Paper Info
Title
Distributed Algorithms for Efficient Learning and Coordination in Ad Hoc Networks
Abstract
A distributed sampling strategy for multiple (N) agents is considered that minimizes the sample complexity and regret of acquiring the best subset of size <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> among total <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> ≥ <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> channels in a cognitive radio access setup. Agents cannot directly communicate with each other, and no central coordination is possible. Each agent can transmit on one channel at a time, and if multiple agents transmit on the same channel at the same time, a collision occurs, and no agent gets any information about the channel gain or how many other agents transmitted on the same channel. If no collision occurs, the agent observes a reward (or gain) sample drawn from an underlying distribution associated with the channel. An algorithm to minimize the sample complexity and regret is proposed. One important property of our algorithm that distinguishes it from the prior work (that do not assume knowledge of N) is that it requires no information about the difference of the means of the channel gains of the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> channels. Our approach results in fewer collisions with improved regret performance compared to the state-of-the-art algorithms. We validate our theoretical guarantees with experiments.
Year
DOI
Venue
2019
10.23919/WiOPT47501.2019.9144100
2019 International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOPT)
Keywords
DocType
ISBN
Multi-player Multi-armed Bandits,Constant regret,Pure exploration,Distributed learning
Conference
978-3-903176-20-1
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Arun Verma100.34
Manjesh Kumar Hanawal29921.89
Rahul Vaze346345.64