AI & ML
Carbon-Aware Joint Resource and Energy Scheduling for Cellular Base Stations
arXiv eess.SP0 views
arXiv:2610.19137v1 Announce Type: new Abstract: Decarbonizing cellular networks requires coordinating communication-resource and battery operation with time-varying electricity prices and carbon intensity. We jointly optimize base station and battery operation under a carbon-adjusted electricity price and traffic-demand constraints, and solve the mixed-integer nonconvex problem through a two-stage decomposition exact under a minimum-power rule. Using California price and carbon-intensity data, we show that carbon-aware scheduling reduces emissions most when low-price and low-carbon periods are misaligned. Above a carbon price of roughly 100-300 USD/ton, battery operation shifts from cost-driven to carbon-driven, cutting emissions by up to 57.8% relative to a cost-only schedule.
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