BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT DTSTART:20260308T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20261101T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260413T140218Z UID:8343522B-A947-4534-8A06-FD617F68BB45 DTSTART;TZID=America/New_York:20260410T180000 DTEND;TZID=America/New_York:20260410T190000 DESCRIPTION:The rapid evolution of wireless communication toward beyond-6G (B6G) and next generation Wi-Fi networks is driving a fundamental transfor mation in how radio resources are perceived\, managed\, and optimized. As future networks are envisioned not only as communication infrastructures b ut as intelligent\, multi-functional ecosystems\, such paradigm shift intr oduces unprecedented challenges of multi-dimensional resource management i ncluding spectrum scarcity\, severe spatial-temporal interference coupling \, device heterogeneity and dynamically evolving service requirements. The refore\, this thesis establishes unified theoretical and algorithmic frame works for goal-directed\, value-oriented\, and learning-empowered resource orchestration and service provisioning to support future intelligent wire less networks.\n\nTo accommodate heterogeneous device capabilities and div erse quality-of-service (QoS) requirements\, a user-centric multi-dimensio nal multiple access (MDMA) framework is proposed by introducing a novel co ncept of resource utilization cost to quantify the energy\, processing\, a nd computational complexity associated with non-orthogonal interference ca ncellation procedure. By formulating user-specific situation-dependent cos t-gain utilities\, a max-min optimized solution is derived\, achieving fin er service provisioning granularity and higher inter-user fairness than st ate-of-the-art schemes.\n\nBuilding upon this foundation\, a novel demand- aware prioritization mechanism and diversity\, equity\, inclusion (DEI)-ba sed resource allocation scheme are proposed. By incorporating demand-aware priorities for individual users\, the DEI-based metric evaluates the weig hted mean-variance tradeoff of network-wide user utilities. The proposed a lgorithms can inclusively support differentiated service categories with h igher average utility and smaller inter-user disparity verified extensive simulations. The DEI method can also adaptively accommodate and prioritize diverse QoS demands based on individualized service requirements and dyna mic resource conditions.\n\nTo address the growing complexity of coupled s patial-temporal interference and distributed resource reusing in ultra-den se Wi-Fi networks\, a novel multi-access point (AP) multi-dimensional reso urce coordination mechanism is developed. The proposed method amalgamates coordinated spatial reuse (Co-SR) and coordinated orthogonal frequency div ision multiple access (Co-OFDMA) by dynamic channel access sensitivity con figuration\, resource unit (RU)-level interference assessment and reusabil ity evaluation. Simulations validate that the proposed multi-agent deep re inforcement learning (MADRL) method can opportunistically coordinate multi -dimensional resources of each AP for diverse QoS provisioning\, outperfor ming baselines by effective interference mitigation with fairness guarante es.\n\nFinally\, a goal-directed\, contextually adaptive multi-AP coordina tion is developed using an hierarchical learning framework. The meta-level performs coarse-timescale coordination goal adaptation using semi-Markov proximal policy optimization (PPO) algorithm\, while the execution level s olves fine-timescale coordinated resource decisions via a goal-conditioned MADRL model. An alternating joint-training strategy is introduced to reco ncile timescale disparity and coupled non-stationarity\, enabling adaptive and context-consistent network intelligence that aligns local actions wit h evolving global intents.\n\nVirtual: https://events.vtools.ieee.org/m/55 3542 LOCATION:Virtual: https://events.vtools.ieee.org/m/553542 ORGANIZER:fchen324@uwo.ca SEQUENCE:37 SUMMARY:Goal-Directed Service Provisioning and Value-Oriented Multi-Dimensi onal Resource Orchestration for Future Wireless Networks URL;VALUE=URI:https://events.vtools.ieee.org/m/553542 X-ALT-DESC:Description: <br /><p>The rapid evolution of wireless communicat ion toward beyond-6G (B6G) and next generation Wi-Fi networks is driving a fundamental transformation in how radio resources are perceived\, managed \, and optimized. As future networks are envisioned not only as communicat ion infrastructures but as intelligent\, multi-functional ecosystems\, suc h paradigm shift introduces unprecedented challenges of multi-dimensional resource management including spectrum scarcity\, severe spatial-temporal interference coupling\, device heterogeneity and dynamically evolving serv ice requirements. Therefore\, this thesis establishes unified theoretical and algorithmic frameworks for goal-directed\, value-oriented\, and learni ng-empowered resource orchestration and service provisioning to support fu ture intelligent wireless networks.</p>\n<p>&nbsp\; &nbsp\; To accommodate heterogeneous device capabilities and diverse quality-of-service (QoS) re quirements\, a user-centric multi-dimensional multiple access (MDMA) frame work is proposed by introducing a novel concept of resource utilization co st to quantify the energy\, processing\, and computational complexity asso ciated with non-orthogonal interference cancellation procedure. By formula ting user-specific situation-dependent cost-gain utilities\, a max-min opt imized solution is derived\, achieving finer service provisioning granular ity and higher inter-user fairness than state-of-the-art schemes.<br>&nbsp \; &nbsp\;&nbsp\;<br>&nbsp\; &nbsp\; Building upon this foundation\, a nov el demand-aware prioritization mechanism and diversity\, equity\, inclusio n (DEI)-based resource allocation scheme are proposed. By incorporating de mand-aware priorities for individual users\, the DEI-based metric evaluate s the weighted mean-variance tradeoff of network-wide user utilities. The proposed algorithms can inclusively support differentiated service categor ies with higher average utility and smaller inter-user disparity verified extensive simulations. The DEI method can also adaptively accommodate and prioritize diverse QoS demands based on individualized service requirement s and dynamic resource conditions.</p>\n<p>&nbsp\; &nbsp\; To address the growing complexity of coupled spatial-temporal interference and distribute d resource reusing in ultra-dense Wi-Fi networks\, a novel multi-access po int (AP) multi-dimensional resource coordination mechanism is developed. T he proposed method amalgamates coordinated spatial reuse (Co-SR) and coord inated orthogonal frequency division multiple access (Co-OFDMA) by dynamic channel access sensitivity configuration\, resource unit (RU)-level inter ference assessment and reusability evaluation. Simulations validate that t he proposed multi-agent deep reinforcement learning (MADRL) method can opp ortunistically coordinate multi-dimensional resources of each AP for diver se QoS provisioning\, outperforming baselines by effective interference mi tigation with fairness guarantees.</p>\n<p>&nbsp\; &nbsp\; Finally\, a goa l-directed\, contextually adaptive multi-AP coordination is developed usin g an hierarchical learning framework. The meta-level performs coarse-times cale coordination goal adaptation using semi-Markov proximal policy optimi zation (PPO) algorithm\, while the execution level solves fine-timescale c oordinated resource decisions via a goal-conditioned MADRL model. An alter nating joint-training strategy is introduced to reconcile timescale dispar ity and coupled non-stationarity\, enabling adaptive and context-consisten t network intelligence that aligns local actions with evolving global inte nts.</p> END:VEVENT END:VCALENDAR