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UID:930@lincs.fr
DTSTART;TZID=Europe/Paris:20251127T140000
DTEND;TZID=Europe/Paris:20251127T180000
DTSTAMP:20251126T170339Z
URL:https://www.lincs.fr/events/optimisation-de-lallocation-des-ressources
 -dans-les-reseaux-sans-fil-du-futur-en-presence-dincertitude/
SUMMARY:Optimisation de l’allocation des ressources dans les réseaux
 sans fil du futur en présence d’incertitude
DESCRIPTION:We address in this thesis the challenge of efficient resource
 allocation under uncertainty for the transport of time-critical ultra
 reliable traffic in next-generation networks. We develop optimization and
 online-learning methods that provide rigorous performance guarantees for
 short-horizon probabilistic requirements and long-term cumulative
 constraints. We begin with Ultra-Reliable Low-Latency Communications
 (URLLC). Prior models for probabilistic delay either impose strong
 assumptions on arrivals or focus primarily on queue stability. We relax
 these assumptions and formulate a chance-constrained minimization of
 resource usage that holds under general arrival processes. By exploiting
 structural properties of the policy space\, we design efficient
 bandit-based algorithms for both offline (known statistics) and online
 (unknown statistics) settings. These algorithms provably converge in a
 fixed number of iterations while meeting stringent 1ms delay and (10^{-5})
 reliability targets with minimal resource consumption. We then push these
 guarantees to extreme URLLC targeting (0.1\,mathrm{ms}) latency and
 reliability on the order of (10^{-7})\, where queuing is impermissible and
 the resource allocation schemes must rely on limited arrival information
 (historical samples\, mean\, variance). Static methods tend to
 over-allocate resources. We introduce an online\, dynamic reservation
 policy based on a sliding-window scenario approach that is robust and safe:
 it tracks minimal reservations from empirical data and avoids conservative
 over-provisioning while preserving stringent QoS constraints. Next\, we
 consider goal-oriented communications\, focusing on haptic applications
 that are highly sensitive to bursts of packet losses. We propose a
 queuing-theoretic framework which minimizes resource costs in the presence
 of losses from both collisions with other haptic packets and poor radio
 conditions. We design a joint control policy that combines adaptive
 transmit-power boosting with preemption of resources initially provisioned
 for enhanced Mobile Broadband (eMBB)\, governed by threshold policies. For
 heterogeneous users\, interdependence across user groups induces a
 high-dimensional decision space\, ruling out exhaustive search. To address
 this complexity\, we make use of a modified simulated-annealing algorithm
 with constraint handling through direct rejection of infeasible policies or
 cost-based penalties. Eventually\, we study long-term compliance and
 introduce Constrained Online Convex Optimization with Memory (COCO-M)\,
 where losses and constraints depend on the last (m) decisions. Prior work
 considered mainly fixed memory length. We generalize to arbitrary memory
 lengths and incorporate untrusted short-horizon predictions\, providing the
 first algorithms with provable sublinear regret and sublinear cumulative
 constraint violation in this general setting. This yields a versatile
 toolbox for online learning and predictive network control under
 adversarial conditions.
CATEGORIES:PhD Defense
LOCATION:Amphi Rose Dieng\, 19 place Marguerite Perey\, Palaiseau\, France
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=19 place Marguerite Perey\,
 Palaiseau\, France;X-APPLE-RADIUS=100;X-TITLE=Amphi Rose Dieng:geo:0,0
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TZID:Europe/Paris
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DTSTART:20251026T020000
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TZOFFSETTO:+0100
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