Channel Modeling via Generative Adversarial Networks
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Solution Overview
Problem
Current data transmission systems face challenges in optimizing the entire communication system as a single entity, with existing channel modeling methods being inadequate for complex channels, leading to suboptimal performance due to the lack of accurate channel models.
Innovation Solution
Implementing neural networks for both the transmitter and receiver, with end-to-end training using a generative model of the channel, and employing a conditional generative adversarial network to improve channel modeling and system optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If separate design and optimisation of each part of the system is used, then design simplicity is maintained, but system performance is suboptimal
Solution Approach 1:
The patent merges the separate design of transmitter, channel model, and receiver into a unified end-to-end training framework. The generator (channel model) and discriminator (receiver) are trained jointly with the transmitter, allowing the entire communication system to be optimized as a single entity rather than as isolated components, thereby improving overall system performance.
2Ease of manufacture
If existing channel modeling methods are used, then implementation simplicity is maintained, but modeling accuracy is inadequate for complex channels
Solution Approach 1:
The patent replaces traditional mathematical channel modeling approaches with a neural network-based generator that learns channel characteristics through end-to-end training. This substitution allows the system to capture complex non-linear channel behaviors that traditional models cannot represent, significantly improving modeling accuracy for complex channels.
Solution Approach 2:
The patent transforms the channel model from a fixed mathematical representation to a learnable neural network with adjustable parameters. The generator's weights and biases are optimized during end-to-end training to accurately represent the actual channel characteristics, enabling adaptive modeling that changes parameters based on the specific channel conditions.
3Reliability
If the entire system is optimized as a single entity, then system performance is improved, but design and training complexity increases
Solution Approach 1:
The patent employs feedback through the discriminator that provides gradient information to the generator and transmitter during training. This feedback mechanism enables coordinated optimization of all system components by propagating performance information back through the entire system, allowing joint optimization without requiring manual intervention for each component.
Data Source
AI summary
Apparatuses, systems and methods are described including: converting generator inputs to a generator output vector using a generator, wherein the generator is a model of a channel of a data transmission system and wherein the generator comprises a generator neural network; selectively providing either the generator output vector or an output vector of the channel of the data transmission system to an input of a discriminator, wherein the discriminator comprises a discriminator neural network; using the discriminator to generate a probability indicative of whether the discriminator input is the channel output vector or the generator output vector; and training at least some weights of the discriminator neural network using a first loss function and training at least some weights of the generator neural network using a second loss function in order to improve the accuracy of the model of the channel.