Adaptive Dynamic Programming for 5G Base Station Energy Savings
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for base station switching in 5G infrastructure face challenges in maximizing energy savings without compromising Quality of Service (QoS), often relying on pre-defined performance degradation constraints or complex computational reward/loss functions that fail to adapt to real-time traffic changes.
Innovation Solution
An adaptive dynamic programming (ADP) method using neural network estimators to predict power consumption, QoS, and handovers, allowing for real-time adjustment of QoS targets and thresholds based on historical data, enabling efficient cell switching decisions that balance energy savings and QoS maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If base station switching strategy is implemented to save energy, then power consumption is reduced, but quality of service (QoS) deteriorates
Solution Approach 1:
The patent implements dynamic QoS threshold adjustment based on real-time traffic patterns and historical data. The system continuously adapts the QoS threshold according to current network conditions, allowing flexible trade-offs between energy saving and QoS maintenance. This dynamic adaptation enables the system to achieve near-optimal energy savings while maintaining acceptable QoS levels under varying traffic conditions.
Solution Approach 2:
The system employs feedback mechanisms by collecting real-time traffic data and historical performance information to continuously refine QoS threshold settings. The feedback loop allows the system to learn from past decisions and outcomes, adjusting QoS parameters to optimize the balance between energy consumption and service quality over time.
2Reliability
If pre-defined performance degradation constraints are used for base station switching, then QoS is maintained within acceptable range, but computational complexity increases
Solution Approach 1:
The patent changes the approach by using adaptive QoS thresholds that dynamically adjust based on traffic patterns rather than using fixed pre-defined constraints. This parameter adaptation simplifies the computational process by eliminating the need for complex real-time optimization calculations while maintaining QoS within acceptable ranges through data-driven threshold adjustment.
3Loss of energy
If complex reward or loss functions are used in base station switching, then energy efficiency is optimized, but computational burden increases
Solution Approach 1:
The system uses simple, lightweight QoS threshold parameters that can be quickly adjusted based on historical data patterns, replacing complex reward or loss functions. These simplified parameters provide sufficient guidance for energy-efficient base station switching without the computational burden of complex optimization functions, achieving near-optimal energy savings with reduced computational complexity.
Data Source
AI summary
A method performed by at least one processor of a network device in communication with a plurality of base stations, the method including: receiving historical data collected by one or more base stations from the plurality of base stations, the historical data indicating one or more of a power consumption, handover data, and quality of service (QOS); generating, from the historical data, training data comprising a plurality of cell states and a corresponding random action for each cell state; and training one or more neural network estimators based on the training data, where the one or more neural network estimators comprise one or more of a power consumption estimator, a QoS estimator, and a handover prediction estimator, and where each base station from the plurality of base stations is associated with a respective cell.