Distributed User Association in Energy Harvesting Small Cell Networks: A Probabilistic Model

TL;DR

This paper introduces a probabilistic model and a bandit-theoretic approach for distributed user association in energy harvesting small cell networks, addressing the randomness in energy availability and user demands without prior network knowledge.

Contribution

It presents a novel probabilistic framework and a bandit-based distributed algorithm for user association in energy harvesting small cell networks, handling uncertainty without prior information.

Findings

Effective modeling of energy harvesting randomness

Distributed algorithm performs well without prior network knowledge

Framework applicable to dense small cell networks

Abstract

We consider a distributed downlink user association problem in a small cell network, where small cells obtain the required energy for providing wireless services to users through ambient energy harvesting. Since energy harvesting is opportunistic in nature, the amount of harvested energy is a random variable, without any a priori known characteristics. Moreover, since users arrive in the network randomly and require different wireless services, the energy consumption is a random variable as well. In this paper, we propose a probabilistic framework to mathematically model and analyze the random behavior of energy harvesting and energy consumption in dense small cell networks. Furthermore, as acquiring (even statistical) channel and network knowledge is very costly in a distributed dense network, we develop a bandit-theoretical formulation for distributed user association when no information is available at users