User Equipment Idle Period Distribution Testing
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Solution Overview
Problem
Current testing methods for user equipment (UE) in wireless systems do not accurately verify the distribution of idle periods within a contention window, leading to potential performance issues when multiple wireless systems operate concurrently, as they only check if idle periods are within a specified range without ensuring fair distribution, allowing manufacturers to manipulate short idle periods for favorable access.
Innovation Solution
A testing approach that collects a predetermined number of idle periods (e.g., 10,000) to analyze their distribution across bins with varying durations and probabilities, ensuring that short idle periods are thoroughly tested while long idle periods are less scrutinized, thereby determining a UE's pass or failure status based on compliance with standards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current testing methods only check if idle periods are within a specified range, then the testing process is simple and fast, but the distribution accuracy is poor and cannot prevent manipulation of short idle periods
Solution Approach 1:
The contention window is divided into multiple bins with varying durations and probabilities. Each bin represents a specific idle period range, allowing the testing method to segment the verification process into discrete, manageable portions that can be individually analyzed for distribution compliance
Solution Approach 2:
Different bins are assigned different scrutiny levels based on their duration and probability characteristics. Short idle period bins receive more rigorous testing with higher expected counts, while long idle period bins receive less scrutiny. This local differentiation ensures that manipulation of short idle periods is detected while maintaining testing efficiency
2Measurement precision
If a predetermined number of idle periods (e.g., 10,000) are collected for analysis, then the distribution verification accuracy is improved, but the testing time and computational resources increase
Solution Approach 1:
The method collects a predetermined number of idle periods (e.g., 10,000) which may be more than strictly necessary for basic verification, but this excessive sampling ensures high statistical confidence in the distribution analysis. The large sample size compensates for the time investment by enabling more efficient bin-based processing and reducing the need for repeated testing
Solution Approach 2:
The idle periods are collected and stored in bins before the actual distribution analysis is performed. This preliminary organization of data into binned categories allows for efficient processing during the verification phase, reducing computational overhead and enabling faster analysis of the collected samples
3Reliability
If short idle periods are thoroughly tested with higher scrutiny, then the detection of manipulation is improved, but the overall testing complexity and resource allocation become more difficult
Solution Approach 1:
The testing system applies different scrutiny levels to different parts of the idle period distribution. Short idle period bins, which are more susceptible to manipulation, receive higher expected counts and more rigorous statistical testing. Long idle period bins receive proportionally less scrutiny. This localized quality approach concentrates testing resources where they are most needed
Solution Approach 2:
The expected count parameter is varied across different bins based on their characteristics. Shorter bins with higher manipulation risk are assigned higher expected counts, requiring more samples to pass the distribution test. This parameter differentiation creates a weighted testing approach that adapts to the risk profile of each bin
Data Source
AI summary
Embodiments include apparatuses, methods, and systems that may test a UE for its idle period distribution. A test system may identify a set of bins in which a union of the set of bins may be equal to a contention window, wherein each individual bin of the set of bins may have an associated probability. A first bin of the set of bins may have a first associated probability, and a second bin of the set of bins may have a second associated probability that is larger than the first associated probability. Each individual idle period may be assigned to a corresponding bin of the set of bins. A UE may have a pass status or a failure status based on the individual idle periods assigned to the corresponding bin of the set of bins, and the associated probability for the bin. Other embodiments may also be described and claimed.