Green networks and green tariffs as driven by user service demand
This section describes an approximate model built from real sub-system performance data, of a public wireless network (3G / LTE) in view of minimum net energy consumption or minimum emissions per time unit and per user. This approach is justified in order to generate the integrated view required for "green" optimizations, while taking into account service demand and operations While subsystems with lower native energy footprints are being migrated into public networks, the many adaptation mechanisms at sub-system, protocol and management levels make system complexity too high to design major comprehensive "green" trade-offs. However, by focusing on the incremental effects of a new network user, the approximate model allows marginal effects to be estimated with good accuracy. This capability allows for the provisioning of personalized energy / emissions reducing tariffs to end users with inherent advantages both to operators, energy suppliers and users. One key advantage is the possibility to reduce waste capacity, and thus energy consumption in the network, by allowing the user to specify just the service capacity and demands he/she has. From an engineering point of view, the incremental model allows to tune sub-system characteristics jointly, especially transceivers, transmission and storage. From a configuration point of view, the model allows to determine which nodes in the network benefit most from back-up and renewable power sources. From a business perspective, the model allows to determine trade-offs between personalized bundle characteristics and the energy cost share in the marginal operating expense share. Detailed sub-system model and design improvements are carried out on a continuous basis in collaboration with industry.
|CO2 emissions, Energy consumption, Green wireless tariffs, Marginal analysis, Personalized tariffs, Wireless networks|
Pau, L-F. (2012). Green networks and green tariffs as driven by user service demand. doi:10.1007/978-3-642-30382-1_16