Distributed Learning in Ad-Hoc Networks with Unknown Number of Players

Abstract

We study algorithms for distributed learning in ad-hoc cognitive networks where no central controller is available. In such networks, the players cannot communicate with each other and even may not know how many other players are present in the network. If multiple players select a common channel they collide, which results in loss of throughput for the colliding players. We consider both the static and dynamic scenarios where the number of players remains fixed throughout the game in the former case and can change in the later. We provide algorithms based on a novel 'trekking approach' that guarantees with high probability constant regret for the static case and sub-linear regret for the dynamic case. The trekking approach gives improved aggregate throughput and also results in fewer collisions compared to the state-of-the-art algorithms.