Title
A Bandit Learning Approach to Energy-Efficient Femto-Caching under Uncertainty
Abstract
We address a resource allocation problem for joint caching and broadcast transmission in small cell networks with time-varying statistical properties. Each small base station (SBS) selects some files to store in its capacity-limited cache, given no prior information about the random and dynamic parameters such as file popularity, channel quality, and network traffic. Moreover, at consecutive rounds, a file is selected from the cache to broadcast. We define the utility of the SBS in terms of the number of successful file receptions per power consumption. The problem is formulated as to place the cache, and afterward select a file from the cache together with a transmission power for every broadcast round. The goal is to maximize the accumulated utility over the horizon. Therefore, we decompose the initial problem into two sub-problems: (i) cache placement, and (ii) joint fileand transmit power selection. The former problem boils down to a stochastic knapsack problem with stationary items' value, whereas the latter is cast as a multi-armed bandit problem with mortal arms. We develop a solution to each problem and evaluate the proposed solutions by theoretical and numerical analysis.
Year
DOI
Venue
2019
10.1109/GLOBECOM38437.2019.9013630
IEEE Global Communications Conference
Keywords
DocType
ISSN
Caching,change point detection,knapsack problem,mortal multi-armed bandits
Conference
2334-0983
Citations 
PageRank 
References 
1
0.34
0
Authors
2
Name
Order
Citations
PageRank
Setareh Maghsudi115416.41
Mihaela Van Der Schaar23968352.59