Predictive RAN Energy Management Through NTN Traffic Offloading
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Solution Overview
Problem
The telecommunications industry's high energy consumption, exacerbated by network densification, leads to significant greenhouse gas emissions, necessitating a reduction in energy usage and emissions.
Innovation Solution
Implementing predictive analytics to manage energy consumption by offloading traffic to non-terrestrial networks (NTN) and putting terrestrial RAN cells into low power modes based on various triggers, including power utility indicators, subscriber behavior, and network events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If network densification is implemented by adding more base stations, then network coverage and capacity are improved, but energy consumption increases
Solution Approach 1:
The patent implements dynamic power management by transitioning base stations between active and low-power states based on real-time traffic conditions. The system monitors traffic patterns and automatically adjusts base station power states, allowing the network to adapt its energy consumption dynamically rather than operating all base stations at full capacity continuously.
Solution Approach 2:
The system employs periodic traffic analysis and prediction to determine optimal power states for base stations. By analyzing traffic patterns over time and predicting future traffic conditions, the system can periodically adjust base station power states in advance, reducing energy consumption during low-traffic periods while maintaining readiness for high-traffic periods.
2Use of energy by moving object
If predictive analytics are used to offload traffic to NTN, then energy consumption is reduced, but network complexity increases
Solution Approach 1:
The patent introduces a traffic analytics system as an intermediary component that sits between the terrestrial network and NTN. This intermediary analyzes traffic patterns, predicts future traffic conditions, and makes intelligent decisions about when to offload traffic to NTN, thereby reducing the complexity burden on individual base stations while enabling energy-efficient operation.
Solution Approach 2:
The system performs preliminary traffic analysis and prediction to proactively determine optimal offloading decisions before traffic conditions require action. By analyzing traffic patterns in advance and predicting future conditions, the system can prepare and execute offloading decisions optimally, reducing the need for complex real-time decision-making during critical moments.
3Reliability
If base stations operate continuously to maintain coverage, then service reliability is maintained, but energy consumption increases
Solution Approach 1:
The system uses traffic prediction to anticipate future traffic conditions and proactively adjusts base station power states before changes occur. By analyzing historical traffic patterns and predicting future conditions, the system can put base stations into low-power states in advance when traffic is expected to be low, and wake them up beforehand when traffic is expected to increase, thereby maintaining service reliability while reducing energy consumption.
Solution Approach 2:
The patent implements feedback mechanisms where base stations and the central controller continuously monitor actual traffic conditions and compare them against predicted patterns. This feedback loop allows the system to adjust power states based on actual performance, ensuring that base stations remain operational when needed while entering low-power states when traffic conditions justify the transition, thus balancing reliability and energy efficiency.
Data Source
AI summary
Method and system for reducing greenhouse gas emissions of a terrestrial wireless telecommunications network are disclosed. The system monitors a current energy consumption metric of the terrestrial network, predicts an energy consumption metric of the terrestrial network in a future time period based on at least one triggering event, and determines an available capacity metric of a non-terrestrial network in the future time period. When the energy consumption metric of the terrestrial network is greater than a first threshold and the available capacity metric of the non-terrestrial network is greater than a second threshold, the system causes the terrestrial network to enter an energy conservation mode of operation by transferring a first call or a first data session from a RAN cell of the terrestrial network to the non-terrestrial network and further causes the RAN cell to enter a low power mode of operation.