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Deep Learning-Based Modulation Recognition for MIMO Systems: Fundamental, Methods, Challenges
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DOI：10.1109/ACCESS.2024.3440350.png)
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Abstract
En 中文
In non-cooperative communication systems such as radio spectrum resource regulation and modern electronic warfare, automatic modulation recognition is a key technology. Traditional modulation recognition methods mainly rely on manual feature extraction, decision theory, and recognition selection. The Deep Leaning (DL) algorithm automatically obtains signal features directly from massive data, and realizes feature extraction and recognition at the same time. However, most of the research on DL-AMR methods focuses on single input single output (SISO) systems, while there are few studies on DL-AMR methods in multiple-input, multiple-output (MIMO) systems, so the integration of deep learning models into modulation recognition of MIMO systems has attracted the attention of many researchers. The purpose of this paper is to provide a comprehensive review of modulation recognition methods for MIMOsystems based on DL. Firstly, the basic theory of MIMO and its derivative systems and modulation recognition is introduced in detail, then the traditional modulation recognition algorithms and deep learning-based modulation recognition algorithms of MIMO systems are introduced, and finally, on the basis of discussion and summary, the problems to be solved, the challenges and potential research directions are proposed.
Keywords:
Modulation
Deep learning
Antenna arrays
Receiving antennas
OFDM
Transmitting antennas
Feature extraction
MIMO
Neural networks
Pattern classification
MIMO systems
modulation recognition
neural networks
signal classification
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
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