An Analytical Energy Performance Evaluation Methodology for 5G Base Stations

Abstract

The implementation of various base station (BS) energy saving (ES) features and the widely varying network traffic demand makes it imperative to quantitatively evaluate the energy consumption (EC) of 5G BSs. An accurate evaluation is essential to understand how to adapt a BS’s resources to reduce its EC. On the other hand, modeling the variation in the power consumption (PC) of a BS with its resources considering the user equipment (UE) performance is mathematically rigorous. In this work, we present a novel analytical methodology to evaluate the EC of a 5G BS under varying traffic load. We mathematically formulate the impact of massive multiple-input and multiple-output (MIMO) arrays, vast spectral resources, and the spatial multiplexing ability of these systems on the UE performance and activity of the BS. Next, we present an updated power model to capture the PC variation of two BSs types: a 4T and a 64T BS. Our proposed analytical methodology simplifies the complex network EC evaluation. Using this methodology, we show that identifying the right BS type for a given deployment area can reduce the overall network EC by up to 60%. Furthermore, by implementing deep sleep modes (SMs) facilitated by 5G, one can gain considerable energy savings (ES), especially during the off peak hours of the day.