[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123879-en":3,"doc-seo-123879-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123879,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Space-Time-Frequency Machine Learning for Improved 4G/5G Energy Detection","Energy detection for spectrum sensing is used to detect 4G LTE transmissions so that 5G New Radio users can access spectrum opportunistically while limiting interference to incumbent primary users. The work improves standard energy-detection quality by applying machine learning using space, time, and frequency information. Simulations evaluate k-Nearest Neighbors and Random Forest, showing a significant increase in detection probability. The study targets enhanced sensing of LTE resource blocks to support cognitive 4G/5G communication and better utilization of unused orthogonal resources.","INTL JOURNAL OF ELECTRONICS AND TELECOMMUNICATIONS, 2020, VOL. 66, NO. 1, PP. 217–223  \nManuscript received November 15, 2019; revised January, 2020 . DOI: 10.24425/ijet.2020.131866  \nSpace-Time-Frequency Machine Learning for Improved 4G/5G Energy Detection  \nMałgorzata Wasilewska, and Hanna Bogucka  \nAbstract—In this paper, the future Fifth Generation (5G New Radio) radio communication system has been considered, coexisting and sharing the spectrum with the incumbent Fourth Generation (4G) Long-Term Evolution (LTE) system. The 4G signal presence is detected in order to allow for opportunistic and dynamic spectrum access of 5G users. This detection is based on known sensing methods, such as energy detection, however, it uses machine learning in the domains of space, time and frequency for sensing quality improvement. Simulation results for the considered methods: k-Nearest Neighbors and Random Forest show that these methods significantly improves the detection probability.  \nKeywords—spectrum sensing, cognitive radio, machine learning, energy detection, 4G, LTE, 5G, k-nearest neighbors, random forest  \nI. INTRODUCTION  \nFREQUENCY spectrum is a scarce resource of high  \nvalue for contemporary and future radio communication networks with ever-growing traffic for ubiquitous Internet access. Nowadays, effective (possibly broadband) spectrum access faces a major problem of limited resources, on one hand, and inefficient usage of the ones already licensed, on the other. The basic issue is how to maintain guaranteed quality of services while a number of spectrum users and the generated traffic are exponentially growing [1] . As a solution to this problem, the idea of cognitive radio was proposed. Cognitive radio is a concept of the intelligent radio network and devices that acquire awareness on their radio environment, and dynamically adopt their communication parameters to the available transmission opportunities (spacial, spectral and timing conditions) [2] . Moreover, cognitive radio is expected to learn from its past actions by assessing the quality of the decisions taken. A cognitive-radio user (called secondary user – SU) is a radio device, that is able to determine the current state of the spectrum occupancy, and to transmit and receive the data, keeping the interference generated to the licensed (incumbent) systems (called primary users – PUs) at the acceptable level. For this spectral awareness, spectrum sensing (SS) techniques are essential. SS allows SU to decide whether the spectrum is occupied or not. If the spectrum is idle, SU can transmit without disturbing PU. On the other  \nThis work was supported by the DAINA project no. 2017/27/L/ST7/03166 ”Cognitive Engine for Radio environmenT Awareness In Networks of the future” (CERTAIN) funded by the National Science Centre, Poland.  \nM. Wasilewska and H. Bogucka are with Department of Wireless Communications, Poznan University of Technology, Poznan, Poland (e-mail: [malgorzata.wasilewska@doctorate.put.poznan.pl](malgorzata.wasilewska@doctorate.put.poznan.pl), [hanna.bogucka@put.poznan.pl](hanna.bogucka@put.poznan.pl)).  \nhand, if PU is active (transmitting), SU should detect this transmission, and wait until PU releases the spectrum.  \nCommon detection methods include, the energy detection method (ED) [3], matched filtering [4], cyclostationarity detection [5], waveform based sensing [6], wavelet transform based detection [7] and other methods. In this paper, the focus is put on ED method. ED is very simple, and is based on the received signal energy calculation. It does not require any knowledge on signal's properties, however, the noise-level cognition is essential [8] . The noise-power level respective to the detected signal-power level, as well as the noise power estimation error significantly impact the ED-based SS performance.  \nThus, the goal of this paper is to examine, how the cognitive-radio introduced intelligence, and in particular learning ability, can increase the pr","cbCair9RhP5A4sbJ","https://ap.wps.com/l/cbCair9RhP5A4sbJ","pdf",711005,1,7,"English","en",105,"# Introduction\n## Cognitive radio and spectrum sensing basics\n## Energy detection and noise-level cognition\n# Proposed approach\n## Space-time-frequency machine learning for ED improvement\n## Targeting LTE resource block sensing\n# Methods and evaluation\n## k-Nearest Neighbors (kNN)\n## Random Forest (RF)\n## Detection across interrelated LTE resource blocks","[{\"question\":\"What problem does the paper address in 4G/5G spectrum sharing?\",\"answer\":\"It addresses detecting whether 4G LTE signals are present so 5G users can access spectrum dynamically while keeping interference to primary users at an acceptable level.\"},{\"question\":\"How is machine learning incorporated into energy detection?\",\"answer\":\"Machine learning is applied to enhance energy-detection quality by using space, time, and frequency domain information to support more reliable sensing decisions.\"},{\"question\":\"Which machine learning methods are evaluated and what is the result?\",\"answer\":\"The paper evaluates k-Nearest Neighbors and Random Forest, and simulation results show a significant improvement in detection probability.\"}]","Space-Time-Frequency Machine Learning for Improved 4G/5G Energy Detection | 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problem does the paper address in 4G/5G spectrum sharing?","Question",{"text":75,"@type":76},"It addresses detecting whether 4G LTE signals are present so 5G users can access spectrum dynamically while keeping interference to primary users at an acceptable level.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is machine learning incorporated into energy detection?",{"text":80,"@type":76},"Machine learning is applied to enhance energy-detection quality by using space, time, and frequency domain information to support more reliable sensing decisions.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning methods are evaluated and what is the result?",{"text":84,"@type":76},"The paper evaluates k-Nearest Neighbors and Random Forest, and simulation results show a significant improvement in detection 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