Deep MIMO detection

By Neev Samuel, Tzvi Diskin, Ami Wiesel. International Workshop on Signal Processing Advances in Wireless Communications, 2017

445 citations recorded by the source corpus.

In this paper, we consider the use of deep neural networks in the context of Multiple-Input-Multiple-Output (MIMO) detection. We give a brief introduction to deep learning and propose a modern neural network architecture suitable for this detection task. First, we consider the case in which the MIMO channel is constant, and we learn a detector for a specific system. Next, we consider the harder case in which the parameters are known yet changing and a single detector must be learned for all multiple varying channels. We demonstrate the performance of our deep MIMO detector using numerical simulations in comparison to competing methods including approximate message passing and semidefinite relaxation. The results show that deep networks can achieve state of the art accuracy with significantly lower complexity while providing robustness against ill conditioned channels and mis-specified noise variance.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/cd5442754f24ef2f8121c137f0b76c5a3b6ae06b

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In Electrical and Electronic Engineering

Citations in the corpus

Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial (2017, methodology): “Such an ML-driven approach has been shown [32]–[37] to have a lot of promise in delivering lower bit error rates and better robustness to the wireless channel impediments.”
An Introduction to Deep Learning for the Physical Layer (2017, methodology): “Among the papers trying to improve existing algorithms are [22]–[24] that consider belief propagation channel decoding and multiple-input multiple-output (MIMO) detection, respectively.”
Massive MIMO Detection Techniques: A Survey (2019, methodology): “Additionally, machine learning based massive MIMO detectors are at an early stage [148].”
Grant-Free Non-Orthogonal Multiple Access for IoT: A Survey (2019, methodology): “For the physical layer, learning algorithms have been applied to demodulation [173], channel codes [174], CS [175], coherent [176] and blind [177] detection, etc.”
Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing (2019, background): “[62] propose a deep network called DetNet, based on unrolling the projected gradient descent algorithm for least squares recovery.”
Deep Learning in Mobile and Wireless Networking: A Survey (2018, methodology): “By unfolding a projected gradient descent method, they design an MLP-based detection network to perform binary MIMO detection [351].”
Learning to optimize: Training deep neural networks for wireless resource management (2017, background): “The very recent work in [27] proposes a DNN architecture to approximate an algorithm for the MIMO detection problem, but the idea, again, follows the unfolding work in [17].”
Deep Learning for Intelligent Wireless Networks: A Comprehensive Survey (2018, methodology): “In addition, DL networks are also used in signal detection scenarios, such as multiple input multiple output (MIMO) signal detection [48], [49] and chemical signal detection [50].”
Learning to Optimize: Training Deep Neural Networks for Interference Management (2017, background): “The very recent work in [20] proposes a DNN architecture to approximate an algorithm for the MIMO detection problem, but the idea, again, follows the unfolding work in [10].”
Team Deep Neural Networks for Interference Channels (2018, methodology): “In [18], deep learning is used for detection to reduce complexity while it is used in [19] to make up for the absence of channel model while performing detection in molecular communication.”
5G-Advanced Toward 6G: Past, Present, and Future (2023, methodology): “framework, called DetNET, has been introduced in [33] for MIMO symbol detection.”
Machine Learning in the Air (2019, background): “The detection problem is also studied in [24] considering a MIMO channel: where y ∈ R N is the received vector, H ∈ R N × K is the channel matrix, x ∈ {− 1 , +1 } K is the unknown channel input vector consisting of independent and equally likely binary symbols, and w ∈ R N is the noise vector…”
6G Wireless Systems: Vision, Requirements, Challenges, Insights, and Opportunities (2020, background): “Here, ML could play a major role, from symbol detection [162], to precoding [163], to beam selection [164],”
Massive MIMO is a Reality - What is Next? Five Promising Research Directions for Antenna Arrays (2019, methodology): “Some recent examples include MIMO detection [141], blind channel equalization [142], channel estimation [143] and feedback [144], Massive MIMO power control [145], [146], user positioning [90], as well as full learning of transceiver implementations [147].”
Deep Learning-Based Channel Estimation (2018, background): “…some approaches have been proposed to enhance the performance of different conventional algorithms including modulation recognition [7], channel encoding and decoding [8], signal detection [9], channel equalization [10], channel state information (CSI) feedback [11] and channel estimation [12].”
Deep learning for wireless physical layer: Opportunities and challenges (2017, methodology): “…the conventional communication system (e.g., modulation recognition [1], channel encoding and decoding [25], [26], [27], [28], [29], [30], and channel estimation and detection [6], [4], [31], [32]) and to replace the total system with a novel architecture on the basis of an autoencoder [1], [33].”
Deep Learning Based Communication Over the Air (2017, background): “Among them are belief propagation for channel decoding [16], [17], one-shot channel decoding [18], compressed sensing [19], [20], coherent [21] and blind [22]”