Deep Learning Techniques For Massive MIMO Detection Algorithms: A Review

Abstract

Massive Multiple-Input Multiple-Output (MIMO) is a critical Technique for next-generation communication systems, as it significantly improves spectrum  efficiency and system capacity, that must meet  user performance and quality of service requirements(QoS). However the detection of signal in massive MIMO systems remains challenging due to the limited performance of traditional algorithms and high computational complexity. Over recent years deep learning has been introduced as a method for improving signal detection algorithms in massive MIMO systems .This study provides a systematic review of Deep neural network-based detection techniques for massive MIMO systems .A systematic literature review was conducted, drawing on recent studies selected from major scientific databases and research published between 2016 and 2025, with a focus on widely adopted detection frameworks. A  unified  comparison was performed using key criteria, including bit error rate (BER) performance, computational complexity and practical feasibility. The reviewed methods were categorized as data-driven, model-driven, and hybrid approaches, allowing for the organization of analyses according to their design principles. The results indicate that model-driven and hybrid techniques enhanced trade-off  between detection accuracy and computational complexity, while purely data-driven methods require intensive training and exhibit limited generalize ability under varying channel conditions. Compared to existing surveys, this work provides a more standardized classification and a more accurate comparative framework, highlighting current research trends and identifying existing challenges, such as scalability for large-scale systems ,effective training strategies and  robustness to realistic channel environments.