Complex-Valued Neural Network Channel Equalizer Design
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Solution Overview
Problem
Existing channel equalization methods in digital communication systems are ineffective in addressing inter-code crosstalk and noise interference due to their inability to efficiently handle complex-valued signals and optimize training algorithms for neural networks.
Innovation Solution
A method is proposed that uses a multi-layer feedforward complex-valued neural network with an adaptive complex-valued L-BFGS algorithm for efficient training, optimizing the loss function and determining the optimal memory size to implement channel equalization in digital communication systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional channel equalization methods are used, then the system structure is simple, but the ability to handle complex-valued signals and eliminate inter-code crosstalk is insufficient
Solution Approach 1:
The patent replaces conventional mechanical/mathematical equalization algorithms with a complex-valued neural network system. The neural network learns optimal equalization parameters through training on complex-valued signal data, substituting traditional algorithmic approaches with a data-driven model that automatically adapts to channel characteristics and eliminates inter-code crosstalk more effectively
Solution Approach 2:
The patent transforms the equalization problem from fixed-parameter conventional algorithms to adaptive parameter learning through neural network training. The complex-valued neural network adjusts its weights and biases based on training data, enabling dynamic optimization of equalization performance for varying channel conditions rather than relying on predetermined parameters
2Reliability
If complex-valued neural network is used for channel equalization, then the equalization performance is improved, but the training complexity and computational burden increase
Solution Approach 1:
The patent segments the training process into distinct phases: data preparation with artificial noise injection, forward propagation through the complex-valued neural network, loss calculation using mean squared error, and gradient-based parameter updates. This segmentation of the training algorithm into manageable stages reduces implementation complexity and allows for targeted optimization of each component
Solution Approach 2:
The patent implements feedback through the loss function calculation and gradient descent mechanism. The mean squared error between predicted and actual signals provides continuous feedback during training, guiding the optimization of neural network parameters. This feedback loop enables automatic adjustment of weights and biases to minimize equalization error without requiring manual intervention
3Productivity
If traditional training algorithms are used, then the computational complexity is low, but the convergence speed and training efficiency are insufficient
Solution Approach 1:
The patent employs dynamic optimization through gradient descent and adaptive learning rate mechanisms. Rather than using fixed-step conventional algorithms, the training process dynamically adjusts parameter updates based on the gradient of the loss function and the curvature of the error surface. This dynamic approach accelerates convergence by adapting the training trajectory to the specific characteristics of the complex-valued neural network and data distribution
Data Source
AI summary
The present invention discloses a method for the design of complex-valued channel equalizer of digital communication systems, including: constructing a channel equalizer by using a complex-valued neural network; collecting the output signal y(n)=[y(n), y(n−1), . . . , y(n−m+1)]T of the nonlinear channel of the underlying digital communication system as the input of complex-valued neural network and s(n−τ) as the desired output, and taking the mean squared error as the loss function for the training of complex-valued neural network, which is optimized by the proposed adaptive complex-valued L-BFGS algorithm, and finally using it to implement the design of channel equalizer for digital communication systems. The present invention proposes the use of a multi-layer feedforward complex-valued neural network to construct complex-valued channel equalizer. A new adaptive complex-valued L-BFGS algorithm is proposed for efficient training of complex-valued neural network, which is eventually applied to facilitate the design of the channel equalizer for digital communication systems.