Neural Source Coding With Latent Constraints for Unreliable Channels
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Solution Overview
Problem
Conventional wireless communication systems struggle to support high-density machine-to-machine communications, particularly in scenarios involving massive IoT devices, due to challenges in maintaining good quality wireless connections and managing the high volume of generated data.
Innovation Solution
A probabilistic encoder and transmitter system that enforces constraints on distribution parameters of latent spaces using deep neural networks, allowing for efficient transmission of features over unreliable channels by encoding source information into probability distributions and transmitting transmission features without channel coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional wireless communication systems are used to support massive IoT devices, then the system can handle basic communications, but the quality of wireless connections deteriorates and the system cannot manage the high volume of generated data effectively
Solution Approach 1:
The patent transforms the communication approach by changing from transmitting raw sensor data to transmitting compressed feature representations. The encoder network extracts essential features from sensor inputs and transmits only these features, fundamentally altering the data transmission parameters to achieve both high capacity and reliability
Solution Approach 2:
The patent extracts only the most critical information from sensor data by using an encoder to identify and transmit essential features rather than complete data sets. This extraction principle allows the system to manage high-volume data from massive IoT deployments while maintaining connection quality by sending only necessary information
2Quantity of substance
If machine sensors generate high amount of information, then comprehensive data collection is achieved, but the transmission load increases beyond what the channel can reliably support
Solution Approach 1:
The encoder extracts essential features from comprehensive sensor data, separating critical information from redundant data. This allows the system to maintain complete data collection capabilities while transmitting only the extracted essential features, reducing transmission load to match channel capacity
Solution Approach 2:
The patent changes the transmission parameter from raw data volume to feature representation dimensionality. By transforming high-dimensional sensor data into lower-dimensional feature spaces that capture essential information, the system maintains comprehensive data collection while adapting transmission volume to channel reliability constraints
3Productivity
If transmission features are encoded without channel coding, then transmission efficiency is improved, but the features must match or exceed channel entropy to ensure reliable reception
Solution Approach 1:
The encoder performs preliminary compression and feature extraction before transmission, preparing the data in advance to match channel capacity. By pre-processing the data to extract essential features and compressing them to appropriate dimensions, the system eliminates the need for additional channel coding while ensuring reliable reception
Solution Approach 2:
The patent changes the encoding approach from conventional channel coding to neural network-based feature encoding. The encoder learns to produce feature representations with entropy matched to channel capacity, fundamentally changing how information is prepared for transmission to achieve both efficiency and reliability
4Productivity
If compression ratios are increased for classification and detection tasks, then communication efficiency is enhanced, but the precision of information representation may deteriorate
Solution Approach 1:
The patent applies different encoding strategies to different feature dimensions based on their importance for specific tasks. The encoder network learns to allocate representation precision selectively, maintaining high precision for critical features while allowing greater compression for less critical information, achieving both efficiency and precision
Solution Approach 2:
The patent uses task-specific loss functions during training to adapt the encoder's compression behavior. By modifying the training parameters and objective functions based on the intended use (classification, detection, etc.), the system optimizes the balance between compression ratio and representation precision for each specific application
Data Source
AI summary
An apparatus for feature-based communications is provided that includes a probabilistic encoder and a transmitter. The probabilistic encoder is configured to encode source information into a set of probability distributions over a latent space. Each probability distribution represents one or more aspects of a subject of the source information. The transmitter is configured to transmit over a transmission channel, to a receiving electronic device, a set of transmission features representing the subject. Each transmission feature provides information about a respective one of the probability distributions in the latent space. The probabilistic encoder is configured to enforce constraints on distribution parameters of the probability distributions over the latent space based on a condition of the transmission channel. Enforcing constraints on the latent space in this manner enables the apparatus to transmit features that are at least as unreliable as the transmission channel.