Distributed Learning and Stable Orthogonalization in Ad-Hoc Networks with Heterogeneous Channels

Abstract--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 loadat base stations leading to a negative effect on spectrum and energy efficiency. To minimize signaling overhead, an adhoc approachis being considered for users communicating in unlicensed and shared spectrum. Decision of such users need 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. 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. Index Terms--Multi-player multi-armed bandit, ad-hoc networks, dynamicnetworks, distributed learning. I. INTRODUCTION Next generation wireless networks such as 5G aim to offer the wide range of new services such as enhanced local broadband, high-speed multimedia, mission-critical control, private networks such as Industrial IoT and enterprise [1] via spectrum sharing. Such networks with diverse service requirements are expected to greatly enhance user experience [1]. Recently, 3GPP proposed a new radio (NR) based heterogeneous networksconsisting of base stations of various sizes. Compared to existing networks, NRs can operate not only in licensed spectrum but also in the shared (2.3 GHz/ 3.5 GHz) as well as unlicensed spectrum (2.4 GHz / 5-7 GHz / 57-71 GHz). Such network opens up many interesting challenges such as resource allocation, dynamic and contextaware networkadaptation, and in-depth knowledge discovery in the complex environment for which machine learning and artificial intelligence frameworks offer novel solutions [1-4]. The next generation networks are envisioned to work on the principle of separate signaling (large base station) and data infrastructure (small base stations) which allows adaptation of data network to the current traffic situation while maintaining the coverage. These networks will be ultra dense with very high peak rate but relatively lower expected traffic per network node [1].