AI Pattern Recognition for 5G Channel Parameter Analysis
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Solution Overview
Problem
Conventional over the air channel analysis in cellular networks is hindered by the complexity and variability of 5G NR channels, which require time and computational resources for frequency and time measurements, making it difficult to diagnose and maintain channel performance efficiently.
Innovation Solution
The implementation of artificial intelligence (AI)/machine learning (ML) pattern recognition in test devices for over the air channel analysis, allowing for the detection of signal shapes and determination of channel parameters such as center frequency, bandwidth, and technology without the need for extensive frequency and time measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional frequency and time measurements are used for channel analysis, then measurement precision is improved, but loss of time increases and productivity decreases
Solution Approach 1:
The patent replaces conventional mechanical measurement systems (frequency and time measurements) with an AI/ML-based pattern recognition system. The test device captures channel signals and uses trained neural networks to directly determine channel parameters (center frequency, bandwidth, technology type) from signal patterns, eliminating the need for time-consuming frequency sweeps and time measurements while maintaining accurate parameter identification
Solution Approach 2:
The patent applies preliminary action by pre-training AI/ML models with extensive channel signal data before deployment. The neural networks are trained offline on various channel conditions, signal types, and parameter combinations, so that during actual field measurements, the already-trained models can rapidly identify parameters without performing exhaustive measurements, thus saving time while maintaining precision
2Measurement precision
If conventional measurement methods are used, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent replaces complex conventional measurement instrumentation (spectrum analyzers, vector signal analyzers with multiple measurement functions) with a simplified AI-based processing system. Instead of using multiple specialized measurement tools and complex signal processing algorithms, the system uses a single trained neural network model that can identify multiple channel parameters simultaneously from a single signal capture, reducing device complexity while maintaining measurement precision
3Productivity
If AI/ML pattern recognition is used, then productivity is improved and loss of time is reduced, but measurement precision may worsen
Solution Approach 1:
The patent ensures measurement precision is maintained by performing preliminary training of AI/ML models with extensive, diverse channel signal data that covers various technologies (5G NR, 4G LTE, Wi-Fi), channel conditions, and parameter ranges. This offline preparation creates highly accurate models that can rapidly predict parameters with high precision during field operations
Solution Approach 2:
The patent implements feedback mechanisms where the AI/ML system continuously refines its predictions based on comparison with known channel characteristics and measurement validation. The system can cross-validate predicted parameters against signal features and adjust predictions to maintain accuracy, ensuring that rapid analysis does not compromise measurement precision
Data Source
AI summary
Methods and systems for over the air channel analysis through artificial intelligence (AI)/machine learning (ML) aided pattern recognition are provided. A shape of a signal (e.g., control signal) may be detected through image analysis using AI/ML and technology of signal in a channel of interest may be determined from the shape. Next, the control channel (SSB, SIB1) may be determined based on period. A user (e.g., a technician) may be provided with center frequency and bandwidth for LTE or SSB, SIB1 for NR to assist with diagnosis and maintenance of the corresponding channel. Furthermore, channel analysis (e.g., EVM, SNR, time error) may be performed without demodulation and provided to the user. Moreover, massive MIMO performance may also be measured based on power difference between broadcast beam and user beam for data throughput evaluation.