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Real-time interference mitigation for 5G RF receivers using AI–Simulink integration and deep learning models

adaptive-and-self-organizing-systems
real-time-and-embedded-systems
software-engineering
neural-networks
internet-of-things

Abstract

In modern 5G wireless communication systems, interference from co-channel signals, hardware non-linearities, and external jamming sources poses a major challenge to signal fidelity and receiver performance. This study presents a hybrid simulation and deep learning framework for interference mitigation in RF receiver chains using MATLAB Simulink and a Deep Neural Network (DNN). A full 5G RF receiver chain was modeled in Simulink, including QPSK modulation, Additive White Gaussian Noise (AWGN), and artificial sinusoidal jamming signals injected at 3 kHz and 5.5 kHz. The model produced over 10,000,001 samples of clean and noisy signals at a 1 MHz sampling rate. These signals were used to train a regression-based DNN in MATLAB, targeting accurate recovery of clean baseband data from distorted RF outputs. After training, the AI filter achieved an SNR improvement from 7.92 dB to 29.79 dB, with an RMSE below 0.027. Time-domain and frequency-domain plots confirmed accurate signal restoration, including waveform tracking, FFT analysis, and zoomed signal segments. The results demonstrate the capability of deep learning to generalize interference characteristics and restore signal integrity under dynamic noise environments, providing a scalable and adaptive solution for interference cancellation in real-time 5G applications.
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