\/2019\/01\/sliders_news1-768x80.jpg 768w, https:\/\/www.iemn.fr\/wp-content\/uploads\/2019\/01\/sliders_news1-1030x107.jpg 1030w, https:\/\/www.iemn.fr\/wp-content\/uploads\/2019\/01\/sliders_news1-1500x156.jpg 1500w, https:\/\/www.iemn.fr\/wp-content\/uploads\/2019\/01\/sliders_news1-705x73.jpg 705w\" sizes=\"auto, (max-width: 2600px) 100vw, 2600px\" \/><ls-layer style=\"font-size:14px;text-align:left;font-style:normal;text-decoration:none;text-transform:none;font-weight:700;letter-spacing:0px;border-style:solid;border-color:#000;background-position:0% 0%;background-repeat:no-repeat;width:180px;height:30px;left:0px;top:231px;line-height:32px;color:#ffffff;border-radius:6px 6px 6px 6px;padding-left:50px;background-color:rgba(0, 0, 0, 0.57);\" class=\"ls-l ls-ib-icon ls-text-layer\" data-ls=\"minfontsize:0;minmobilefontsize:0;\"><i class=\"fa fa-quote-right\" style=\"color:#ffffff;margin-right:0.8em;font-size:1em;transform:translateY( -0.125em );\"><\/i>ACTUALITES<\/ls-layer><\/div><\/div><\/div><div id='after_layer_slider_1'  class='main_color av_default_container_wrap container_wrap sidebar_right'  ><div class='container av-section-cont-open' ><div class='template-page content  av-content-small alpha units'><div class='post-entry post-entry-type-page post-entry-55497'><div class='entry-content-wrapper clearfix'>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-lb4w5w3u-30ece17517ad78d37648d8531b080936\">\n#top .av-special-heading.av-lb4w5w3u-30ece17517ad78d37648d8531b080936{\nmargin:0 0 10px 0;\npadding-bottom:4px;\n}\nbody .av-special-heading.av-lb4w5w3u-30ece17517ad78d37648d8531b080936 .av-special-heading-tag .heading-char{\nfont-size:25px;\n}\n.av-special-heading.av-lb4w5w3u-30ece17517ad78d37648d8531b080936 .av-subheading{\nfont-size:15px;\n}\n<\/style>\n<div  class='av-special-heading av-lb4w5w3u-30ece17517ad78d37648d8531b080936 av-special-heading-h2  avia-builder-el-1  el_after_av_layerslider  el_before_av_hr  avia-builder-el-first'><h2 class='av-special-heading-tag'  itemprop=\"headline\"  >THESE : TESFAY A. \u2013 G\u00e9rer les interf\u00e9rences des utilisateurs dans les r\u00e9seaux LoRa <\/h2><div class=\"special-heading-border\"><div class=\"special-heading-inner-border\"><\/div><\/div><\/div>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-18u73nj-dad6a947580930e400fc42ba200e80f1\">\n#top .hr.av-18u73nj-dad6a947580930e400fc42ba200e80f1{\nmargin-top:5px;\nmargin-bottom:5px;\n}\n.hr.av-18u73nj-dad6a947580930e400fc42ba200e80f1 .hr-inner{\nwidth:100%;\n}\n<\/style>\n<div  class='hr av-18u73nj-dad6a947580930e400fc42ba200e80f1 hr-custom  avia-builder-el-2  el_after_av_heading  el_before_av_textblock  hr-left hr-icon-no'><span class='hr-inner inner-border-av-border-thin'><span class=\"hr-inner-style\"><\/span><\/span><\/div>\n<section  class='av_textblock_section av-jriy64i8-2f4600354c0449b610997916bbd9b6bc'   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" >\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-13ewzjw-68e036126b913e5028f77311dc66b825\">\n.av_font_icon.av-13ewzjw-68e036126b913e5028f77311dc66b825{\ncolor:#bfbfbf;\nborder-color:#bfbfbf;\n}\n.av_font_icon.av-13ewzjw-68e036126b913e5028f77311dc66b825 .av-icon-char{\nfont-size:60px;\nline-height:60px;\n}\n<\/style>\n<span  class='av_font_icon av-13ewzjw-68e036126b913e5028f77311dc66b825 avia_animate_when_visible av-icon-style- avia-icon-pos-left avia-icon-animate'><span class='av-icon-char' aria-hidden='true' data-av_icon='\ue8c9' data-av_iconfont='entypo-fontello' ><\/span><\/span>\n<p><strong>A. TESFAY<br \/>\n<\/strong><\/p>\n<p>Soutenance : <strong>1 October 2021<br \/>\n<\/strong><span class=\"titre\">Th\u00e8se de doctorat en Electronique, micro\u00e9lectronique, nano\u00e9lectronique et micro-ondes, Universit\u00e9 de Lille, ENGSYS Sciences de l\u2019ing\u00e9nierie et des syst\u00e8mes<\/span><span class=\"annee_parution\">.<\/span><\/p>\n<\/div><\/section>\n<section  class='av_textblock_section av-jtefqx33-628129dba2299b2ecd65ebfc92eac29d'   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><div  class='hr av-kjh3zw-4dff888f744b728a1aca9b3a0971493a hr-default  avia-builder-el-6  avia-builder-el-no-sibling'><span class='hr-inner'><span class=\"hr-inner-style\"><\/span><\/span><\/div>\n<h5>Summary:<\/h5>\n<p>Internet of Things (IoT) technology has become ubiquitous in many applications and its use is expanding very rapidly. However, the expansion of the IoT faces a significant scalability challenge, i.e. the very dense deployment of communicating devices is currently limited. This thesis focuses on providing solutions to the problems associated with the scalability of LoRa-type networks in both directions of communication, uplink and downlink. In long-range networks, such as LoRa, the downlink is critical as it limits the number of acknowledgements that can be sent, and therefore reliability. It also limits the ability to update devices, which could be critical when they are deployed for decades. To overcome these problems, we propose a solution, inspired by non-orthogonal multiple access (NOMA) techniques, to increase by at least an order of magnitude the number of devices that can be addressed. While the approach differentiates devices by the power allocated to them, it differs from the vast majority of previous work on the NOMA power domain in that it does not require interference cancellation. Instead, it takes advantage of the chirp spread spectrum of the modulation scheme, where, at the end of the decoding phase, the information carried by a symbol is found at the position of a peak in the Fourier domain. In most cases, the information from different users results in different peak positions, creating no interference. In this sense, we are approaching avoidance schemes such as time or frequency hopping, but without using code. This thesis proposes a new solution based on NOMA in the power domain that does not suffer from the limitations induced by interference cancellation residuals. The proposed scheme, including preamble detection and channel estimation, is presented and evaluated by simulations. We show that our scheme increases the number of connected devices by an order of magnitude compared to the current system, while maintaining full compatibility with the LoRa physical layer standard. On the uplink side, we present a new receiver capable of demodulating multiple users transmitted simultaneously on the same frequency channel with the same spreading factor. From the point of view of non-orthogonal multiple access, it is based on the power domain and uses serial interference cancellation. Simulation results show that the receiver allows a significant increase in the number of devices connected in the network. Finally, this thesis deals with the detection of uplink signals in a LoRa network using an approach based on deep learning. Two strategies are proposed: regression for bit detection based on a deep feedforward neural network and classification for symbol detection based on a convolutional neural network. These receivers can decode the signals of a selected user when several users are transmitting simultaneously on the same frequency band with the same spreading factor. Simulation results show that both receivers outperform conventional LoRa in the presence of interference. In addition, the results show that the approach introduced is relevant for addressing the issue of scalability.<\/p>\n<h5>Abstract:<\/h5>\n<p>Internet of Things (IoT) technology has become ubiquitous in many applications and its use is growing very rapidly. However, the expansion of the IoT faces a significant scalability challenge, i.e., the very dense deployment of communicating devices is currently limited. This thesis focuses on providing solutions to the problems associated with the scalability of LoRa-like networks in both the uplink and downlink directions of communication. In long-range networks, such as LoRa, the downlink is critical because it limits the number of acknowledgements that can be sent, and therefore, the reliability. It also limits the ability to update devices, which could be critical when they are deployed for decades. To overcome these problems, we propose a solution, inspired by non-orthogonal multiple access (NOMA) techniques, to increase the number of addressable devices by at least an order of magnitude. While the approach differentiates devices by the power allocated to them, it differs from the vast majority of previous work on the NOMA power domain because it does not require interference cancellation. Instead, it takes advantage of the spectral spread of the modulation scheme (chirp spread spectrum), where, at the end of the decoding phase, the information carried by a symbol is found at the position of a peak in the Fourier domain. In most cases, information from different users results in different peak positions, creating no interference. In this sense, we come close to avoidance schemes such as time or frequency hopping, but without using any code. This thesis proposes a new solution based on NOMA in the power domain that does not suffer from the limitations induced by interference cancellation residues. The proposed scheme, including preamble detection and channel estimation, is presented and evaluated by simulations. We show that our scheme increases the number of connected devices by an order of magnitude over the current system, while maintaining full compatibility with the LoRa physical layer standard. Regarding the uplink, we present a new receiver capable of demodulating multiple users transmitted simultaneously on the same frequency channel with the same spreading factor. From the non-orthogonal multiple access point of view, it is based on the power domain and uses serial interference cancellation. Simulation results show that the receiver allows a significant increase in the number of devices connected in the network. Finally, this thesis deals with the detection of signals, in uplink, of a LoRa network through an approach based on deep learning. Two strategies are proposed: regression for bit detection based on a deep feedforward neural network and classification for symbol detection based on a convolutional neural network. These receivers can decode the signals of a selected user when multiple users transmit simultaneously on the same frequency band with the same spreading factor. Simulation results show that both receivers outperform conventional LoRa in the presence of interference. Furthermore, the results show that the introduced approach is relevant to address the scalability issue.<\/p>\n<\/div><\/section>","protected":false},"excerpt":{"rendered":"","protected":false},"author":20,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[317],"tags":[],"class_list":["post-55497","post","type-post","status-publish","format-standard","hentry","category-these-2021"],"_links":{"self":[{"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/posts\/55497","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/users\/20"}],"replies":[{"embeddable":true,"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/comments?post=55497"}],"version-history":[{"count":0,"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/posts\/55497\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/media?parent=55497"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/categories?post=55497"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/tags?post=55497"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}