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

TL;DR

This paper explores a multi-agent reinforcement learning framework for telecom operators to cooperate and reduce energy consumption by sharing infrastructure support, using blockchain technology for trust and negotiation, with the imposed rules yielding the best results.

Contribution

It introduces a novel MARL-based cooperation framework with blockchain integration for energy savings in RAN, evaluating different negotiation rules and demonstrating the effectiveness of imposed rules.

Findings

Imposed negotiation rules outperform other rule sets.

Cooperation among multiple SPs significantly reduces energy consumption.

Blockchain ensures trustworthiness in the negotiation process.

Abstract

Reducing energy consumption is crucial to reduce the human debt's with regard to our planet. Therefore most companies try to reduce their energetic consumption while taking care to preserve the service delivered to their customers. To do so, a service provider (SP) typically downscale or shutdown part of its infrastructure in periods of low-activity where only few customers need the service. However an SP still needs to maintain part of its infrastructure "on", which still requires significant energy. For example a mobile national operator (MNO) needs to maintain most of its radio access network (RAN) active. Could an SP do better by cooperating with other SPs who would temporarily support its users, thus allowing it to temporarily shut down its infrastructure, and then reciprocate during another low-activity period? To answer this question, we investigated a novel collaboration framework based on multi-agent reinforcement learning (MARL) allowing negotiations between SPs as well as trustful reports from a distributed ledger technology (DLT) to evaluate the amount of energy being saved. We leveraged it to experiment three different sets of rules (free, recommended, or imposed) regulating the negotiation between multiple SPs (3, 4, 8, or 10). With respect to four cooperation metrics (efficiency, safety, incentive-compatibility, and fairness), the simulations showed that the imposed set of rules proved to be the best mode.