Carbon Efficiency Modeling and Analysis of Renewable-energy-powered Cellular Networks

To meet the imperative for sustainable low-carbon wireless communications, integrating distributed renewable energy sources with base stations is essential. However, there is often a mismatch between the energy generated by renewable sources and the energy required by base stations. To address this issue, optimization strategies such as traffic offloading and energy sharing are commonly employed. This paper introduces a spatial model based on stochastic geometry, mapping the spatial distribution of base stations and users, and quantifying the probability of coverage in cellular networks powered by renewable energy with energy-sharing and traffic offloading capabilities. Complementing this spatial analysis, we apply queuing theory to represent the base station energy status as a Markov chain, leading to a nuanced carbon emissions model post energy-sharing. Furthermore, a new metric called carbon efficiency is defined for accurately capturing the trade off between carbon emissions and performance of cellular networks. Simulations show that there is an optimal traffic offloading probability that can minimize carbon emissions in cellular networks while sacrificing the expected ergodic rate of users. These insights offer a foundation for developing optimization strategies that elevate the carbon efficiency of renewable-energy-driven cellular networks.