Synthetic Wireless Channel Data With Latent-Space Attribute Control
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Solution Overview
Problem
Existing methods for generating synthetic wireless channel data are limited by the availability of measured data in a limited number of frequency bands, leading to insufficient testing of new measurement and communication equipment.
Innovation Solution
A method and system for generating synthetic wireless channel data using a latent space representation, where user-defined mutation parameters control the attributes that are boosted, dampened, or frozen, allowing only specific channel attributes to mutate, and utilizing neural networks like GANs to create realistic RF signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real world measurements are used to create test sets, then the data reflects actual channel conditions, but the limited number of available measurements restricts the coverage of frequency bands and scenarios
Solution Approach 1:
The patent creates synthetic copies of real wireless channel measurements through neural network generation. The system learns from limited real measurements and generates additional synthetic channel data that replicates the statistical properties and characteristics of real channels, thereby expanding frequency band coverage without requiring extensive additional physical measurements
Solution Approach 2:
The patent transforms channel data into a latent space representation and manipulates parameters in this transformed domain. By changing parameters in the latent space and using mutation operators, the system generates diverse channel scenarios across different frequency bands while maintaining the essential characteristics of real wireless channels
2Adaptability or versatility
If more real world measurements are collected to expand frequency band coverage, then the versatility of test data improves, but the time and resources required for measurements increase
Solution Approach 1:
The patent performs preliminary learning from a small set of real measurements to train the neural network model. Once trained, the system can rapidly generate synthetic channel data for any desired frequency band without requiring additional time-consuming physical measurements, thus achieving broad coverage efficiently
Solution Approach 2:
Instead of performing new measurements for each frequency band, the system creates synthetic copies of channel data through neural network generation, dramatically reducing the time and resources needed to expand frequency band coverage while maintaining data versatility
3Adaptability or versatility
If synthetic data is generated without constraints, then the diversity of generated scenarios increases, but the ability to control specific channel attributes is reduced
Solution Approach 1:
The patent applies different mutation strategies to different channel attributes based on user specifications. Certain attributes can be frozen to maintain specific characteristics, while others are allowed to mutate to generate diversity. This localized control approach enables both attribute control and scenario diversity simultaneously
Solution Approach 2:
The system dynamically adjusts the mutation process based on user input and desired outcomes. The mutation strength, target attributes, and generation parameters can be adapted in real-time to balance between maintaining specific channel characteristics and generating diverse test scenarios
Data Source
AI summary
The present disclosure relates to a method for generating synthetic wireless channel data, comprising: proving wireless channel data in a latent space, wherein the wireless channel data comprises a plurality of datasets, wherein each dataset represents channel characteristics of a wireless communication channel and comprises a plurality of channel attributes; receiving a user input which defines at least one channel attribute; mutating the wireless channel data, wherein during said mutation only channel attributes of wireless channel data other than the at least one channel attribute defined by the user input are allowed to mutate; and generating synthetic wireless channel data based on the mutated wireless channel data in latent space.