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Reducing energy consumption is crucial to reduce the human debt's with regardto our planet. Therefore most companies try to reduce their energeticconsumption while taking care to preserve the service delivered to theircustomers. To do so, a service provider (SP) typically downscale or shutdownpart of its infrastructure in periods of low-activity where only few customersneed the service. However an SP still needs to maintain part of itsinfrastructure "on", which still requires significant energy. For example amobile national operator (MNO) needs to maintain most of its radio accessnetwork (RAN) active. Could an SP do better by cooperating with other SPs whowould temporarily support its users, thus allowing it to temporarily shut downits infrastructure, and then reciprocate during another low-activity period? Toanswer this question, we investigated a novel collaboration framework based onmulti-agent reinforcement learning (MARL) allowing negotiations between SPs aswell as trustful reports from a distributed ledger technology (DLT) to evaluatethe amount of energy being saved. We leveraged it to experiment three differentsets of rules (free, recommended, or imposed) regulating the negotiationbetween multiple SPs (3, 4, 8, or 10). With respect to four cooperation metrics(efficiency, safety, incentive-compatibility, and fairness), the simulationsshowed that the imposed set of rules proved to be the best mode.

Evaluating Inter-Operator Cooperation Scenarios to Save Radio Access Network Energy