Deep Neural Network Architectures for Spectrum Sensing Using Signal Processing Features

Chandra, Shreeram Suresh; Upadhye, Akshay; Saravanan, P.; Gurugopinath, Sanjeev; Muralishankar, R.

doi:10.1109/discover52564.2021.9663583

Cited by 4 publications

(5 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Do not need a priori information about the signal and want to improve the detection probability and robustness at low SNRs. [65,69,72,73] ResNet Improvements on CNN models and, in general, some performance gains over CNNs.…”

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

confidence: 99%

“…Shreeram Suresh Chandra et al conducted experiments [65] on several methods based on energy, differential entropy [66], geometric power [67], and the P-paradigm [68] using neural network structures such as DNNs, CNNs, ResNet, MLPs, etc., in order to compare the performances and study the optimal combinations of neural networks with signal processing methods. The experiments in this paper demonstrate that as the depth of a CNN increases, its performance also increases, but the vanishing gradient problem also occurs, so residual blocks are introduced to solve this problem.…”

Section: Application Of Residual Neural Network To Spectrum Sensingmentioning

confidence: 99%

See 1 more Smart Citation

A Review of Research on Spectrum Sensing Based on Deep Learning

Zhang,

Luo

2023

Electronics

View full text Add to dashboard Cite

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.

show abstract

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

confidence: 99%

Section: Application Of Residual Neural Network To Spectrum Sensingmentioning

confidence: 99%

A Review of Research on Spectrum Sensing Based on Deep Learning

Zhang,

Luo

2023

Electronics

View full text Add to dashboard Cite

show abstract

“…On the basis of [21], when the noise distribution has a heavy algebraic tail, the SS based on DE characteristics and the traditional fractional low order statistics (FLOS) can successfully deal with the heavy tail process. However, when the noise distribution has a very heavy algebraic tail, that is, when the algebraic tail constant is close to zero, the processing effect of DE and FLOS will become worse, and geometric power (GP), also known as zero-order statistics [27][28][29][30], is applicable to any process with a number or a light tail, which has been reflected ref. [30].…”

Section: Introductionmentioning

confidence: 99%

“…However, when the noise distribution has a very heavy algebraic tail, that is, when the algebraic tail constant is close to zero, the processing effect of DE and FLOS will become worse, and geometric power (GP), also known as zero-order statistics [27][28][29][30], is applicable to any process with a number or a light tail, which has been reflected ref. [30]. Ref.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Supervised Learning Spectrum Sensing Method via Geometric Power Feature

Luo

Xiao

2023

Electronics

View full text Add to dashboard Cite

In order to improve the spectrum sensing (SS) performance under a low Signal Noise Ratio (SNR), this paper proposes a supervised learning spectrum sensing method based on Geometric Power (GP) feature. The GP is used as the feature vector in the supervised learning spectrum sensing method for training and testing based on the actual captured data set. Experimental results show that the detection performance of the GP-based supervised learning spectrum sensing method is better than that of the Energy Statistics (ES) and Differential Entropy (DE)-based supervised learning spectrum sensing methods.

show abstract

Cross-attention mechanism-based spectrum sensing in generalized Gaussian noise

Xi,

Guo,

Yang

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Spectrum sensing (SS) technology is essential for cognitive radio (CR) networks to effectively identify and utilize idle spectrum resources. Due to the influence of noise characteristics in the channel, providing accurate sensing results is challenging. In order to improve the performance of SS under non-Gaussian noise and overcome the limitations of existing methods that are mostly based on a single feature, we propose a novel time-frequency cross fusion network (TFCFN). Specifically, we utilize gated recurrent units (GRU) to capture long-term dependencies in the time domain on the original signals, meanwhile, we perform a fast Fourier transform (FFT) on the original signals to obtain the frequency domain information, and subsequently use convolutional neural networks (CNN) to extract the local spatial features in the frequency domain. Ultimately, these time-domain and frequency-domain features are dynamically fused through a cross-attention mechanism to construct more comprehensive and robust features for signal classification. We use generalized Gaussian distribution (GGD) as the noise model and reconstruct the RadioML2016.10a dataset to explore the performance under various noise conditions. The experimental results show that compared with the baseline methods, TFCFN exhibits better detection ability and maintains lower complexity in both Gaussian and non-Gaussian noise environments. Notably, when the shape parameter of GGD is set to 0.5 and the signal-to-noise ratio (SNR) of the received signal is -16dB, it can maintain the probability of false alarm ( ) of 10% while still ensuring the probability of detection ( ) of over 90%.

show abstract

Deep Neural Network Architectures for Spectrum Sensing Using Signal Processing Features

Cited by 4 publications

References 25 publications

A Review of Research on Spectrum Sensing Based on Deep Learning

A Review of Research on Spectrum Sensing Based on Deep Learning

Supervised Learning Spectrum Sensing Method via Geometric Power Feature

Cross-attention mechanism-based spectrum sensing in generalized Gaussian noise

Contact Info

Product

Resources

About