HYBRID DEEP LEARNING FOR 5G SIGNAL PROCESSING: LSTM-CNN FOR CHANNEL ESTIMATION AND INTERFERENCE MITIGATION

Abstract

This paper presents a hybrid deep learning framework for 5G signal processing, integrating Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN) for enhanced channel estimation and interference mitigation. As 5G networks continue to grow, accurate channel estimation and effective interference management are crucial for maintaining high-speed, low-latency communication. The proposed framework leverages LSTM to model temporal dependencies and CNN to extract spatial features, ensuring precise estimation of the communication channel. It also mitigates interference from neighboring cells and noise, improving overall network performance. Experimental results show a significant reduction in key performance metrics: the Mean Squared Error (MSE) for channel estimation is 0.02, demonstrating high accuracy. The Signal-to-Interference-plus-Noise Ratio (SINR) improves by 15 dB, and the Interference Reduction Ratio (IRR) shows a 30% reduction in interference compared to traditional methods. The framework operates efficiently with a processing latency of 50 ms per frame, making it suitable for real-time applications. Furthermore, the generalization error on unseen data is 0.05, confirming the model's robustness and adaptability. The proposed hybrid LSTM-CNN framework offers a promising solution for reliable 5G communication, enhancing signal quality and mitigating interference in dynamic environments.