Stochastic Transmitter Algorithm for Information Rate Optimization
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Solution Overview
Problem
Current communications systems face limitations in maximizing information transfer rate due to challenges in mapping bits to constellation symbols that occur with target probabilities, especially when a probability distribution is identified to optimize the information rate.
Innovation Solution
The implementation of a stochastic transmitter algorithm with trainable parameters, utilizing a neural network to convert inputs into data symbols and a receiver algorithm to demap these symbols, which updates parameters to minimize divergence from a target distribution and maximize information transfer rate, using methods like stochastic gradient descent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a probability distribution is identified to optimize information rate, then information transfer rate is improved, but the complexity of mapping bits to constellation symbols increases
Solution Approach 1:
The patent changes the parameters of the transmitter algorithm by introducing trainable parameters that define a probability distribution for mapping bits to constellation symbols. This allows the system to optimize the information transfer rate by adjusting these parameters to match a target probability distribution, resolving the contradiction between improved productivity and increased complexity through parameter optimization rather than structural complexity
Solution Approach 2:
The patent replaces traditional deterministic mapping mechanisms with a stochastic transmitter algorithm that uses probability distributions. This substitution allows the system to achieve optimal information transfer rates through statistical methods rather than complex deterministic mapping rules, reducing the effective complexity while improving productivity
2Productivity
If stochastic transmitter algorithm with trainable parameters is used, then information transfer rate is maximized, but the complexity of the transmission system increases
Solution Approach 1:
The transmitter algorithm performs self-service by automatically training its own parameters to optimize the probability distribution for symbol mapping. The system uses gradient-based optimization methods to self-adjust the trainable parameters, eliminating the need for external complex configuration mechanisms and reducing overall system complexity while maximizing information transfer rate
Solution Approach 2:
The patent implements feedback mechanisms where the transmitter algorithm uses information about the channel and received symbols to update its trainable parameters. This feedback loop allows the system to automatically adapt and optimize performance without requiring complex external control, resolving the contradiction between improved productivity and system complexity
Data Source
AI summary
An apparatus, method and computer program is described including circuitry configured for using a transmitter algorithm to convert one or more inputs at a transmitter of a transmission system into one or more data symbols, wherein: the transmission system includes the transmitter implementing said transmitter algorithm, a channel and a receiver including a receiver algorithm; the transmitter algorithm includes trainable parameters for converting one or more received data symbols into one or more outputs; and the transmitter algorithm is stochastic.