Improving Wireless Signal Quality: Which AI Models Best Predict Signal-to-Noise Ratio?

What is it about?

This research evaluates how different Artificial Intelligence (AI) models, specifically Convolutional Neural Networks (CNNs), can accurately predict the Signal-to-Noise Ratio (SNR) in modern wireless systems like 5G. Accurate SNR estimation is vital for high-speed data transmission and energy efficiency, yet traditional mathematical methods often fail in complex, real-world environments. Our study shifts the focus from simple classification to regression-based deep learning, which allows for more precise, continuous measurements of signal quality. We compared six distinct AI architectures, ranging from the lightweight MobileNetV2 to the more complex DenseNet-201. By testing these models against various QAM-modulated signals and mismatched channel conditions, we identified which specific neural network features such as skip connections in ResNet and depthwise separable convolutions in Xception, provide the most reliable and stable performance in non-ideal communication environments.

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Photo by Logan Voss on Unsplash

Photo by Logan Voss on Unsplash

Why is it important?

Accurate Signal-to-Noise Ratio (SNR) estimation is the backbone of efficient wireless communication. In the transition to 5G and 6G networks, even minor errors in signal quality assessment can lead to dropped calls, slow data speeds, and high energy waste. This study is important because it proves that AI-driven regression models for example, those with advanced architectures like ResNet and Xception, can maintain reliability even when the signal conditions change unexpectedly (known as covariate shift). By demonstrating that deep learning can outperform traditional mathematical methods in complex, real-world environments, this research provides a roadmap for engineers to build more resilient and self-optimizing communication systems. Ultimately, these findings help ensure that future wireless networks are more robust, supporting everything from autonomous vehicles to global mobile connectivity.