Monte Carlo Sampling Interrupt Mechanism for 6G Systems
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Solution Overview
Problem
Monte Carlo sampling methods are time-consuming, especially in complex scenarios like 6G communication systems, due to the need for numerous repeated random trials to estimate rare-event probabilities, necessitating a faster approach.
Innovation Solution
An interrupt mechanism is introduced that allows for early termination of Monte Carlo sampling based on the number of unfavorable events collected, using a control parameter to determine when to stop or increase output, thereby reducing the sampling rounds and accelerating the estimation process without relying on prior knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Monte Carlo sampling is performed with numerous repeated random trials to estimate rare-event probabilities, then estimation accuracy is improved, but sampling time increases tremendously
Solution Approach 1:
The patent implements a feedback mechanism by continuously monitoring the number of unfavorable events collected during Monte Carlo sampling and comparing it against a dynamically calculated threshold. This threshold is derived from the relationship between the number of unfavorable events, total measurements, and a preset probability threshold. When the monitoring indicates sufficient sampling (i.e., the collected unfavorable events meet the threshold criteria), the system provides feedback to terminate the sampling process early, thus resolving the contradiction between estimation accuracy and sampling time by adaptively stopping when sufficient statistical evidence is obtained.
Solution Approach 2:
The patent applies dynamics by making the sampling process adaptive rather than static. The sampling termination criterion is not fixed but dynamically adjusted based on the actual sampling progress and the relationship between unfavorable events and total measurements. This dynamic approach allows the system to terminate sampling early when the statistical regularity is sufficiently approached, or continue sampling when more measurements are needed, thereby optimizing the balance between accuracy and time consumption for different rare-event scenarios.
2Measurement precision
If the number of independent measurements is increased to collect sufficient unfavorable events, then probability measure estimation accuracy is improved, but the total number of measurements required increases
Solution Approach 1:
The patent uses feedback to monitor the ratio of unfavorable events to total measurements and compares it against a preset probability threshold. This continuous monitoring provides feedback on whether sufficient sampling has been achieved, allowing the system to terminate measurements early when the statistical regularity is sufficiently approached, thus reducing the total number of measurements needed while maintaining estimation accuracy.
Solution Approach 2:
The patent implements partial action by terminating the Monte Carlo sampling process before completing the full predetermined number of measurements. By using the interrupt mechanism to stop sampling when the collected unfavorable events meet the threshold criteria, the system performs only the necessary portion of measurements required to achieve sufficient estimation accuracy, avoiding excessive sampling that would increase the total number of measurements without proportionally improving accuracy.
Data Source
AI summary
A method for estimating a status of a system includes performing independent measurements on the system, associating a value P(γ) with a Monte Carlo calculated probability measure P(γ), which depends on (1) a preset threshold Nu of unfavorable events, and (2) a total number Ñtot(γ) of the independent measurements for collecting the preset threshold Nu, setting a threshold level η requiring that a critical value γc of a power related metric γ makes a value P(γc) of the probability measure P to be substantially equal to the threshold level η, comparing the total number Ñtot(γ) to a ratio of (1) a product of a control parameter ϵ the preset threshold Nu, and (2) the threshold level η, and interrupting the independent measurements if Ñtot(γ) is larger than the ratio, or increasing an output of the system if Ñtot(γ) is smaller than or equal to the ratio.