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Next-generation networks are expected to be ultra-dense with a very high peakrate but relatively lower expected traffic per user. For such scenario,existing central controller based resource allocation may incur substantialsignaling (control communications) leading to a negative effect on the qualityof service (e.g. drop calls), energy and spectrum efficiency. To overcome thisproblem, cognitive ad-hoc networks (CAHN) that share spectrum with othernetworks are being envisioned. They allow some users to identify andcommunicate in `free slots' thereby reducing signaling load and allowing thehigher number of users per base stations (dense networks). Such networks openup many interesting challenges such as resource identification, coordination,dynamic and context-aware adaptation for which Machine Learning and ArtificialIntelligence framework offers novel solutions. In this paper, we discussstate-of-the-art multi-armed multi-player bandit based distributed learningalgorithms that allow users to adapt to the environment and coordinate withother players/users. We also discuss various open research problems forfeasible realization of CAHN and interesting applications in other domains suchas energy harvesting, Internet of Things, and Smart grids.

Distributed Learning in Ad-Hoc Networks: A Multi-player Multi-armed Bandit Framework