🤖 AI Summary
Accurately modeling energy consumption in 5G radio access networks (RANs) remains challenging due to technological heterogeneity and deployment diversity. To address this, this paper proposes a fine-grained, configurable cross-layer energy consumption model. For the first time, it jointly incorporates physical-layer algorithmic complexity and hardware implementation characteristics, using computational cycles as a unifying metric to holistically characterize energy consumption across baseband processing, user equipment access, and channel interaction. The model is calibrated via MATLAB simulations and empirical measurements on Intel platforms. Validation across diverse deployment scenarios demonstrates an average error of less than 8%, significantly outperforming existing approaches. The proposed model enables cross-application energy-efficiency benchmarking and network-level green optimization. It establishes a new paradigm for verifiable and scalable energy-efficiency assessment of 5G RANs.
📝 Abstract
Despite the rapid advancements in 5G technology, accurately assessing the energy consumption of its Radio Ac-cess Networks (RANs) remains a challenge due to the diverse range of applicable technologies and implementation solutions. Designing a versatile power model for estimating the 5G RAN-specific power consumption requires extensive data collection and experimental studies to capture the diverse range of technolo-gies and implementation solutions. The objective is to outline a versatile energy model capable of estimating RAN-specific energy consumption, encompassing both mobile terminals and the physical layer (PHY) of base stations. In this paper, we focus on the computational complexity of the baseband part of the model. The developed (part of the) model is compared with the estimation of the number of cycles (and energy per cycle) used by a specific implementation (here a Matlab code ported on an Intel target), enabling the assessment of the model with the estimation of energy consumed on a real target. The study's results show a good agreement between the model and the implementation, even if some parts need to be refined to take specific algorithms into account. The key contribution is the development of an initial flexible energy model with finer granularity, enabling comparisons of energy use across various applications and contexts, and offering a comprehensive tool for optimizing 5G network energy consumption.