BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:US/Pacific BEGIN:DAYLIGHT DTSTART:20210314T030000 TZOFFSETFROM:-0800 TZOFFSETTO:-0700 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:PDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20211107T010000 TZOFFSETFROM:-0700 TZOFFSETTO:-0800 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:PST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20210920T161444Z UID:7F71C308-FB18-49A8-9DB4-A74DD4DDB9F9 DTSTART;TZID=US/Pacific:20210917T080000 DTEND;TZID=US/Pacific:20210917T120000 DESCRIPTION:Welcome to the [SPS Seasonal School](https://signalprocessingso ciety.org/blog/2021-ieee-sps-seasonal-school-signal-processing-and-communi cation-systems-5g) on Signal Processing and Communication Systems for 5G. The jointly\, internationally\, organized by IEEE Vizag Bay Section [Commu nications Society & SPS](https://r10.ieee.org/vizagbay/) Joint Chapter\, V izag\, India\, IEEE Long Island Section ([SPS](http://www.ieee.li/sp)) Cha pter\, New York\, USA\, and IEEE [Finland SP/CAS](https://site.ieee.org/fi nland/) Chapter\, Finland presents the following technical lecture:\n\nAbs tract:\n\nWith the emergence of fifth-generation (5G) networks\, there has been a shift in the research focus towards exploring new technologies for the next-generation communication systems\, sixth-generation (6G). The po tential target expectations from 6G are to achieve even higher data rates\ , further reduction in latency and ultra massive machine type connection d ensity compared to 5G. In this search for new technologies\, there has bee n a significant interest in applying machine learning and artificial intel ligence to communication systems.\n\nIn this lecture\, we motivate the AI/ ML for wireless communications by starting with simple machine learning ap plications and similarities to communication use cases. During the course\ , you will learn about various wireless communication system blocks and th e application of ML to them. We cover the applications at the physical and higher layers at both base station and user equipment of the wireless com munication system. Later we briefly discuss the possibility of end-to-end conventional communication system replaced by an ML trained communication system. We will observe that ML/AI does not give benefits all the time.\n\ nIn the later part of this lecture\, we cover model-based and model-free s ystems and how they evolve with continuous improvements. We will further d iscuss and exercise a step-by-step procedure on designing an ML applicatio n for a wireless communication system with the constraints of complexity\, timing\, performance\, and training requirements.\n\nBy the end of this l ecture\, you will have learned about various blocks of communication syste ms that ML trained systems can replace and where to apply and where not to apply ML/AI in wireless communication systems.\n\nSpeaker(s): Mr. Ashok K umar Reddy Chavva \, \n\nVirtual: https://events.vtools.ieee.org/m/268696 LOCATION:Virtual: https://events.vtools.ieee.org/m/268696 ORGANIZER:signal@ieee.li SEQUENCE:8 SUMMARY:Machine Learning for Next Generation Wireless Communication Systems 5G/6G URL;VALUE=URI:https://events.vtools.ieee.org/m/268696 X-ALT-DESC:Description: <br /><p>Welcome to the <a href="https://signalproc essingsociety.org/blog/2021-ieee-sps-seasonal-school-signal-processing-and -communication-systems-5g">SPS Seasonal School</a> on Signal Processing an d Communication Systems for 5G. The jointly\, internationally\, organized by IEEE Vizag Bay Section <a href="https://r10.ieee.org/vizagbay/">Communi cations Society &amp\; SPS</a> Joint Chapter\, Vizag\, India\, IEEE Long I sland Section&nbsp\;(<a href="http://www.ieee.li/sp">SPS</a>)&nbsp\;Chapte r\, New York\, USA\, and IEEE <a href="https://site.ieee.org/finland/">Fin land SP/CAS</a> Chapter\, Finland presents the following technical lecture :</p>\n<p>Abstract:</p>\n<p>With the emergence of fifth-generation (5G) ne tworks\, there has been a shift in the research focus towards exploring ne w technologies for the next-generation communication systems\, sixth-gener ation (6G). The potential target expectations from 6G are to achieve even higher data rates\, further reduction in latency and ultra massive machine type connection density compared to 5G. In this search for new technologi es\, there has been a significant interest in applying machine learning an d artificial intelligence to communication systems.</p>\n<p>In this lectur e\, we motivate the AI/ML for wireless communications by starting with sim ple machine learning applications and similarities to communication use ca ses. During the course\, you will learn about various wireless communicati on system blocks and the application of ML to them. We cover the applicati ons at the physical and higher layers at both base station and user equipm ent of the wireless communication system. Later we briefly discuss the pos sibility of end-to-end conventional communication system replaced by an ML trained communication system. We will observe that ML/AI does not give be nefits all the time.</p>\n<p>In the later part of this lecture\, we cover model-based and model-free systems and how they evolve with continuous imp rovements. We will further discuss and exercise a step-by-step procedure o n designing an ML application for a wireless communication system with the constraints of complexity\, timing\, performance\, and training requireme nts.</p>\n<p>By the end of this lecture\, you will have learned about vari ous blocks of communication systems that ML trained systems can replace an d where to apply and where not to apply ML/AI in wireless communication sy stems.</p> END:VEVENT END:VCALENDAR