BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT DTSTART:20240310T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20241103T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20240311T000750Z UID:5A6C1734-77EC-4D2C-B7AD-3C939E421A09 DTSTART;TZID=America/New_York:20240423T200000 DTEND;TZID=America/New_York:20240423T213000 DESCRIPTION:Recent interest in joint radar-communications (JRC) has led to the design of novel signal processing techniques to recover information fr om an overlaid radar-communications signal as well as transmit a common si gnal for both systems. In this talk\, we focus on two important tools for the design and signal processing of JRC systems: learning and sparsity. Th e interest in learning-based JRC is driven largely by the need to solve di fficult nonconvex optimization problems inherent in a JRC design as well a s to address the highly dynamic channel environments. Toward fully realizi ng the coexistence/co-design of both radar and communications\, the optimi zation of resources for both sensing and wireless communications modalitie s is crucial. But the optimization-based approaches suffer from high compu tational complexity and their performance strongly relies on factors such as perfect channel conditions\, specific constraints\, and mobility. In th is context\, learning techniques provide robust performance at an upfront training cost. We discuss applying learning to various JRC aspects includi ng channel estimation\, antenna selection\, resource allocation\, and wide band beamforming. The second half of the talk focuses on exploiting sparsi ty in a general spectral coexistence scenario\, wherein the channels and t ransmit signals of both radar and communications systems are unknown at th e receiver. In this dual-blind deconvolution (DBD) problem\, a common rece iver admits a multi-carrier wireless communications signal that is overlai d with the radar signal reflected off multiple targets. The communications and radar channels are represented by continuous-valued range-time and Do ppler velocities of multiple transmission paths and multiple targets. We e xploit the sparsity of both channels to solve the highly ill-posed DBD pro blem by casting it into a sum of multivariate atomic norms (SoMAN) minimiz ation. Toward the end of the talk\, we focus on highlighting emerging JRC scenarios\, particularly at mm-Wave and THz frequencies\, vehicular applic ations\, distributed radar-communications networks\, intelligent surfaces\ , and aerial channels.\n\nCo-sponsored by: IEEE-USA MOVE Program\n\nSpeake r(s): Kumar Vijay Mishra\n\nVirtual: https://events.vtools.ieee.org/m/4065 64 LOCATION:Virtual: https://events.vtools.ieee.org/m/406564 ORGANIZER:l.arellano@ieee.org SEQUENCE:10 SUMMARY:MOVE Tech Talk - Apr 2024 - Exploiting Learning and Sparcity for Jo int Radar Communications URL;VALUE=URI:https://events.vtools.ieee.org/m/406564 X-ALT-DESC:Description: <br /><p><span style="font-size: 12.0pt\; line-heig ht: 107%\; font-family: 'Arial'\,sans-serif\; mso-fareast-font-family: Apt os\; mso-fareast-theme-font: minor-latin\; mso-ansi-language: EN-US\; mso- fareast-language: EN-US\; mso-bidi-language: AR-SA\;">Recent interest in j oint radar-communications (JRC) has led to the design of novel signal proc essing techniques to recover information from an overlaid radar-communicat ions signal as well as transmit a common signal for both systems. In this talk\, we focus on two important tools for the design and signal processin g of JRC systems: learning and sparsity. The interest in learning-based JR C is driven largely by the need to solve difficult nonconvex optimization problems inherent in a JRC design as well as to address the highly dynamic channel environments. Toward fully realizing the coexistence/co-design of both radar and communications\, the optimization of resources for both se nsing and wireless communications modalities is crucial. But the optimizat ion-based approaches suffer from high computational complexity and their p erformance strongly relies on factors such as perfect channel conditions\, specific constraints\, and mobility. In this context\, learning technique s provide robust performance at an upfront training cost. We discuss apply ing learning to various JRC aspects including channel estimation\, antenna selection\, resource allocation\, and wideband beamforming. The second ha lf of the talk focuses on exploiting sparsity in a general spectral coexis tence scenario\, wherein the channels and transmit signals of both radar a nd communications systems are unknown at the receiver. In this dual-blind deconvolution (DBD) problem\, a common receiver admits a multi-carrier wir eless communications signal that is overlaid with the radar signal reflect ed off multiple targets. The communications and radar channels are represe nted by continuous-valued range-time and Doppler velocities of multiple tr ansmission paths and multiple targets. We exploit the sparsity of both cha nnels to solve the highly ill-posed DBD problem by casting it into a sum o f multivariate atomic norms (SoMAN) minimization. Toward the end of the ta lk\, we focus on highlighting emerging JRC scenarios\, particularly at mm- Wave and THz frequencies\, vehicular applications\, distributed radar-comm unications networks\, intelligent surfaces\, and aerial channels.</span></ p> END:VEVENT END:VCALENDAR