Energy-Based Cell Association in Nonuniform Renewable Energy-Powered Cellular Networks: Analysis and Optimization of Carbon Efficiency

TL;DR

The paper addresses carbon emissions in renewable-energy-powered cellular networks with nonuniform user distributions by jointly modeling renewable-energy (RE) dynamics and channel occupancy as a quasi-birth-death process, and by evaluating performance via stochastic geometry. It derives closed-form expressions for channel blocking, successful transmission, throughput, and the carbon-emission/efficiency metrics, linking them to an energy-based biased cell association. A biased max long-term received-power scheme is proposed, and a genetic-algorithm optimization identifies association biases that maximize carbon efficiency (η_ce = R / E_tot) under QoS constraints, achieving up to 13.0% lower CEm and 11.3% higher CEf compared with nearest-cell association. The results demonstrate a practical pathway to greener cellular networks by better matching renewable supply with demand, though simplifications (e.g., BS independence, PCP modeling) suggest avenues for further refinement and AI-driven energy management.

Abstract

The increasing global push for carbon reduction highlights the importance of integrating renewable energy into the supply chain of cellular networks. However, due to the stochastic nature of renewable energy generation and the uneven load distribution across base stations, the utilization rate of renewable energy remains low. To address these challenges, this paper investigates the trade-off between carbon emissions and downlink throughput in cellular networks, offering insights into optimizing both network performance and sustainability. The renewable energy state of base station batteries and the number of occupied channels are modeled as a quasi-birth-death process. We construct models for the probability of channel blocking, average successful transmission probability for users, downlink throughput, carbon emissions, and carbon efficiency based on stochastic geometry. Based on these analyses, an energy-based cell association scheme is proposed to optimize the carbon efficiency of cellular networks. The results show that, compared to the closest cell association scheme, the energy-based cell association scheme is capable of reducing the carbon emissions of the network by 13.0% and improving the carbon efficiency by 11.3%.

Figures (9)

• Figure 1: System model.
• Figure 2: State-transition diagram of the QBD with $N=5$ channels
• Figure 3: Probability of successful transmission.
• Figure 4: (a) Proportion of BSs in different statuses within different association-bias parameters. (b) Proportion of BSs in different statuses within different RE-generation rates.
• Figure 5: CEm per unit area.