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Advances in Cognitive Radio Networking and Communications
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Advances in Cognitive Radio Networking and Communications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".

Deadline for manuscript submissions: closed (20 November 2022) | Viewed by 7310

Special Issue Editor

Prof. Dr. Shufang Li

E-Mail Website
Guest Editor
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China
Interests: RF theory and application; wireless network planning and optimization; EMC and EMI key theory; technologies and applications of Internet of Things; cognitive radio technologies

Special Issue Information

Dear Colleagues,

In recent years, the explosive growth of mobile data traffic generated by global mobile users has aroused intense scholarly interest in the study of cognitive radio networks. A cognitive radio network is an intelligent network that dynamically changes its characteristics through the spectrum sensing process and adapts to the convenience of its environment. This cognitive radio technology overcomes the problem of spectrum scarcity caused by traditional fixed-spectrum allocation methods, improves spectrum utilization and channels a capacity of wireless communications. With increased service demands of higher wireless transmission capacity and performance, the efficient utilization of radio spectrum resources is an important challenge for modern wireless networks and communications.

This Special Issue aims to collate the latest research results in the design and application of radio networks and communication systems.

Prof. Dr. Shufang Li
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cognitive radio
  • cognitive radio network
  • spectrum sensing
  • WSN
  • wireless communications

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Published Papers (3 papers)

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Research

17 pages, 763 KiB  
Article
GRU-SVM Based Threat Detection in Cognitive Radio Network
by Evelyn Ezhilarasi I and J Christopher Clement
Sensors 2023, 23(3), 1326; https://doi.org/10.3390/s23031326 - 24 Jan 2023
Cited by 9 | Viewed by 2160
Abstract
Cognitive radio networks are vulnerable to numerous threats during spectrum sensing. Different approaches can be used to lessen these attacks as the malicious users degrade the performance of the network. The cutting-edge technologies of machine learning and deep learning step into cognitive radio [...] Read more.
Cognitive radio networks are vulnerable to numerous threats during spectrum sensing. Different approaches can be used to lessen these attacks as the malicious users degrade the performance of the network. The cutting-edge technologies of machine learning and deep learning step into cognitive radio networks (CRN) to detect network problems. Several studies have been conducted utilising various deep learning and machine learning methods. However, only a small number of analyses have used gated recurrent units (GRU), and that too in software defined networks, but these are seldom used in CRN. In this paper, we used GRU in CRN to train and test the dataset of spectrum sensing results. One of the deep learning models with less complexity and more effectiveness for small datasets is GRU, the lightest variant of the LSTM. The support vector machine (SVM) classifier is employed in this study’s output layer to distinguish between authorised users and malicious users in cognitive radio network. The novelty of this paper is the application of combined models of GRU and SVM in cognitive radio networks. A high testing accuracy of 82.45%, training accuracy of 80.99% and detection probability of 1 is achieved at 65 epochs in this proposed work. Full article
(This article belongs to the Special Issue Advances in Cognitive Radio Networking and Communications)
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18 pages, 995 KiB  
Article
Dual Residual Denoising Autoencoder with Channel Attention Mechanism for Modulation of Signals
by Ruifeng Duan, Ziyu Chen, Haiyan Zhang, Xu Wang, Wei Meng and Guodong Sun
Sensors 2023, 23(2), 1023; https://doi.org/10.3390/s23021023 - 16 Jan 2023
Cited by 8 | Viewed by 2633
Abstract
Aiming to address the problems of the high bit error rate (BER) of demodulation or low classification accuracy of modulation signals with a low signal-to-noise ratio (SNR), we propose a double-residual denoising autoencoder method with a channel attention mechanism, referred to as DRdA-CA, [...] Read more.
Aiming to address the problems of the high bit error rate (BER) of demodulation or low classification accuracy of modulation signals with a low signal-to-noise ratio (SNR), we propose a double-residual denoising autoencoder method with a channel attention mechanism, referred to as DRdA-CA, to improve the SNR of modulation signals. The proposed DRdA-CA consists of an encoding module and a decoding module. A squeeze-and-excitation (SE) ResNet module containing one residual connection is modified and then introduced into the autoencoder as the channel attention mechanism, to better extract the characteristics of the modulation signals and reduce the computational complexity of the model. Moreover, the other residual connection is further added inside the encoding and decoding modules to optimize the network degradation problem, which is beneficial for fully exploiting the multi-level features of modulation signals and improving the reconstruction quality of the signal. The ablation experiments prove that both the improved SE module and dual residual connections in the proposed method play an important role in improving the denoising performance. The subsequent experimental results show that the proposed DRdA-CA significantly improves the SNR values of eight modulation types in the range of −12 dB to 8 dB. Especially for 16QAM and 64QAM, the SNR is improved by 8.38 dB and 8.27 dB on average, respectively. Compared to the DnCNN denoising method, the proposed DRdA-CA makes the average classification accuracy increase by 67.59∼74.94% over the entire SNR range. When it comes to the demodulation, compared with the RLS and the DnCNN denoising algorithms, the proposed denoising method reduces the BER of 16QAM by an average of 63.5% and 40.5%, and reduces the BER of 64QAM by an average of 46.7% and 18.6%. The above results show that the proposed DRdA-CA achieves the optimal noise reduction effect. Full article
(This article belongs to the Special Issue Advances in Cognitive Radio Networking and Communications)
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17 pages, 3250 KiB  
Article
Energy-Efficient Cooperative Spectrum Sensing Using Machine Learning Algorithm
by Qingying Wu, Benjamin K. Ng and Chan-Tong Lam
Sensors 2022, 22(21), 8230; https://doi.org/10.3390/s22218230 - 27 Oct 2022
Cited by 3 | Viewed by 1964
Abstract
Cognitive Radio (CR) is a practical technique for overcoming spectrum inefficiencies by sensing and utilizing spectrum holes over a wide spectrum. In particular, cooperative spectrum sensing (CSS) determines the state of primary users (PUs) by cooperating with multiple secondary users (SUs) distributed around [...] Read more.
Cognitive Radio (CR) is a practical technique for overcoming spectrum inefficiencies by sensing and utilizing spectrum holes over a wide spectrum. In particular, cooperative spectrum sensing (CSS) determines the state of primary users (PUs) by cooperating with multiple secondary users (SUs) distributed around a Cognitive Radio Network (CRN), further overcoming various noise and fading issues in the radio environment. But it’s still challenging to balance energy efficiency and good sensing performances in the existing CSS system, especially when the CRN consists of battery-limited sensors. This article investigates the application of machine learning technologies for cooperative spectrum sensing, especially through solving a multi-dimensional optimization that cannot be readily addressed by traditional approaches. Specifically, we develop a neural network, which involves parameters that are integral to the CSS performance, including a device sleeping rate for each sensor and thresholds used in the energy detection method, and a customized loss function based on the energy consumption of the CSS system and multiple penalty terms reflecting the system requirements. Using this formulation, energy consumption is to be minimized with the guarantee of reaching a certain probability of false alarm and detection in the CSS system. With the proposed method, comparison studies under different hard fusion rules (‘OR’ and ‘AND’) demonstrate its effectiveness in improving the CSS system performances, as well as its robustness in the face of changing global requirements. This paper also suggests the combination of the traditional and the proposed scheme to circumvent the respective inherent pitfalls of neural networks and the traditional semi-analytic methods. Full article
(This article belongs to the Special Issue Advances in Cognitive Radio Networking and Communications)
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