2013/02/27 by Marc Lelarge, Lelarge, Marc, Alexandre Proutière +3
Computer Science · Decision Sciences · Engineering · #Advanced Bandit Algorithms Research #Advanced Wireless Network Optimization #Cognitive Radio Networks and Spectrum Sensing #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Networking and Internet Architecture (cs.NI) #Optimization and Control (math.OC)
paper · doi:10.48550/arxiv.1302.6974
openalex publication_date 2013/02/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We consider the problem of allocating radio channels to links in a wireless network. Links interact through interference, modelled as a conflict graph (i.e., two interfering links cannot be simultaneously active on the same channel). We aim at identifying the channel allocation maximizing the total network throughput over a finite time horizon. Should we know the average radio conditions on each channel and on each link, an optimal allocation would be obtained by solving an Integer Linear Program (ILP). When radio conditions are unknown a priori, we look for a sequential channel allocation policy that converges to the optimal allocation while minimizing on the way the throughput loss or \it regret due to the need for exploring sub-optimal allocations. We formulate this problem as a generic linear bandit problem, and analyze it first in a stochastic setting where radio conditions are driven by a stationary stochastic process, and then in an adversarial setting where radio conditions can evolve arbitrarily. We provide new algorithms in both settings and derive upper bounds on their regrets.