Self-Supervised RF Signal Embedding for Antenna Data Saturation
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Solution Overview
Problem
Digital antenna arrays face data saturation issues due to high data rates from wideband RF signals, leading to potential loss of signal information, especially when using beamforming, which does not effectively exploit spatial and spectral redundancies in RF signals.
Innovation Solution
A method involving pretraining a machine learning network using unlabeled data samples to generate embeddings, which are then used to train a downstream network for signal processing tasks, leveraging self-supervised learning to reduce data dimensionality and improve processing efficiency without significant loss of signal content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If beamforming is used to process RF signals from antenna arrays, then signal reception performance is improved, but spatial and spectral redundancies are not effectively exploited leading to data saturation
Solution Approach 1:
The patent extracts and removes redundant information from RF signals by identifying and eliminating spatial and spectral redundancies. The system separates useful signal information from redundant data, keeping only the essential components for processing while discarding duplicate information that contributes to data saturation.
Solution Approach 2:
The patent creates a universal embedding representation that serves multiple downstream signal processing tasks simultaneously. This multi-functional embedding can be used for various applications including but not limited to beamforming, making the system more efficient by avoiding redundant processing for each individual task.
2Speed
If high data rates are used to transmit wideband RF signals, then signal bandwidth is improved, but data saturation occurs leading to loss of signal information
Solution Approach 1:
The patent transforms the high-dimensional RF signal data into a lower-dimensional embedding space while preserving essential signal information. By changing the dimensionality from the original high-rate data representation to a compressed embedding representation, the system maintains signal integrity while avoiding data saturation.
Solution Approach 2:
The patent changes the parameter representation of RF signals by transforming time-frequency domain data into embedding vectors with different statistical properties. This parameter transformation allows the system to capture essential signal characteristics in a compressed form that avoids saturation while preserving information.
3Measurement precision
If labeled data is used to train machine learning networks for signal processing, then task-specific performance is improved, but data annotation requirements and training time increase
Solution Approach 1:
The patent performs preliminary action by pretraining the machine learning network on unlabeled RF signal data before fine-tuning on labeled data. This preliminary pretraining phase allows the network to learn general signal representations without requiring time-consuming labeled data annotation, reducing overall training time while maintaining task-specific performance.
Solution Approach 2:
The patent implements self-service learning by using the network's own predictions and the structure of the data itself to create training signals from unlabeled data. The system serves its own training needs by generating pseudo-labels or using self-supervised objectives, eliminating the need for external labeled data and reducing annotation requirements.
Data Source
AI summary
A method for processing radio frequency (RF) signals is provided. The method includes receiving one or more RF signals from one or more antenna channels. The method includes obtaining, from the one or more RF signals, a plurality of unlabeled data samples. The method includes generating an input tensor representation of the plurality of data samples. The method includes pretraining a first machine learning network using the input tensor representation to obtain one or more embeddings. The method includes training a second machine learning network using the one or more embeddings. The second machine learning network is configured to perform one or more signal processing tasks. Also provided is a system having an antenna array and one or more processors.