Distributed User Association in Energy Harvesting Small Cell Networks: A Competitive Market Model with Uncertainty

Abstract

We consider a distributed user association problem in the downlink of 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 a priori known characteristics. We model the network as a competitive market with uncertainty, where self-interested small cells, modeled as consumers, are willing to maximize their utility scores by selecting users, represented by commodities. The utility scores of small cells depend on the amount of harvested energy, formulated as natures' state. Under this model, the problem is to assign users to small cells, so that the aggregate network utility is maximized. The solution is the general equilibrium under uncertainty, also called Arrow-Debreu equilibrium. We show that in our setting, such equilibrium not only exists, but also is unique and is Pareto optimal in the sense of expected aggregate network utility. We use the Walras' tatonnement process with some modifications in order to implement the equilibrium efficiently.

References in corpus (3)

• Distributed Cell Association for Energy Harvesting IoT Devices in Dense Small Cell Networks: A Mean-Field Multi-Armed Bandit Approach
• A context-aware matching game for user association in wireless small cell networks
• Distributed User Association in Energy Harvesting Small Cell Networks: A Probabilistic Model

Cited by in corpus (5)

• Energy Efficient Resource Allocation in Machine-to-Machine Communications with Multiple Access and Energy Harvesting for IoT
• Distributed Cell Association for Energy Harvesting IoT Devices in Dense Small Cell Networks: A Mean-Field Multi-Armed Bandit Approach
• A Non-Stationary Bandit-Learning Approach to Energy-Efficient Femto-Caching with Rateless-Coded Transmission
• Distributed Task Management in Cyber-Physical Systems: How to Cooperate under Uncertainty?
• Distributed Task Management in Fog Computing: A Socially Concave Bandit Game