{"id":1081,"date":"2022-03-31T15:07:38","date_gmt":"2022-03-31T13:07:38","guid":{"rendered":"https:\/\/www.uni.lu\/snt-fr\/events\/phd-defense-machine-learning-based-efficient-resource-scheduling-for-future-wireless-communication-networks\/"},"modified":"2022-03-31T15:07:38","modified_gmt":"2022-03-31T13:07:38","slug":"phd-defense-machine-learning-based-efficient-resource-scheduling-for-future-wireless-communication-networks","status":"publish","type":"events","link":"https:\/\/www.uni.lu\/snt-fr\/events\/phd-defense-machine-learning-based-efficient-resource-scheduling-for-future-wireless-communication-networks\/","title":{"rendered":"PhD Defense: Machine Learning-based Efficient Resource Scheduling for Future Wireless Communication Networks"},"content":{"rendered":"<section class=\"wp-block-unilux-blocks-free-section section\"><div class=\"container xl:max-w-screen-xl\"><p>We&rsquo;re happy to welcome you to the PhD defence of <strong>Yuan Yaxiong<\/strong> (SigCom group) on 13 May 2022 at 11:00.<\/p><p>The event will take place digitally on WebEx. Click\u00a0<a href=\"https:\/\/unilu.webex.com\/mw3300\/mywebex\/default.do?nomenu=true&#038;siteurl=unilu&#038;service=6&#038;rnd=0.6762238074843493&#038;main_url=https%3A%2F%2Funilu.webex.com%2Fec3300%2Feventcenter%2Fevent%2FeventAction.do%3FtheAction%3Ddetail%26%26%26EMK%3D4832534b0000000593ff051dddedcab21db2649c0fa72e16755acfb5c8854587f43be129341c4e77%26siteurl%3Dunilu%26confViewID%3D223358384615620365%26encryptTicket%3DSDJTSwAAAAVpPg51XdsEv34eFkPS_nVM0YVqB4QWjCXWei2XZB4Wag2%26\" target=\"_self\" title=\"\" rel=\"noopener\"><strong>here<\/strong><\/a>\u00a0to join.\u00a0<\/p><p>Please be informed that Webex events are not accessible on Linux OS.<\/p><p><\/p><p><strong>\u00a0Members of the defense committee<\/strong>:<\/p><ul class=\"ulux-list\"><li class=\"ulux-list-item\">Prof. Dr. Bj\u00f6rn Ottersten, University of Luxembourg, Chairman<\/li><li class=\"ulux-list-item\">Prof. Dr Lei Lei, Xi\u2019an Jiaotong University, China, Deputy Chairman<\/li><li class=\"ulux-list-item\">Prof. Dr. Symeon Chatzinotas, University of Luxembourg, Supervisor<\/li><li class=\"ulux-list-item\">Prof. Dr. Arumugam Nallanathan, Queen Mary University of London, United Kingdom, Member<\/li><li class=\"ulux-list-item\">Prof. Dr. Osvaldo Simeone, King\u2019s London College London, United Kingdom, member<\/li><\/ul><p><strong>Abstract<\/strong>:<\/p><p>The next-generation mobile communication system, e.g., 6G communication system, is envisioned to support unprecedented performance requirements such as exponentially increased data requests, heterogeneous service demands, and massive connectivity. When these challenging tasks meet the scarcity of wireless resources, ef\ufb01cient resource management becomes increasingly important. Conventionally, optimization algorithms, either optimal or suboptimal, are the major approaches in the toolbox for solving resource allocation problems. However, the ef\ufb01ciency of these iterative optimization algorithms can signi\ufb01cantly degrade when the problems become large or dif\ufb01cult, e.g., non-convex or combinatorial optimization problems. Over the past few years, machine learning (ML), as an emerging approach in the toolbox, is widely investigated to accelerate the decision-making process. Since applying ML-based approaches to solve complex resource management problems is in its early-stage study, many open issues and challenges need to be solved towards maturity and practical applications. This dissertation aims at enriching this line of studies. The motivation and objective of this dissertation lie in investigating and providing answers to the following research questions: 1) How to overcome the shortcomings of extensively adopted end-to-end learning in addressing resource management problems, and which types of features are suited to be learned if supervised learning is applied? 2) What are the limitations and bene\ufb01ts when widely-used deep reinforcement learning (DRL) approaches are used to address constrained and combinatorial optimization problems in wireless networks, and are any tailored solutions to overcome the inherent drawbacks? 3) How to make ML-based approaches timely adapt to dynamic and complex wireless environments? 4) How to enlarge the performance gains when the paradigm shifts from centralized learning to distributed learning? The main contributions are organized by the following four research works.<\/p><p>Firstly, from a supervised-learning perspective, we address common issues, e.g., unsatisfactory pre- diction performance and resultant infeasible solutions, when end-to-end learning approaches apply to solve resource scheduling problems. Based on the analysis of optimal results, we design suited-to-learn features for a class of resource scheduling problems and develop combined learning-and-optimization approaches to enable time-ef\ufb01cient and energy-ef\ufb01cient resource scheduling in multi-antenna systems. The original optimization problems are mixed-integer programming problems with high-dimensional decision vectors. The optimal solution requires exponential complexity due to the inherent dif\ufb01culties of the problems. Towards an ef\ufb01cient and competitive solution, we apply a fully-connected deep neural network (DNN) and convolutional neural network (CNN) to learn the designed features. The predicted information can effectively reduce the large search space and accelerate the optimization process. Compare to the conventional optimization and pure ML algorithms, the proposed method achieves a good trade-off between quality and complexity.<\/p><p>\u00a0<\/p><p>Secondly, we address typical issues when DRL is adopted to deal with combinatorial and non-convex scheduling problems. The original problem is to provide energy-saving solutions via resource scheduling in energy-constrained networks. An optimal algorithm and a golden section search suboptimal approach are developed to serve as of\ufb02ine benchmarks. For online operations, we propose an actor-critic-based deep stochastic online scheduling (AC-DSOS) algorithm. Compared to supervised learning, DRL is suitable for dynamic environments and capable of making decisions based on the current state without an of\ufb02ine training phase. However, for the speci\ufb01c constrained scheduling problem, conventional DRL may not be able to handle two major issues of exponentially-increased action space and infeasible actions. The proposed AC-DSOS is developed to overcome these drawbacks. In simulations, AC-DSOS is able to provide feasible solutions and save more energy compared to the conventional DRL algorithms. Compared to the of\ufb02ine benchmarks, AC-DSOS reduces the computational time from second-level to millisecond-level.<\/p><p>\u00a0<\/p><p>Thirdly, the dissertation pays more attention to the performance of the ML-based approaches in highly dynamic and complex environments. Most of the ML models are trained by the collected data or the observed environments. They may not be able to timely respond to the large variations of environments, such as dramatically \ufb02uctuated channel states or bursty data demands. In this work, we develop ML-based approaches in a time-varying satellite-terrestrial network, and address two practical issues. The \ufb01rst is how to ef\ufb01ciently schedule resources to serve the massive number of connected users, such that more data and users can be delivered\/served. The second is how to make the algorithmic solution more resilient in adapting to the time-varying wireless environments. We propose an enhanced meta-critic learning (EMCL) algorithm, combining a DRL model with a meta-learning technique, where the meta-learning can acquire meta-knowledge from different tasks and fast adapt to the new task. The results demonstrate EMCL\u2019s effectiveness and fast-response capabilities in over-loaded systems and in adapting to dynamic environments compare to previous actor-critic and meta-learning methods.<\/p><p>Fourthly, the dissertation focuses on reducing the energy consumption for federated learning (FL), in mobile edge computing. The power supply and computation capabilities are typically limited in edge devices, then energy becomes a critical issue in FL. We propose a joint energy-saving scheme (JESS) to jointly reduce computational and transmission energy. In the \ufb01rst part of JESS, we introduce sparsity and adopt sparse or binary neural networks (SNN or BNN) as the learning model to complete the local training tasks at the devices. Compared to fully-connected DNN, the computational operations can be signi\ufb01cantly reduced, thus enabling less energy consumption and fewer transmitted data to the central node. In the second part, we develop an ef\ufb01cient scheduling scheme to minimize the overall transmission energy by optimizing wireless resources and learning parameters. We develop an enhanced FL algorithm in JESS, i.e., non-smoothness and constraints &#8211; stochastic gradient descent, to handle the non-smoothness and constraints of SNN and BNN, and provide guarantees for convergence.<\/p><p>Finally, we conclude the thesis with the main \ufb01ndings and insights on future research directions.<\/p><\/div><\/section>","protected":false},"excerpt":{"rendered":"<p>We&rsquo;re happy to welcome you to the PhD defence of Yuan Yaxiong (SigCom group) on 13 May 2022 at 11:00.The event will take place digitally on WebEx. Click\u00a0here\u00a0to join.\u00a0Please be informed that Webex events are not accessible on Linux OS.\u00a0Members of the defense committee:Prof. Dr. Bj\u00f6rn Ottersten, University of Luxembourg, ChairmanProf. Dr Lei Lei, Xi\u2019an Jiaotong University, China, Deputy ChairmanProf. Dr. Symeon Chatzinotas, University of Luxembourg, SupervisorProf. Dr. Arumugam Nallanathan, Queen Mary University of London, United Kingdom, MemberProf. Dr. Osvaldo Simeone, King\u2019s London College London, United Kingdom, member<\/p>\n","protected":false},"author":0,"featured_media":1082,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"featured_image_focal_point":[],"show_featured_caption":false,"ulux_newsletter_groups":"","uluxPostTitle":"","uluxPrePostTitle":"","_trash_the_other_posts":false,"_price":"","_stock":"","_tribe_ticket_header":"","_tribe_default_ticket_provider":"","_tribe_ticket_capacity":"0","_ticket_start_date":"","_ticket_end_date":"","_tribe_ticket_show_description":"","_tribe_ticket_show_not_going":false,"_tribe_ticket_use_global_stock":"","_tribe_ticket_global_stock_level":"","_global_stock_mode":"","_global_stock_cap":"","_tribe_rsvp_for_event":"","_tribe_ticket_going_count":"","_tribe_ticket_not_going_count":"","_tribe_tickets_list":"[]","_tribe_ticket_has_attendee_info_fields":false,"event_start_date":"2022-05-13 11:00:00","event_end_date":"2022-05-13 14:00:00","event_speaker_name":"Yuan Yaxiong","event_speaker_link":"","event_is_online":false,"event_location":"E004\/005, JFK Building & Online","event_street":"","event_location_link":"","event_zip_code":"","event_city":"","event_country":"LU"},"events-topic":[],"events-type":[],"organisation":[183],"authorship":[],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v22.3 (Yoast SEO v22.3) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>PhD Defense: Machine Learning-based Efficient Resource Scheduling for Future Wireless Communication Networks - SnT - Universit\u00e9 du Luxembourg I Uni.lu<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.uni.lu\/snt-fr\/events\/phd-defense-machine-learning-based-efficient-resource-scheduling-for-future-wireless-communication-networks\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"PhD Defense: Machine Learning-based Efficient Resource Scheduling for Future Wireless Communication Networks\" \/>\n<meta property=\"og:description\" content=\"We&#039;re happy to welcome you to the PhD defence of Yuan Yaxiong (SigCom group) on 13 May 2022 at 11:00.The event will take place digitally on WebEx. 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