Base Station Power Control for Time-Averaged Radio Emissions
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Solution Overview
Problem
Existing solutions for controlling radio emissions from base stations fail to optimize emissions control over longer time windows, leading to excessive reduction in radio emissions and low traffic performance.
Innovation Solution
A method and apparatus for computing a power reduction policy that predicts averaged radio emissions over a time window, using historical data to minimize a cost function by iteratively finding optimal power reduction factors, ensuring compliance with emission limits while minimizing system throughput impact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current emission control solutions are applied to longer time windows, then regulatory compliance is achieved, but traffic performance deteriorates due to overly high reductions of radio emissions
Solution Approach 1:
The system performs preliminary actions by predicting future radio emissions and traffic demand before the time window expires. It computes optimal power reduction policies in advance for each time step, using historical data and forecasting algorithms to anticipate emission patterns, thereby avoiding reactive excessive power reductions that would harm traffic performance.
Solution Approach 2:
The system implements dynamic power reduction policies that adapt to changing conditions within the time window. Instead of applying static power reductions, it continuously adjusts power levels at each time step based on real-time predictions of traffic demand and emission patterns, optimizing the balance between compliance and performance throughout the window.
2Object-generated harmful factors
If power reduction factors are increased to meet emission limits over longer time windows, then radio emissions are controlled, but system throughput decreases
Solution Approach 1:
The system changes parameters dynamically by adjusting power reduction factors at each time step based on predicted conditions. It uses multiple parameters including historical emission data, traffic demand forecasts, and regulatory limits to compute optimal power levels, transforming the static parameter approach into a dynamic multi-parameter optimization system.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual radio emissions and comparing them against predicted values and regulatory limits. It uses this feedback to refine future predictions and adjust power reduction policies, creating a closed-loop control system that learns from past performance to optimize future emission control while maintaining throughput.
Data Source
AI summary
Methods and apparatus are proposed for computing a power reduction policy to control radio emissions of a base station over a time window, with the objective to keep the time-averaged radio emissions over the time window below an authorized value set by regulations, with minimum impact on the performance of the overall system. The time window is subdivided into a plurality of time periods starting at a plurality of time steps, and the policy control comprises, at least at a first time step in the time window: generating a predicted average radio emissions over subsequent time periods in the time window, based on radio emissions historical data; finding couples of values of a power reduction factor and an average radio emissions that minimize a cost function for each time step in the time window, based on the predicted average radio emissions.