Controlled Corrupted Information for Wireless Network Training
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current wireless communication systems face challenges in effectively managing and detecting corrupted information, which can impact the accuracy of machine learning models used in network management, leading to reduced sensitivity and increased false positives, affecting signal quality and data throughput.
Innovation Solution
The method involves transmitting a corruption configuration indication to specify a corruption parameter for generating controlled corrupted information, which is used to train detection algorithms to identify and mitigate uncontrolled corrupted data, thereby preventing erroneous changes to network management algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If machine learning models are used in network management, then network automation and efficiency are improved, but the models become vulnerable to corrupted information that reduces accuracy and increases false positives
Solution Approach 1:
The patent applies preliminary action by generating controlled corrupted information in advance and storing it in a database before it is needed for training. The corruption configuration indications are predetermined and sent to user equipment beforehand, allowing the system to prepare training data with known corruption patterns before the machine learning model training process begins. This pre-preparation ensures that the model can learn to recognize and handle corrupted information without compromising accuracy during actual network management operations.
2Measurement precision
If controlled corrupted information is generated and transmitted, then detection algorithms can be trained to identify corrupted data, but additional signaling overhead and system complexity are introduced
Solution Approach 1:
The patent applies parameter changes by systematically varying corruption configuration indications such as corruption types, corruption levels, and data characteristics to generate diverse training scenarios. Different corruption parameters (e.g., bit error rates, packet loss patterns, signal interference levels) are adjusted to create multiple training datasets, allowing the detection algorithm to learn robust recognition patterns across various corruption conditions without requiring complex manual intervention for each scenario.
Solution Approach 2:
The patent applies universality by designing a multi-functional corruption configuration indication mechanism that serves multiple purposes: it generates controlled corrupted information for training, tracks corruption patterns for analysis, and provides a standardized interface for different types of data corruption. This universal approach allows the same system components to handle various corruption scenarios (signal corruption, data corruption, transmission errors) without requiring separate specialized systems for each corruption type, thereby reducing overall system complexity.
3Loss of information
If corruption parameters are configured and transmitted to user equipment, then controlled corrupted information can be generated for training purposes, but signaling overhead increases
Solution Approach 1:
The patent applies partial action by transmitting only the essential corruption configuration indications to user equipment rather than complete training datasets. The network node sends compact corruption parameters (such as corruption type identifiers, severity levels, and data characteristics) that enable the user equipment to locally generate the actual corrupted information. This partial signaling approach reduces overhead compared to transmitting full corrupted datasets while still providing sufficient information for generating diverse training scenarios.
Data Source
AI summary
Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a network node may transmit a corruption configuration indication that specifies a corruption parameter associated with generating controlled corrupted information. The network node may receive the controlled corrupted information that is based at least in part on the corruption parameter. Numerous other aspects are described.