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Communication Dans Un Congrès Année : 2022

Online convex optimization in wireless networks and beyond: The feedback -performance trade-off

Résumé

The high degree of variability present in current and emerging mobile wireless networks calls for mathematical tools and techniques that transcend classical (convex) optimization paradigms. The aim of this short survey paper is to provide a gentle introduction to online learning and optimization algorithms that are able to provably cope with this variability and provide policies that are asymptotically optimal in hindsight-a property known as no regret. The focal point of this survey will be to delineate the trade-off between the information available as feedback to the learner, and the achievable regret guaranteesstarting with the case of gradient-based (first-order) feedback, then moving on to value-based (zeroth-order) feedback, and, ultimately, pushing the envelope to the extreme case of a single bit of feedback. We illustrate our theoretical analysis with a series of practical wireless network examples that highlight the potential of this elegant toolbox.
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Dates et versions

hal-03737125 , version 1 (23-07-2022)
hal-03737125 , version 2 (27-07-2022)

Identifiants

  • HAL Id : hal-03737125 , version 2

Citer

Elena Veronica Belmega, Panayotis Mertikopoulos, Romain Negrel. Online convex optimization in wireless networks and beyond: The feedback -performance trade-off. RAWNET 2022 - International Workshop on Resource Allocation and Cooperation in Wireless Networks, Sep 2022, Turin, Italy. pp.1-8. ⟨hal-03737125v2⟩
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