AI-Assisted Signal Processing Technologies for 5G Infrastructure

Abstract

The advent of 5G networks has revolutionized telecommunications, offering unprecedented speed, ultra-low latency, and massive connectivity. However, realizing these capabilities requires sophisticated signal processing to overcome complex channel conditions, interference, and hardware impairments. AI-powered signal processing techniques have emerged as a transformative solution, leveraging machine learning (ML) and deep learning (DL) models to enhance performance across various signal processing tasks such as channel estimation, interference management, beamforming, and modulation classification. Traditional signal processing depends on predefined mathematical models and assumptions, which might not be able to capture the dynamic and complex nature of 5G environments. In contrast, AI-driven approaches can adapt to varying conditions by learning from real-time data, hence offering better accuracy, robustness, and efficiency. Supervised and unsupervised ML models are used for tasks such as noise reduction and signal detection, while reinforcement learning helps optimize resource allocation and network scheduling. Besides, neural networks can approximate any non-linear transformations, which brings improved receiver design for Massive MIMO systems. In terms of signal demodulation and decoding, deep learning models like convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been proved to show promising results. In addition, AI-powered signal processing enables real-time decision-making in 5G use cases such as autonomous vehicles and industrial automation. The main goal of this paper is to overview the use of AI in signal processing for 5G networks by addressing the key techniques, performance enhancements, and future research directions. With the combination of AI into traditional signal processing, next-generation networks can become more reliable, efficient, and flexible under different operation environments