Deep Learning for Massive MIMO Uplink Detectors

Abstract

Detection techniques for massive multiple-input multiple-output (MIMO) have gained a lot of attention in both academia and industry. Detection techniques have a significant impact on the massive MIMO receivers' performance and complexity. Although a plethora of research is conducted using the classical detection theory and techniques, the performance is deteriorated when the ratio between the numbers of antennas and users is relatively small. In addition, most of classical detection techniques are suffering from severe performance loss and/or high computational complexity in real channel scenarios. Therefore, there is a significant room for fundamental research contributions in data detection based on the deep learning (DL) approach. DL architectures can be exploited to provide optimal performance with similar complexity of conventional detection techniques. This paper aims to provide insights on DL based detectors to a generalist of wireless communications. We garner the DL based massive MIMO detectors and classify them so that a reader can find the differences between various architectures with a wider range of potential solutions and variations. In this paper, we discuss the performance-complexity profile, pros and cons, and implementation stiffness of each DL based detector's architecture. Detection in cell-free massive MIMO is also presented. Challenges and our perspectives for future research directions are also discussed. This article is not meant to be a survey of a mature-subject, but rather serve as a catalyst to encourage more DL research in massive MIMO.

A Survey on Machine Learning Techniques for Massive MIMO Configurations: Application Areas, Performance Limitations and Future Challenges

“…The work of [22] mainly focused on the presentation and analysis of the detection algorithms for m-MIMO systems, where ML has been introduced as well. In the same context, [23] provided a thorough study of m-MIMO detectors based on DL techniques. In [18], a survey on ML approaches for 5G/6G networks was presented.…”

Intelligent Massive MIMO Systems for Beyond 5G Networks: An Overview and Future Trends

“…In [22] Zappone et al, provided a detailed discussion on the application of DL in wireless communication systems by establishing the link between ML and DL, and the application of DL models and mathematical models in wireless networks. A review of DL-based detectors for uplink communication in mMIMO systems was presented in [26] with a detailed discussion of various deep neural networks (DNN). While in [27] a tutorial on DRL as multiagent learning in cooperative AI-enabled wireless networks was presented.…”

“…In summary, some of these works have discussed the general applications of AI and its subset in wireless communication technology [14], [22], [23], [27], [31], [33]. A few have highlighted the role of ML in mMIMO [1], [22], [26], [28], [33] with less review on the extensive existing work in the literature. To the best of our knowledge, no work has provided an overview of the plethora of work on the application of ML in mMIMO systems.…”

LAMANet: A Real-Time, Machine Learning-Enhanced Approximate Message Passing Detector for Massive MIMO

“…Most commonly, some parameters of existing algorithms are optimized via machine learning, leading to higher performance. An overview of model-driven techniques can be found in [11].…”