DS2MA: A Deep Learning-Based Spectrum Sensing Scheme for a Multi-Antenna Receiver

Chae, Keunhong; Kim, Yusung

doi:10.1109/lwc.2023.3250257

Cited by 5 publications

(7 citation statements)

References 16 publications

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“…We believe that the DNNs can learn more hidden features from raw IQ data than from the preprocessed features. Experimental results validate that the proposed methods have better detection performance than the DL methods based on covariance (CM-CNN) [22] or correlation matrices (DS2MA) [23].…”

Section: Introductionmentioning

confidence: 63%

“…They designed three coupled neural networks to improve the adaptability of different conditions of different SNR. Chae et al [23] devised a deep spectrum sensing method for multiple antennas (DS2MA) receiving, utilizing the auto-correlation and cross-correlation functions of received signals. However, among the existing methods, the use of raw IQ data as input for DL-based multipleantenna spectrum sensing approaches and the exploration of deployment scenarios with varying numbers of antennas have not been thoroughly investigated.…”

Section: B Dl-based Spectrum Sensing Methodsmentioning

confidence: 99%

“…2) DS2MA: DS2MA is a newly proposed method designed to tackle the issue of multiple antennas spectrum sensing with deep learning [23]. It merges the auto-correlation and crosscorrelation of received signals as the input matrices for CNNbased detector.…”

Section: B Comparison Methodsmentioning

confidence: 99%

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MASSnet: Deep-Learning-Based Multiple-Antenna Spectrum Sensing for Cognitive-Radio-Enabled Internet of Things

Zhang,

Zheng,

Qiu

et al. 2024

IEEE Internet Things J.

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Cognitive radio-based Internet of Things (CR-IoTs) provide an efficient spectrum management for IoT networks with massive wireless access and data transmission needs. As one of the key technologies of CR-IoT, spectrum sensing is of great research significance. Motivated by the recent boom on applications of deep learning in wireless communications networks and IoT, several spectrum sensing methods based on deep learning have emerged. However these algorithms train the sensing models with the extracted features of received signals and require a retraining of sensing models when the number of sensing antennas changes. Thus, we develop multiple-antenna spectrum sensing methods based on convolutional neural networks (MASSnet) using the inphase (I) and quadrature (Q) components of the signals as the input. The three schemes of MASSnet also provide the flexibility to choose between retraining the sensing models or using the obtained models for different sensing antenna configurations. Experiment results demonstrate the superior performance of the proposed methods over existing deep learning-based spectrum sensing methods in terms of probability of detection especially in very low signal-to-noise ratio (SNR) condition. Furthermore, the proposed methods have good generalization ability to new noise distribution, new fading channel, different frequency offsets, and detecting signals with a new modulation even without retraining.

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Section: Introductionmentioning

confidence: 63%

Section: B Dl-based Spectrum Sensing Methodsmentioning

confidence: 99%

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MASSnet: Deep-Learning-Based Multiple-Antenna Spectrum Sensing for Cognitive-Radio-Enabled Internet of Things

Zhang,

Zheng,

Qiu

et al. 2024

IEEE Internet Things J.

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“…Based on deep learning [53][54][55][56][57][58][59][60][61] CNN By extracting higher-order statistical features of the signal, it is possible to achieve higher detection probabilities at low SNRs, and the model is also more robust.…”

Section: Performance Comparison Of Conventional Methods and Deep-lear...mentioning

confidence: 99%

“…The authors Keunhong Chae et al, in [61], considered spectrum sensing in the application scenario of a multi-antenna transmitter. The researchers propose a deep spectrumsensing (DS2MA) system that employed a multi-antenna receiver with multiple antennas.…”

mentioning

confidence: 99%

A Review of Research on Spectrum Sensing Based on Deep Learning

Zhang,

Luo

2023

Electronics

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In recent years, with the rapid development in wireless communication and 5G networks, the rapid growth in mobile users has been accompanied by an increasing demand for the electromagnetic spectrum. The birth of cognitive radio and its spectrum-sensing technology provides hope for solving the problem of low utilization of the wireless spectrum. Artificial intelligence (AI) has been widely discussed globally. Deep learning technology, known for its strong learning ability and adaptability, plays a significant role in this field. Moreover, integrating deep learning with wireless communication technology has become a prominent research direction in recent years. The research objective of this paper is to summarize the algorithm of cognitive radio spectrum-sensing technology combined with deep learning technology. To review the advantages of deep-learning-based spectrum-sensing algorithms, this paper first introduces the traditional spectrum-sensing methods. It summarizes and compares the advantages and disadvantages of each method. It then describes the application of deep learning algorithms in spectrum sensing and focuses on the typical deep-neural-network-based sensing methods. Then, the existing deep-learning-based cooperative spectrum-sensing methods are summarized. Finally, the deep learning spectrum-sensing methods are discussed, along with challenges in the field and future research directions.

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