Cognitive Small Cell Networks: Energy Efficiency and Trade-Offs

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.

Energy-Aware Cognitive Radio Systems

“…The total instantaneous/average consumed energy in a given transmission can be also used as an energy-aware metric, and it is measured in joule (J) [18,24,26,30,33,46,48].…”

“…The average energy consumption of a two-tier heterogenous network, where the 2nd tier network is equipped with SAP with cognitive capabilities, can be expressed as [46] (15) where ϖ m and ϖ s are the densities of mobile base station of the 1st tier network and the small cell access points of the 2nd tier network, respectively, p syn and p proc are the synchronization and processing powers, respectively, φ UE is the probability that there is a user equipment within the coverage of an SAP unit, and φ s is the success probability of a typical user within the coverage area of SAP. For a cooperative sensing distributed CR sensor network, where the SU may turn off its sensing and transmission capabilities in order to save energy, the average energy consumption is expressed as [33] …”

“…The sensing process plays an important role in EE optimization in CR networks [7,18,24,26,29,30,33,39,41,42,46,47]. Increasing the sensing time improves the detection probability at the expense of consuming more energy, whereas reducing the sensing time results in more collisions with existing PUs due to false detection.…”

A Novel Handover Scheme based on Context Information in 5G Network

“…In [9], the threshold ratio of small cell BS in sleep state is defined, and if the ratio of small cell BS in sleep state is below the threshold ratio, small cell BSs with low traffic load are turned off, and the traffic load covered by the small cell BSs are handed over to other small cell BSs. Otherwise, BSs with high traffic load are turned off, and the traffic load covered by the small cell BSs are distributed to other small cell BSs.…”

Modeling and Performance Evaluation of a Context Information-Based Optimized Handover Scheme in 5G Networks

“…However, this results in signaling overhead between a macro cell BS and small cell BSs [15]. In distributed activation/deactivation schemes [16], each small cell BS decides whether it should be activated or deactivated. If there is no UE within the coverage of a small cell BS, it is turned off.…”