Cognitive Small Cell Networks: Energy Efficiency and Trade-Offs

TLDR

Heterogeneous networks combining macrocells and small cells are expected to meet rising mobile traffic, yet their extensive deployment substantially increases energy consumption, making energy‑efficient design essential. The study evaluates a distributed sleep‑mode strategy for cognitive small‑cell access points and examines the trade‑off between macrocell traffic offloading and small‑cell energy use. Using stochastic geometry, the authors model user discovery and uplink capacity under random positions, densities, user activity, propagation, interference, and sensing, define an interference‑density limit for robust detection, relate energy efficiency to sensing time via large‑deviation theory, and formulate optimization problems that yield design guidelines for energy‑efficient small‑cell networks. They establish a fundamental interference‑density threshold enabling robust detection, demonstrate how energy efficiency scales with sensing time, and provide optimization‑based design guidelines for energy‑efficient small‑cell networks.

Abstract

Heterogeneous networks using a mix of macrocells and small cells are foreseen as one of the solutions to meet the ever increasing mobile traffic demand. Nevertheless, a massive deployment of small cell access points (SAPs) leads also to a considerable increase in energy consumption. Spurred by growing environmental awareness and the high price of energy, it is crucial to design energy efficient wireless systems for both macrocells and small cells. In this work, we evaluate a distributed sleep-mode strategy for cognitive SAPs and we analyze the trade-off between traffic offloading from the macrocell and the energy consumption of the small cells. Using tools from stochastic geometry, we define the user discovery performance of the SAP and derive the uplink capacity of the small cells located in the Voronoi cell of a macrocell base station, accounting for the uncertainties associated with random position, density, user activity, propagation channel, network interference generated by uncoordinated activity, and the sensing scheme. In addition, we define a fundamental limit on the interference density that allows robust detection and we elucidate the relation between energy efficiency and sensing time using large deviations theory. Through the formulation of several optimization problems, we propose a framework that yields design guidelines for energy efficient small cell networks.