Distributed Learning and Stable Orthogonalization in Ad-Hoc Networks with Heterogeneous Channels
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Next generation networks are expected to be ultra dense and aim to explore spectrum sharing paradigm that allows users to communicate in licensed, shared as well as unlicensed spectrum. Such ultra-dense networks will incur significant signaling load at base stations leading to a negative effect on spectrum and energy efficiency. To minimize signaling overhead, an ad-hoc approach is being considered for users communicating in an unlicensed and shared spectrum. A decision of such users needs to completely decentralized as: 1) No communication between users and signaling from the base station is possible which necessitates independent channel selection at each user. A collision occurs when multiple users transmit simultaneously on the same channel, 2) Channel qualities may be heterogeneous, i.e., they are not same across all users, and moreover are unknown, and 3) The network could be dynamic where users can enter or leave anytime. We develop a multi-armed bandit based distributed algorithm for static networks and extend it for the dynamic networks. The algorithms aim to achieve stable orthogonal allocation (SOC) in finite time and meet the above three constraints with two novel characteristics: 1) Low complex narrowband radio compared to wideband radio in existing works, and 2) Epoch-less approach for dynamic networks. We establish convergence of our algorithms to SOC and validate via extensive simulation experiments.
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