{"id":null,"code":"521393S","name":{"valueFi":"Tilastollinen tietoliikenneteoria","valueEn":"Statistical Communication Theory","valueSv":""},"credits":7.0,"minCredits":7,"maxCredits":7,"tags":[],"createdAt":1790534916921,"contentList":[{"title":{"valueFi":"Osaamistavoitteet","valueEn":"Learning outcomes","valueSv":""},"content":{"valueFi":"After completing this course  \r\n- Student is conversant with commonly used estimation and detection techniques: receiver design and algorithms.  \r\n- Student is able to evaluate the performance of a wireless receiver by analytical or simulation methods.  \r\n- Student is able to read and understand peer reviewed publications in relevant topics.  \r\n- Student is familiar with the novel applications in physical layer and new directions including 5G and beyond  \r\n- Student can observe and explain the performance of these technologies with variable system and channel parameters through the course laboratory exercise – Vienna simulator.  \r\n  \r\nObjective is to develop a theoretical understanding of statistical communication theory.\r\nGeneric skills to be achieved:\r\nis able to apply analytical and critical thinking skills in a manner appropriate to their discipline, considering the interfaces between fields and new knowledge.\r\nis able to apply creative thinking and problem solving in their work to develop new knowledge and new procedures.","valueEn":"After completing this course  \r\n- Student is conversant with commonly used estimation and detection techniques: receiver design and algorithms.  \r\n- Student is able to evaluate the performance of a wireless receiver by analytical or simulation methods.  \r\n- Student is able to read and understand peer reviewed publications in relevant topics.  \r\n- Student is familiar with the novel applications in physical layer and new directions including 5G and beyond  \r\nGeneric skills to be achieved:\r\nis able to apply analytical and critical thinking skills in a manner appropriate to their discipline, considering the interfaces between fields and new knowledge.\r\nis able to apply creative thinking and problem solving in their work to develop new knowledge and new procedures.\r\n- Student can observe and explain the performance of these technologies with variable system and channel parameters through the course laboratory exercise – Vienna simulator.  \r\n  \r\nObjective is to develop a theoretical understanding of statistical communication theory.","valueSv":""}},{"title":{"valueFi":"Sisältö","valueEn":"Content","valueSv":""},"content":{"valueFi":"Detection of Signals – general Gaussian, ROC curves – performance, Estimation, Representation of Random Processes: Homogeneous Integral Equations and Eigenfunctions, Signals with unwanted parameters, Multiple channels, Mobility in Detection, Correlation functions: Bello functions – derivations, Waveforms for mm-wave and higher frequencies, Application of learning methods in Physical layer","valueEn":"Detection of Signals – general Gaussian, ROC curves – performance, Estimation, Representation of Random Processes: Homogeneous Integral Equations and Eigenfunctions, Signals with unwanted parameters, Multiple channels, Mobility in Detection, Correlation functions: Bello functions – derivations, Waveforms for mm-wave and higher frequencies, Application of learning methods in Physical layer","valueSv":""}},{"title":{"valueFi":"Suoritustavat","valueEn":"Study Methods","valueSv":""},"content":{"valueFi":"The course is passed two mid-term exams or with final exam. The final grade is a weighted sum of exam (50%), home assignments (45%), and lab exercise (5%).","valueEn":"The course is passed two mid-term exams or with final exam. The final grade is a weighted sum of exam (50%), home assignments (45%), and lab exercise (5%).","valueSv":""}},{"title":{"valueFi":"Toteutustavat","valueEn":"Teaching Methods","valueSv":""},"content":{"valueFi":"Face-to-face teaching  \r\nLectures and exercises 70 h and compulsory home assignments and lab 50 h","valueEn":"Face-to-face teaching  \r\nLectures and exercises 70 h and compulsory home assignments and lab 50 h","valueSv":""}},{"title":{"valueFi":"Oppimateriaalit","valueEn":"Learning material","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Lisätiedot","valueEn":"Further information","valueSv":""},"content":{"valueFi":"Ajoitus  \r\nFall, period 1-2, every other year (2022, 2024...)  \r\n  \r\nKohderyhmä  \r\n2nd year M.Sc. and WCE students","valueEn":"Timing  \r\nFall, period 1-2, every other year (2022, 2024...)  \r\n  \r\nTarget group  \r\n2nd year M.Sc. and WCE students","valueSv":""}},{"title":{"valueFi":"Kurssikirjallisuus","valueEn":"Literature","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Esitietovaatimukset","valueEn":"Qualifications","valueSv":""},"content":{"valueFi":"Esitietovaatimukset  \r\nSignals and Systems, Probability, Random Variables and Processes, Linear Algebra  \r\n  \r\nYhteydet muihin opintojaksoihin  \r\nWireless Communications I, Statistical Signal Processing I","valueEn":"Prerequisites and co-requisites  \r\nSignals and Systems, Probability, Random Variables and Processes, Linear Algebra  \r\n  \r\nRecommended optional programme components  \r\nWireless Communications I, Statistical Signal Processing I","valueSv":""}},{"title":{"valueFi":"Arviointiasteikko","valueEn":"Assessment scale","valueSv":""},"content":{"valueFi":"1-5/HYL","valueEn":"1-5/FAIL","valueSv":"1-5/FAIL"}},{"title":{"valueFi":"Arviointikriteerit","valueEn":"Assessment criteria","valueSv":""},"content":{"valueFi":"The course unit utilizes a numerical grading scale 1-5.","valueEn":"The course unit utilizes a numerical grading scale 1-5.","valueSv":""}},{"title":{"valueFi":"Arviointikriteerit 2","valueEn":"Evaluation criteria 2","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Arviointikriteerit 3","valueEn":"Evaluation criteria 3","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Arviointikriteerit 4","valueEn":"Evaluation criteria 4","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Pääasiallinen opetuskieli","valueEn":"Primary Teaching Language","valueSv":""},"content":{"valueFi":"englanti","valueEn":"English","valueSv":"engelska"}},{"title":{"valueFi":"Taso","valueEn":"Level","valueSv":""},"content":{"valueFi":"Syventävät opinnot","valueEn":"Advanced Studies","valueSv":"Syventävät opinnot"}},{"title":{"valueFi":"Oppiaine","valueEn":"Subject","valueSv":""},"content":{"valueFi":"Tietoliikennetekniikka","valueEn":"Communications Engineering","valueSv":""}},{"title":{"valueFi":"Vastuuhenkilöt","valueEn":"Person in charge","valueSv":""},"content":{"valueFi":"Premanandana Rajatheva","valueEn":"Premanandana Rajatheva","valueSv":"Premanandana Rajatheva"}},{"title":{"valueFi":"Luokittelu","valueEn":"Classification","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Linkit","valueEn":"Links","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}}]}