Why AI-Powered RAN Is an Energy Efficiency Breakthrough - Seite 3

One example is a renowned CSP that was exploring avenues to optimize RAN energy consumption without degrading network performance and customer experience. In the current scenario Ericsson provides static thresholds to be set manually through the cell sleep mode feature, however, the team proposed setting thresholds dynamically. To determine the values for this threshold dynamically, it was necessary to conduct field experiments on a live network as there was no variation in data due to static nature in CSM.

The image above portrays the following:

1. High Level Solution: Energy consumption forecasting model

2. Methodology: Optimal configuration threshold grid search model

3. Model Fitting: Threshold validation on live network

4. Optimised Scenario: Impact analysis

The forecasting model predicts energy consumption levels per cell for a day in advance. It provides an idea for the optimization model for possible improvements in terms of various performance KPIs like accessibility (QCI9, QCI5 & QCI1), retainability, mobility, latency, throughput and traffic volume.

The optimization model determines the dynamic threshold at cell level at which a capacity cell should be awakened or put to sleep based on RRC Connections and PRB Utilization. In this case, it was considered as a convex optimization problem, with the objective function to maximize the sleep hours subjected to various constraints like business constraints (for example user experience or operational expenditure) or technical constraints (such as accessibility during coverage hole detection, drop call rate, average sector loading, location estimation, mobility, and lower latency handling in case of URLLC use cases).

Impact of the solution

• Target exceeded: 10-12 percent energy reduction across pilot sites
• No degradation in RRC, ERAB Success Rate and Call Drop Rate across all bands
• Stable traffic volume, mobility success rate and latency
• Stable DL/UL Throughput and other primary KPIs against historical trend

Our research shows a few more areas that operators can focus on reducing energy consumption

• MIMO Sleep: ML-enabled MIMO path and radio head control for energy savings using optimal MIMO configuration resulting in optimal power efficiency and performance balance for various traffic conditions. This learns power savings and network performance under various traffic loads, then activates and deactivates MIMO paths according to trained models.
• IoT based energy optimization: Smart Homes, Smart Factories & equipments, Smart Health monitoring, shipping & Logistics etc., are utilizing various interconnected devices to autonomously manage intent operations, which consumes a lot of energy, resulting in a need for energy optimization. A function of IoT devices is to reliably collect and share the perceived data with the physical world. The hardware element of the IoT device consists of a battery-powered sensor, an actuator, and a communication system. IoT sensors, cloud computing technologies and the telco network with the help of AI based techniques (events and data driven), improve productivity and energy efficiency.
• Smart green sourcing: Green Sourcing refers to the purchase of goods and services that cause minimal adverse environmental impact. The demand for recyclable products, energy-efficient systems, and clean technology and fuels is driving the adoption of ecologically responsible business norms. In green sourcing, concerns about environmental impact are given weight over other business decisions to reduce pollution. Sunsetting legacy systems and equipment is key in bringing sustainable smart green sourcing.
• Machine learning algorithms for HetNet traffic pattern: Energy-aware platforms analyze events through AI/ML and reinforcement learning (RL), to allow proactive energy savings coupled with reduced CO2 emissions in Heterogeneous Network (HetNet) architecture. Advanced frameworks for optimally handling the switching on or off of sleeping cells, in case of low latency services without impacting the QoS, take mobility prediction, UE location estimation and network environment into consideration.

Summary