Distributed User Association in Energy Harvesting Small Cell Networks: A Probabilistic Model
This work addresses energy-efficient user association in dense wireless networks, but it is incremental as it builds on existing probabilistic and bandit methods.
The authors tackled the problem of distributed user association in energy-harvesting small cell networks by proposing a probabilistic model to handle random energy harvesting and consumption, and developed a bandit-theoretical formulation for distributed decision-making without prior network knowledge.
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