Holistic Energy Performance Management: Enablers, Capabilities, and Features

2026-03-19

AI Summary

This study addresses the limitations of existing 5G NR energy-saving mechanisms, which are typically enabled in isolation and struggle to balance energy efficiency with network performance. To overcome this, the authors propose a holistic energy management framework that decouples hardware capabilities from higher-layer energy-saving features and introduces a logical “feature coordinator” to jointly orchestrate signaling, dynamic resource allocation, and advanced sleep modes. The framework establishes a compact taxonomy of energy-saving features, enabling, for the first time, coordinated optimization across multiple mechanisms. Evaluations on a 3GPP-aligned simulation platform using production-grade parameters demonstrate that the proposed approach significantly reduces gNodeB energy consumption compared to non-coordinated schemes, while incurring only negligible throughput degradation.

Technology Category

• Game Theory and Economic Paradigms: Coordination and Collaboration
• Machine Learning: Hardware-aware ML
• Planning, Routing, and Scheduling: Plan Execution and Monitoring

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Energy consumption is a significant concern for mobile network operators, and to enable further network energy improvements it is also an important target when developing the emerging 6G standard. In this paper we show that, despite the existence of many energy-saving features in 5G new radio (NR) networks, activating them in isolation yields only suboptimal savings and often compromises other network key performance indicators (KPIs) such as coverage or latency. We first introduce a compact taxonomy that distinguishes hardware capabilities from higher-layer features. Features fall into two classes: (i) signaling and scheduling mechanisms that create idle windows, and (ii) features that utilize those windows to save energy. We then present a feature orchestrator as a logical node to coordinate between features to maximize the gain. Using a 3GPP-aligned simulator with product-realistic parameters, we show that coordinating lean NR, scheduling, and advanced sleep modes significantly reduces gNodeB (gNB) energy consumption with negligible throughput loss, compared to the uncoordinated scenario. We conclude by outlining open issues in observability, system dynamics, coordination, and intelligent automation for energy performance management.

Problem

• energy performance management
• 5G NR
• energy-saving features
• network KPIs
• feature coordination

Innovation

• feature orchestrator
• energy performance management
• 6G
• lean NR
• sleep modes