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:20240325T160316Z UID:DBA4527B-3395-4B01-B33A-515760BE6B5C DTSTART;TZID=America/New_York:20240320T190000 DTEND;TZID=America/New_York:20240320T200000 DESCRIPTION:The unprecedented proliferation of wireless infrastructures and their ongoing convergence with diverse industrial Internet of Things (IoT ) applications introduce new demands for upcoming wireless networks. In re sponse to such diversity of demands\, envisioned future wireless networks must have multiple beyond communication capabilities\, such as localizatio n and sensing. To efficiently utilize\, allocate\, and manage these capabi lities\, the integration of localization\, sensing\, and communication (IL SAC) within a unified wireless system structure is of utmost importance. H owever\, the seamless integration of ILSAC into intricate network infrastr uctures is encumbered by critical challenges\, including high-accuracy loc alization/sensing algorithm\, efficient resource management and allocation scheme\, and robust optimization method under dynamic situations. Therefo re\, this thesis introduces a value-driven\, multi-objective ILSAC system design mechanism.\n\nFirstly\, to extend the applicability of wireless loc alization into 3D environments targeting objects with six degrees of freed om\, while simultaneously enhancing localization accuracy and extracting v aluable environmental information from received signals\, a rigid body joi nt localization and environment sensing scheme is proposed. Specifically\, a two-step hierarchical compressive sensing algorithm is proposed to extr act the angular and distance information of the LOS (if available) and sin gle-bounce specular reflections. Then a particle swarm optimization (PSO) based method is derived to recover the posture of the rigid body and the l ocation of reflection points. The simulation results demonstrate that the proposed scheme can achieve high accuracy in rigid body localization and l ocate the reflection points around the rigid body even under obstructed li ne-of-sight (OLOS) conditions in an indoor scene.\n\nSecondly\, to address the challenge of integrative resource allocation among coexisting functio ns and services within an integrated system\, a service-oriented ILAC syst em is presented to allocate radio resources for diverse service provisioni ng under both static and dynamic environments. A novel concept\, termed Va lue of Service (VoS)\, is coined to maximize the unified performance of th e ILAC system for diverse service provisioning including localization accu racy and communication data rate. In the static scenario\, the bandwidth a nd temporal resource allocation problem is formulated as a mixed-integer n onlinear problem for ILAC to maximize its VoS. In a dynamic scenario\, a d eep-reinforcement learning (DRL) based adaptive resource allocation update algorithm is developed for long-term system gain maximization. Simulation results demonstrate the significant superiority of our proposed VoS evalu ation metric and resource allocation method in the ILAC system under both static and dynamic scenarios.\n\nThirdly\, to tackle the dual challenge of the environment-dependent and resource-intensive nature of wireless sensi ng\, along with managing the varied resource requirements of multiple user s\, we introduce a VoS-driven resource allocation scheme for cooperative s ervice provisioning in a multi-user ISAC system. We formulate the multi-us er resource allocation problem as a bargaining game-based model and addres s it using an iterative algorithm to attain the Nash equilibrium solution. In each iteration\, power and bandwidth resources are allocated by solvin g the Lagrangian dual problem. Numerical simulations are performed under v arying resource conditions\, service demands\, and channel states. The res ults highlight the superiority of our proposed scheme over non-collaborati ve alternatives and the other two benchmark schemes.\n\nVirtual: https://e vents.vtools.ieee.org/m/413483 LOCATION:Virtual: https://events.vtools.ieee.org/m/413483 ORGANIZER:pjia7@uwo.ca SEQUENCE:16 SUMMARY:Value of Service-Oriented Multi-Service Provisioning and Resource A llocation in Integrated Localization\, Sensing and Communication Systems URL;VALUE=URI:https://events.vtools.ieee.org/m/413483 X-ALT-DESC:Description: <br /><p class="MsoNormal">The unprecedented prolif eration of wireless infrastructures and their ongoing convergence with div erse industrial Internet of Things (IoT) applications introduce new demand s for upcoming wireless networks. In response to such diversity of demands \, envisioned future wireless networks must have multiple beyond communica tion capabilities\, such as localization and sensing. To efficiently utili ze\, allocate\, and manage these capabilities\, the integration of localiz ation\, sensing\, and communication (ILSAC) within a unified wireless syst em structure is of utmost importance. However\, the seamless integration o f ILSAC into intricate network infrastructures is encumbered by critical c hallenges\, including high-accuracy localization/sensing algorithm\, effic ient resource management and allocation scheme\, and robust optimization m ethod under dynamic situations. Therefore\, this thesis introduces a value -driven\, multi-objective ILSAC system design mechanism.</p>\n<p class="Ms oNormal">Firstly\, to extend the applicability of wireless localization in to 3D environments targeting objects with six degrees of freedom\, while s imultaneously enhancing localization accuracy and extracting valuable envi ronmental information from received signals\, a rigid body joint localizat ion and environment sensing scheme is proposed. Specifically\, a two-step hierarchical compressive sensing algorithm is proposed to extract the angu lar and distance information of the LOS (if available) and single-bounce s pecular reflections. Then a particle swarm optimization (PSO) based method is derived to recover the posture of the rigid body and the location of r eflection points. The simulation results demonstrate that the proposed sch eme can achieve high accuracy in rigid body localization and locate the re flection points around the rigid body even under obstructed line-of-sight (OLOS) conditions in an indoor scene.</p>\n<p class="MsoNormal">Secondly\, to address the challenge of integrative resource allocation among coexist ing functions and services within an integrated system\, a service-oriente d ILAC system is presented to allocate radio resources for diverse service provisioning under both static and dynamic environments. A novel concept\ , termed Value of Service (VoS)\, is coined to maximize the unified perfor mance of the ILAC system for diverse service provisioning including locali zation accuracy and communication data rate. In the static scenario\, the bandwidth and temporal resource allocation problem is formulated as a mixe d-integer nonlinear problem for ILAC to maximize its VoS. In a dynamic sce nario\, a deep-reinforcement learning (DRL) based adaptive resource alloca tion update algorithm is developed for long-term system gain maximization. Simulation results demonstrate the significant superiority of our propose d VoS evaluation metric and resource allocation method in the ILAC system under both static and dynamic scenarios.</p>\n<p class="MsoNormal">Thirdly \, to tackle the dual challenge of the environment-dependent and resource- intensive nature of wireless sensing\, along with managing the varied reso urce requirements of multiple users\, we introduce a VoS-driven resource a llocation scheme for cooperative service provisioning in a multi-user ISAC system. We formulate the multi-user resource allocation problem as a barg aining game-based model and address it using an iterative algorithm to att ain the Nash equilibrium solution. In each iteration\, power and bandwidth resources are allocated by solving the Lagrangian dual problem. Numerical simulations are performed under varying resource conditions\, service dem ands\, and channel states. The results highlight the superiority of our pr oposed scheme over non-collaborative alternatives and the other two benchm ark schemes.</p> END:VEVENT END:VCALENDAR