Two-Sided Model Error Source Detection in Wireless UE Encoders
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Solution Overview
Problem
Conventional methods for monitoring two-sided models in wireless networks are unable to efficiently identify the specific sources of performance deficiencies, limiting their effectiveness in resolving model performance issues.
Innovation Solution
Implement a process to determine possible sources of error in two-sided models by utilizing a network-side encoder model and various monitoring techniques, including the exchange of reference input sets and statistical comparisons, to pinpoint errors in deployment, data shift, or communication links.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional monitoring methods are used for two-sided models, then the monitoring process is simple, but the ability to identify specific error sources is insufficient
Solution Approach 1:
The monitoring system is segmented into multiple specialized components: a data exchange module for reference sample transmission, a statistical parameter calculation module for computing means and covariances, a hypothesis testing module for GLR tests, and an error source identification module. Each component handles a specific aspect of error detection, allowing the system to achieve high measurement precision through distributed specialized functions rather than a single complex monolithic system.
Solution Approach 2:
Reference samples serve as an intermediary between the training data and the monitoring process. These reference samples are exchanged between network entities and used to establish baseline statistical parameters. This intermediary mechanism enables the system to detect deviations from expected behavior without requiring direct access to the original training data, thus improving error source identification while maintaining system modularity.
2Reliability
If detailed error analysis is performed to identify specific error sources, then the reliability of model performance monitoring is improved, but the computational overhead and time consumption increase
Solution Approach 1:
Statistical parameters (means and covariances) are pre-calculated from reference samples before actual error detection begins. These pre-computed parameters are stored and reused during monitoring operations, eliminating the need to recalculate them repeatedly. This preliminary action significantly reduces the computational overhead and time required for each error detection event while maintaining high reliability through accurate baseline comparisons.
Solution Approach 2:
The system replaces complex mechanical error analysis with statistical hypothesis testing. Instead of performing detailed mechanistic analysis of model failures, the system uses Generalized Likelihood Ratio (GLR) tests and statistical parameter comparisons to automatically identify error sources. This substitution of statistical methods for mechanical analysis reduces computational complexity and time consumption while improving reliability through objective quantitative metrics.
3Measurement precision
If reference samples are exchanged between network entities to improve error detection accuracy, then the precision of error source identification is improved, but the communication overhead and network resource consumption increase
Solution Approach 1:
The system extracts and exchanges only the essential elements needed for error detection: reference samples and their derived statistical parameters. Rather than transmitting complete datasets or model weights, only the minimal necessary information (reference samples and pre-computed statistics) is exchanged over the network. This extraction approach maintains high error detection accuracy while minimizing communication overhead and energy consumption.
Solution Approach 2:
The system transforms reference samples into compact statistical parameters (means and covariances) for network transmission. By changing the representation from raw data samples to condensed statistical descriptors, the system achieves the same error detection accuracy with significantly reduced communication bandwidth requirements and lower energy consumption. The statistical parameters capture the essential characteristics needed for GLR testing without requiring transmission of large volumes of raw data.
Data Source
AI summary
Various aspects of the present disclosure relate to a UE comprising at least one memory and at least one processor coupled with the at least one memory and configured to cause the UE to implement a first encoder of a two-sided model trained by a set of reference samples, determine, using a first set of information and at least one of the first encoder or input data, at least one possible source of error associated with the two-sided model, and transmit a message indicating the at least one possible source of error to a network entity.