BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Europe/Amsterdam BEGIN:DAYLIGHT DTSTART:20240331T030000 TZOFFSETFROM:+0100 TZOFFSETTO:+0200 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:CEST END:DAYLIGHT BEGIN:STANDARD DTSTART:20241027T020000 TZOFFSETFROM:+0200 TZOFFSETTO:+0100 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:CET END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20240531T153439Z UID:0C90B134-71CA-42DC-BA99-29B3679928AB DTSTART;TZID=Europe/Amsterdam:20240531T150000 DTEND;TZID=Europe/Amsterdam:20240531T163000 DESCRIPTION:The AES/GRS and VT/COM joint chapters of the IEEE Benelux secti on are pleased to invite you to this IEEE Distinguished Lecture by Dr Kuma r Vijay Mishra on the topic of "Leveraging Learning and Sparsity for Joint Radar-Communications".\n\nAbstract: Recent interest in joint radar-commun ications (JRC) has led to the design of novel signal processing techniques to recover information from an overlaid radar-communications signal as we ll as transmit a common signal for both systems. In this talk\, we focus o n two important tools for the design and signal processing of JRC systems: learning and sparsity. The interest in learning-based JRC is driven large ly by the need to solve difficult nonconvex optimization problems inherent in a JRC design as well as to address the highly dynamic channel environm ents. Toward fully realizing the coexistence/co-design of both radar and c ommunications\, the optimization of resources for both sensing and wireles s communications modalities is crucial. But the optimization-based approac hes suffer from high computational complexity and their performance strong ly relies on factors such as perfect channel conditions\, specific constra ints\, and mobility. In this context\, learning techniques provide robust performance at an upfront training cost. We discuss applying learning to v arious JRC aspects including channel estimation\, antenna selection\, reso urce allocation\, and wideband beamforming. The second half of the talk fo cuses on exploiting sparsity in a general spectral coexistence scenario\, wherein the channels and transmit signals of both radar and communications systems are unknown at the receiver. In this dual-blind deconvolution (DB D) problem\, a common receiver admits a multi-carrier wireless communicati ons signal that is overlaid with the radar signal reflected off multiple t argets. The communications and radar channels are represented by continuou s-valued range-time and Doppler velocities of multiple transmission paths and multiple targets. We exploit the sparsity of both channels to solve th e highly ill-posed DBD problem by casting it into a sum of multivariate at omic norms (SoMAN) minimization. Toward the end of the talk\, we focus on highlighting emerging JRC scenarios\, particularly at mm-Wave and THz freq uencies\, vehicular applications\, distributed radar-communications networ ks\, intelligent surfaces\, and aerial channels.\n\nRoom: Snijderszaal\, B ldg: 36\, MEkelweg 4\, Delft\, Zuid-Holland\, Netherlands\, 2628CD\, Virtu al: https://events.vtools.ieee.org/m/418809 LOCATION:Room: Snijderszaal\, Bldg: 36\, MEkelweg 4\, Delft\, Zuid-Holland\ , Netherlands\, 2628CD\, Virtual: https://events.vtools.ieee.org/m/418809 ORGANIZER:F.Fioranelli@tudelft.nl SEQUENCE:23 SUMMARY:IEEE Distinguished Lecture "Leveraging Learning and Sparsity for Jo int Radar-Communications" - Dr Kumar Vijay Mishra URL;VALUE=URI:https://events.vtools.ieee.org/m/418809 X-ALT-DESC:Description: <br /><p>The AES/GRS and VT/COM joint chapters of t he IEEE Benelux section are pleased to invite you to this IEEE Distinguish ed Lecture by Dr Kumar Vijay Mishra on the topic of "Leveraging Learning a nd Sparsity for Joint Radar-Communications".</p>\n<p class="MsoNormal"><st rong><span lang="EN-US">Abstract</span></strong><span lang="EN-US">: Recen t interest in joint radar-communications (JRC) has led to the design of no vel signal processing techniques to recover information from an overlaid r adar-communications signal as well as transmit a common signal for both sy stems. In this talk\, we focus on two important tools for the design and s ignal processing of JRC systems: learning and sparsity. The interest in le arning-based JRC is driven largely by the need to solve difficult nonconve x optimization problems inherent in a JRC design as well as to address the highly dynamic channel environments. Toward fully realizing the coexisten ce/co-design of both radar and communications\, the optimization of resour ces for both sensing and wireless communications modalities is crucial. Bu t the optimization-based approaches suffer from high computational complex ity and their performance strongly relies on factors such as perfect chann el conditions\, specific constraints\, and mobility. In this context\, lea rning techniques provide robust performance at an upfront training cost. W e discuss applying learning to various JRC aspects including channel estim ation\, antenna selection\, resource allocation\, and wideband beamforming . The second half of the talk focuses on exploiting sparsity in a general spectral coexistence scenario\, wherein the channels and transmit signals of both radar and communications systems are unknown at the receiver. In t his dual-blind deconvolution (DBD) problem\, a common receiver admits a mu lti-carrier wireless communications signal that is overlaid with the radar signal reflected off multiple targets. The communications and radar chann els are represented by continuous-valued range-time and Doppler velocities of multiple transmission paths and multiple targets. We exploit the spars ity of both channels to solve the highly ill-posed DBD problem by casting it into a sum of multivariate atomic norms (SoMAN) minimization. Toward th e end of the talk\, we focus on highlighting emerging JRC scenarios\, part icularly at mm-Wave and THz frequencies\, vehicular applications\, distrib uted radar-communications networks\, intelligent surfaces\, and aerial cha nnels.</span></p> END:VEVENT END:VCALENDAR