Title :
Online learning for network optimization under unknown models
Author :
Yixuan Zhai ; Qing Zhao
Author_Institution :
Electr. & Comput. Eng., Univ. of California, Davis, Davis, CA, USA
Abstract :
We consider the shortest path problem in a communication network with random link costs drawn from unknown distributions. A realization of the total end-to-end cost is obtained when a path is selected for communication. The objective is an online learning algorithm that minimizes the total expected communication cost in the long run. The problem is formulated as a multi-armed bandit problem with dependent arms, and an algorithm based on basis-based learning integrated with a Best Linear Unbiased Estimator (BLUE) is developed.
Keywords :
learning (artificial intelligence); random processes; telecommunication computing; telecommunication links; telecommunication network routing; BLUE; basis-based learning; best linear unbiased estimator; communication network; multiarmed bandit problem; multihop communication network; network optimization; online learning algorithm; packet routing; random link costs; shortest path problem; total end-to-end cost; total expected communication cost; unknown distribution model; Adaptation models; Cognitive radio; Delays; Optimization; Random variables; Routing; Vectors; Bandit problem; best linear unbiased estimator; shortest path;
Conference_Titel :
Global Conference on Signal and Information Processing (GlobalSIP), 2013 IEEE
Conference_Location :
Austin, TX
DOI :
10.1109/GlobalSIP.2013.6736943
