Title : « Online Power Optimization in Feedback-Limited, Dynamic and Unpredictable IoT Networks »
Abstract :
One of the key challenges in Internet of Things (IoT) networks is to connect many different types of autonomous devices while reducing their individual power consumption. This problem is exacerbated by two main factors: a) the fact that these devices operate in and give rise to a highly dynamic and unpredictable environment where existing solutions (e.g., water-filling algorithms) are no longer relevant and b) the lack of sufficient information at the device end. To address these issues, we propose a regret-based formulation that accounts for arbitrary network dynamics: this allows us to derive an online power control scheme which is provably capable of adapting to such changes, while relying solely on strictly causal feedback. In so doing, we identify an important tradeoff between the amount of feedback available at the transmitter side and the resulting system performance: if the device has access to unbiased gradient observations, the algorithm’s regret after T stages is O(T^(-1/2)) (up to logarithmic factors); on the other hand, if the device only has access to a scalar, utility-based information, this rate drops to O(T^(-3/4)). The above is validated by an extensive suite of numerical simulations in realistic channel conditions, which clearly exhibit the agains of the proposed online approach over traditional water-filling methods.
Bio :
Diplômé de l’ENSEA (École Nationale Supérieur de l’Électronique et de ses Applications) en 2014, Alexandre Marcastel a poursuivi ses études avec un Master de Recherche SIC (Systèmes Intelligents et Communicants) à l’université de Cergy-Pontoise. Son manuscrit de master portait sur le développement d’Algorithmes efficaces d’allocation de ressources dans les systèmes Radio Cognitifs.
Il est actuellement en thèse dans le laboratoire ETIS-UMR 8051 Université Paris Seine, Université Cergy-Pontoise, ENSEA, CNRS et ses recherches portent sur le développement d’algorithmes d’allocation de ressource en ligne pour des systèmes de communication IoT.
