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Cognitive Radio Networks: Technologies, Challenges and Applications

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

Deadline for manuscript submissions: closed (20 May 2024) | Viewed by 30698

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Dr. Jia Liu

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Guest Editor

Center for Strategic Cyber Resilience Research and Development, National Institute of Informatics, Tokyo 101-8430, Japan
Interests: wireless systems security; covert communications; Internet of Things; cognitive radio networks; 5G
Special Issues, Collections and Topics in MDPI journals

Dr. Kechen Zheng

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Guest Editor

School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
Interests: cognitive radio networks; energy harvesting networks; wireless-powered communication networks; spectrum sensing; supervised learning; reinforcement learning
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Dr. Xiaoying Liu

E-Mail Website
Guest Editor

School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
Interests: green communication; energy harvesting; physical payer security; cooperative communication; cognitive radio networks
Special Issues, Collections and Topics in MDPI journals

Dr. Zhao Li

E-Mail Website
Guest Editor

School of Cyber Engineering, Xidian University, Xi’an 710071, China
Interests: cognitive radio networks; wireless interference management; signal processing for wireless communications; adaptive beamforming; MIMO
Special Issues, Collections and Topics in MDPI journals

Dr. Yang Xu

E-Mail Website
Guest Editor

School of Computer Science and Technology, Xidian University, Xi’an 710071, China
Interests: wireless communications; air–space–ground integrated networks; physical layer security; network economics; cognitive radio networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The demand for radio spectrum efficiency has been increasing dramatically over the past decade along with the explosive growth of various wireless devices, and the efficient utilization of radio spectrum resources is a key requirement for modern wireless networks and communications. In this context, the technology of cognitive radio networks, which equips wireless devices with the capability to optimally adapt operating parameters according to their interactions with the surrounding radio environment, is emerging to address the issues of spectrum efficiency and resource allocation, and has been receiving increasing academic and industrial attention in recent years. Although carrying huge application potential, cognitive radio networks also face great challenges. For instance, spectrum sensing is the key enabling function of cognitive radio networks to prevent harmful interference to licensed users and identify the available spectrum for improving spectrum utilization. However, practical detection performance is often compromised with multipath fading, shadowing, receiver uncertainty, and so on.

The aim of this Special Issue is to bring together and disseminate state-of-the-art research advances in the analysis, design, optimization, implementation, and standardization of cognitive radio networks. We welcome original research and review articles. The potential topics include, but are not limited to, the following:

Architecture and building blocks of cognitive radio networks and systems;
Design, analysis, and optimization of large-scale cognitive radio networks;
Spectrum sensing, spectrum sharing, and spectrum learning and prediction;
Interference mitigation and management for cognitive radio networks;
Detection and estimation techniques for cognitive radio networks;
Machine learning techniques for cognitive radio networks;
Game-theoretic modeling of cognitive radio networks;
Economic aspects of cognitive radio networking and spectrum sharing;
Cognitive radio network standards, testbeds, simulation tools, and hardware prototypes;
Challenges and issues in designing cognitive radio networks and communications.

Dr. Jia Liu
Dr. Kechen Zheng
Dr. Xiaoying Liu
Dr. Zhao Li
Dr. Yang Xu
Guest Editors

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Published Papers (13 papers)

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Research

15 pages, 2322 KiB

Open AccessArticle

Decision-Making Algorithm with Geographic Mobility for Cognitive Radio

by Gabriel B. Cervantes-Junco, Enrique Rodriguez-Colina, Leonardo Palacios-Luengas, Michael Pascoe-Chalke, Pedro Lara-Velázquez and Ricardo Marcelín-Jiménez

Sensors 2024, 24(5), 1540; https://doi.org/10.3390/s24051540 - 28 Feb 2024

Viewed by 886

Abstract

The proposed novel algorithm named decision-making algorithm with geographic mobility (DMAGM) includes detailed analysis of decision-making for cognitive radio (CR) that considers a multivariable algorithm with geographic mobility (GM). Scarce research work considers the analysis of GM in depth, even though it plays a crucial role to improve communication performance. The DMAGM considerably reduces latency in order to accurately determine the best communication channels and includes GM analysis, which is not addressed in other algorithms found in the literature. The DMAGM was evaluated and validated by simulating a cognitive radio network that comprises a base station (BS), primary users (PUs), and CRs considering random arrivals and disappearance of mobile devices. The proposed algorithm exhibits better performance, through the reduction in latency and computational complexity, than other algorithms used for comparison using 200 channel tests per simulation. The DMAGM significantly reduces the decision-making process from 12.77% to 94.27% compared with ATDDiM, FAHP, AHP, and Dijkstra algorithms in terms of latency reduction. An improved version of the DMAGM is also proposed where feedback of the output is incorporated. This version is named feedback-decision-making algorithm with geographic mobility (FDMAGM), and it shows that a feedback system has the advantage of being able to continually adjust and adapt based on the feedback received. In addition, the feedback version helps to identify and correct problems, which can be beneficial in situations where the quality of communication is critical. Despite the fact that the FDMAGM may take longer than the DMAGM to calculate the best communication channel, constant feedback improves efficiency and effectiveness over time. Both the DMAGM and the FDMAGM improve performance in practical scenarios, the former in terms of latency and the latter in terms of accuracy and stability. Full article

(This article belongs to the Special Issue Cognitive Radio Networks: Technologies, Challenges and Applications)

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20 pages, 21127 KiB

Open AccessArticle

Respecting Partial Privacy of Unstructured Data via Spectrum-Based Encoder

by Qingcai Luo and Hui Li

Sensors 2024, 24(3), 1015; https://doi.org/10.3390/s24031015 - 4 Feb 2024

Viewed by 1085

Abstract

Since the popularity of Machine Learning as a Service (MLaaS) has been increasing significantly, users are facing the risk of exposing sensitive information that is not task-related. The reason is that the data uploaded by users may include some information that is not useful for inference but can lead to privacy leakage. One straightforward approach to mitigate this issue is to filter out task-independent information to protect user privacy. However, this method is feasible for structured data with naturally independent entries, but it is challenging for unstructured data. Therefore, we propose a novel framework, which employs a spectrum-based encoder to transform unstructured data into the latent space and a task-specific model to identify the essential information for the target task. Our system has been comprehensively evaluated on three benchmark visual datasets and compared to previous works. The results demonstrate that our framework offers superior protection for task-independent information and maintains the usefulness of task-related information. Full article

(This article belongs to the Special Issue Cognitive Radio Networks: Technologies, Challenges and Applications)

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12 pages, 565 KiB

Open AccessArticle

Efficient Cumulant-Based Automatic Modulation Classification Using Machine Learning

by Ben Dgani and Israel Cohen

Sensors 2024, 24(2), 701; https://doi.org/10.3390/s24020701 - 22 Jan 2024

Cited by 1 | Viewed by 1500

Abstract

This paper introduces a new technique for automatic modulation classification (AMC) in Cognitive Radio (CR) networks. The method employs a straightforward classifier that utilizes high-order cumulant for training. It focuses on the statistical behavior of both analog modulation and digital schemes, which have received limited attention in previous works. The simulation results show that the proposed method performs well with different signal-to-noise ratios (SNRs) and channel conditions. The classifier’s performance is superior to that of complex deep learning methods, making it suitable for deployment in CR networks’ end units, especially in military and emergency service applications. The proposed method offers a cost-effective and high-quality solution for AMC that meets the strict demands of these critical applications. Full article

(This article belongs to the Special Issue Cognitive Radio Networks: Technologies, Challenges and Applications)

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27 pages, 491 KiB

Open AccessArticle

Performance Analysis of Centralized Cooperative Schemes for Compressed Sensing

by Luca Rugini and Paolo Banelli

Sensors 2024, 24(2), 661; https://doi.org/10.3390/s24020661 - 20 Jan 2024

Cited by 1 | Viewed by 1015

Abstract

This paper presents a performance analysis of centralized spectrum sensing based on compressed measurements. We assume cooperative sensing, where unlicensed users individually perform compressed sensing and send their results to a fusion center, which makes the final decision about the presence or absence of a licensed user signal. Several cooperation schemes are considered, such as and-rule, or-rule, majority voting, soft equal-gain combining (EGC). The proposed analysis provides simplified closed-form expressions that calculate the required number of sensors, the required number of samples, the required compression ratio, and the required signal-to-noise ratio (SNR) as a function of the probability of detection and the probability of the false alarm of the fusion center and of the sensors. The resulting expressions are derived by exploiting some accurate approximations of the test statistics of the fusion center and of the sensors, equipped with energy detectors. The obtained results are useful, especially for a low number of sensors and low sample sizes, where conventional closed-form expressions based on the central limit theorem (CLT) fail to provide accurate approximations. The proposed analysis also allows the self-computation of the performance of each sensor and of the fusion center with reduced complexity. Full article

(This article belongs to the Special Issue Cognitive Radio Networks: Technologies, Challenges and Applications)

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22 pages, 2403 KiB

Open AccessArticle

Multitask Learning-Based Deep Signal Identification for Advanced Spectrum Sensing

by Hanjin Kim, Young-Jin Kim and Won-Tae Kim

Sensors 2023, 23(24), 9806; https://doi.org/10.3390/s23249806 - 13 Dec 2023

Cited by 2 | Viewed by 1606

Abstract

The explosive demand for wireless communications has intensified the complexity of spectrum dynamics, particularly within unlicensed bands. To promote efficient spectrum utilization and minimize interference during communication, spectrum sensing needs to evolve to a stage capable of detecting multidimensional spectrum states. Signal identification, which identifies each device’s signal source, is a potent method for deriving the spectrum usage characteristics of wireless devices. However, most existing signal identification methods mainly focus on signal classification or modulation classification, thus offering limited spectrum information. In this paper, we propose DSINet, a multitask learning-based deep signal identification network for advanced spectrum sensing systems. DSINet addresses the deep signal identification problem, which involves not only classifying signals but also deriving the spectrum usage characteristics of signals across various spectrum dimensions, including time, frequency, power, and code. Comparative analyses reveal that DSINet outperforms existing shallow signal identification models, with performance improvements of 3.3% for signal classification, 3.3% for hall detection, and 5.7% for modulation classification. In addition, DSINet solves four different tasks with a 65.5% smaller model size and 230% improved computational performance compared to single-task learning model sets, providing meaningful results in terms of practical use. Full article

(This article belongs to the Special Issue Cognitive Radio Networks: Technologies, Challenges and Applications)

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22 pages, 705 KiB

Open AccessArticle

Algorithm Design and Convergence Analysis for Coexistence of Cognitive Radio Networks in Unlicensed Spectrum

by Yuan Zhang, Weihua Wu, Wei He and Nan Zhao

Sensors 2023, 23(24), 9705; https://doi.org/10.3390/s23249705 - 8 Dec 2023

Cited by 1 | Viewed by 4074

Abstract

This paper focuses on achieving the low-cost coexistence of the networks in an unlicensed spectrum by making them operate on non-overlapping channels. For achieving this goal, we first give a universal convergence analysis framework for the unlicensed spectrum allocation algorithm. Then, a one-timescale iteration-adjustable unlicensed spectrum allocation algorithm is developed, where the step size and timescale parameter can be jointly adjusted based on the system performance requirement and signal overhead concern. After that, we derive the sufficient condition for the one-timescale algorithm. Furthermore, the upper bound of convergence error of the one-timescale spectrum allocation algorithm is obtained. Due to the multi-timescale evolution of the network states in the wireless network, we further propose a two-timescale iteration-adjustable joint frequency selection and frequency allocation algorithm, where the frequency selection iteration timescale is set according to the slow-changing statistical channel state information (CSI), whereas the frequency allocation iteration timescale is set according to the fast-changing local CSI. Then, we derive the convergence condition of two-timescale algorithms and the upper bound of the corresponding convergence error. The experimentalresults show that the small timescale adjustment parameter and large step size can help decrease the convergence error. Moreover, compared with traditional algorithms, the two-timescale policy can achieve throughput similar to traditional algorithms with very low iteration overhead. Full article

(This article belongs to the Special Issue Cognitive Radio Networks: Technologies, Challenges and Applications)

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15 pages, 3303 KiB

Open AccessArticle

Study of Interference Detection of Rail Transit Wireless Communication System Based on Fourth-Order Cyclic Cumulant

by Peng Wang, Junliang Yao, Yong Pu, Shuyuan Zhang and Lu Wen

Sensors 2023, 23(19), 8291; https://doi.org/10.3390/s23198291 - 7 Oct 2023

Cited by 1 | Viewed by 1072

Abstract

The wireless communication system is used to provide dispatching, control, communication and other services for rail transit operations. In practice, interference from other wireless communication systems will affect the normal operation of trains, so it is an urgent problem to study the interference detection algorithms for rail transit applications. In this paper, the fourth-order cyclic cumulant (FOCC) of signals with different modulation modes is analyzed for the narrow-band wireless communications system of rail transit. Based on the analysis results, an adjacent-frequency interference detection algorithm is proposed according to the FOCC of the received signal within the predetermined cyclic frequency range. To detect interference with the same carrier frequency, a same-frequency interference detection algorithm using the relationship between the FOCC and the received power is proposed. The performance of the proposed detection algorithms in terms of correct rate and computational complexity is analyzed and compared with the traditional second-order statistical methods. Simulation results show that when an interference signal coexists with the expected signal, the correct rates of the adjacent-frequency and the same-frequency interference detection algorithms are greater than 90% when the signal-to-noise ratio (SNR) is higher than 2 dB and −4 dB, respectively. Under the practical rail transit wireless channel with multipath propagation and the Doppler effect, the correct rates of the adjacent-frequency and the same-frequency interference detection algorithms are greater than 90% when the SNR is higher than 3 dB and 7 dB, respectively. Compared with the existing second-order statistical methods, the proposed method can detect both the adjacent-frequency and the same-frequency interference when the interference signals coexist with the expected signal. Although the computational complexity of the proposed method is increased, it is acceptable in the application of rail transit wireless communication interference detection. Full article

(This article belongs to the Special Issue Cognitive Radio Networks: Technologies, Challenges and Applications)

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20 pages, 4253 KiB

Open AccessArticle

Environment-Aware Rate Adaptation Based on Occasional Request and Robust Adjustment in 802.11 Networks

by Weijie Yu, Li Wang, Jin Song, Lijun He and Yanting Wang

Sensors 2023, 23(18), 7889; https://doi.org/10.3390/s23187889 - 14 Sep 2023

Viewed by 1075

Abstract

The IEEE 802.11 standard provides multi-rate support for different versions. As there is no specification on the dynamic strategy to adjust the rate, different rate adaptation algorithms are applied according to different manufacturers. Therefore, it is often hard to interpret the performance discrepancy of various devices. Moreover, the ever-changing channels always challenge the rate adaptation, especially in the scenario with scarce spectrum and low SNR. As a result, it is important to sense the radio environment cognitively and reduce the unnecessary oscillation of the transmission rate. In this paper, we propose an environment-aware robust (EAR) algorithm. This algorithm employs an occasional small packet, designs a rate scheme adaptive to the environment, and enhances the robustness. We verify the throughput of EAR using network simulator NS-3 in terms of station number, motion speed and node distance. We also compare the proposed algorithm with three benchmark methods: AARF, RBAR and CHARM. Simulation results demonstrate that EAR outperforms other algorithms in several wireless environments, greatly improving the system robustness and throughput. Full article

(This article belongs to the Special Issue Cognitive Radio Networks: Technologies, Challenges and Applications)

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20 pages, 1269 KiB

Open AccessArticle

An Overview of Cognitive Radio Technology and Its Applications in Civil Aviation

by Ruikang Zheng, Xuan Li and Yudong Chen

Sensors 2023, 23(13), 6125; https://doi.org/10.3390/s23136125 - 3 Jul 2023

Cited by 5 | Viewed by 6182

Abstract

This paper provides an overview of cognitive radio technology and its applications in the field of civil aviation. Cognitive radio technology is a relatively new and emerging field that allows for dynamic spectrum access and efficient use of spectrum resources. In the context of civil aviation, cognitive radio technology has the potential to enable more efficient use of the limited radio spectrum available for communication and navigation purposes. This paper examines the current state of cognitive radio technology, including ongoing research and development efforts, regulatory issues, and potential challenges to widespread adoption. The potential applications of cognitive radio technology in civil aviation are also explored, including improved spectrum utilization, increased safety and security, and enhanced situational awareness. Finally, the paper concludes with a discussion of future research directions and the potential impact of cognitive radio technology on the future of civil aviation. It is hoped that this paper will serve as a useful resource for researchers, engineers, and policy makers interested in the emerging field of cognitive radio technology and its potential applications in the field of civil aviation. Full article

(This article belongs to the Special Issue Cognitive Radio Networks: Technologies, Challenges and Applications)

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19 pages, 2058 KiB

Open AccessArticle

Free-Rider Games for Cooperative Spectrum Sensing and Access in CIoT Networks

by Kejian Jiang, Chi Ma, Ruiquan Lin, Jun Wang, Weibing Jiang and Haifeng Hou

Sensors 2023, 23(13), 5828; https://doi.org/10.3390/s23135828 - 22 Jun 2023

Viewed by 1180

Abstract

With the rapid development of technologies such as wireless communications and the Internet of Things (IoT), the proliferation of IoT devices will intensify the competition for spectrum resources. The introduction of cognitive radio technology in IoT can minimize the shortage of spectrum resources. However, the open environment of cognitive IoT may involve free-riding problems. Due to the selfishness of the participants, there are usually a large number of free-riders in the system who opportunistically gain more rewards by stealing the spectrum sensing results from other participants and accessing the spectrum without spectrum sensing. However, this behavior seriously affects the fault tolerance of the system and the motivation of the participants, resulting in degrading the system’s performance. Based on the energy-harvesting cognitive IoT model, this paper considers the free-riding problem of Secondary Users (SUs). Since free-riders can harvest more energy in spectrum sensing time slots, the application of energy harvesting technology will exacerbate the free-riding behavior of selfish SUs in Cooperative Spectrum Sensing (CSS). In order to prevent the low detection performance of the system due to the free-riding behavior of too many SUs, a penalty mechanism is established to stimulate SUs to sense the spectrum normally during the sensing process. In the system model with multiple primary users (PUs) and multiple SUs, each SU considers whether to free-ride and which PU’s spectrum to sense and access in order to maximize its own interests. To address this issue, a two-layer game-based cooperative spectrum sensing and access method is proposed to improve spectrum utilization. Simulation results show that compared with traditional methods, the average throughput of the proposed TL-CSAG algorithm increased by

26.3 %

and the proposed method makes the SUs allocation more fair. Full article

(This article belongs to the Special Issue Cognitive Radio Networks: Technologies, Challenges and Applications)

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28 pages, 19540 KiB

Open AccessArticle

Cooperative Multiband Spectrum Sensing Using Radio Environment Maps and Neural Networks

by Yanqueleth Molina-Tenorio, Alfonso Prieto-Guerrero, Rafael Aguilar-Gonzalez and Miguel Lopez-Benitez

Sensors 2023, 23(11), 5209; https://doi.org/10.3390/s23115209 - 30 May 2023

Cited by 4 | Viewed by 1670

Abstract

Cogitive radio networks (CRNs) require high capacity and accuracy to detect the presence of licensed or primary users (PUs) in the sensed spectrum. In addition, they must correctly locate the spectral opportunities (holes) in order to be available to nonlicensed or secondary users (SUs). In this research, a centralized network of cognitive radios for monitoring a multiband spectrum in real time is proposed and implemented in a real wireless communication environment through generic communication devices such as software-defined radios (SDRs). Locally, each SU uses a monitoring technique based on sample entropy to determine spectrum occupancy. The determined features (power, bandwidth, and central frequency) of detected PUs are uploaded to a database. The uploaded data are then processed by a central entity. The objective of this work was to determine the number of PUs, their carrier frequency, bandwidth, and the spectral gaps in the sensed spectrum in a specific area through the construction of radioelectric environment maps (REMs). To this end, we compared the results of classical digital signal processing methods and neural networks performed by the central entity. Results show that both proposed cognitive networks (one working with a central entity using typical signal processing and one performing with neural networks) accurately locate PUs and give information to SUs to transmit, avoiding the hidden terminal problem. However, the best-performing cognitive radio network was the one working with neural networks to accurately detect PUs on both carrier frequency and bandwidth. Full article

(This article belongs to the Special Issue Cognitive Radio Networks: Technologies, Challenges and Applications)

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20 pages, 789 KiB

Open AccessArticle

Deep Q-Learning-Based Buffer-Aided Relay Selection for Reliable and Secure Communications in Two-Hop Wireless Relay Networks

by Cheng Zhang, Xuening Liao, Zhenqiang Wu, Guoyong Qiu, Zitong Chen and Zhiliang Yu

Sensors 2023, 23(10), 4822; https://doi.org/10.3390/s23104822 - 17 May 2023

Viewed by 1767

Abstract

This paper investigates the problem of buffer-aided relay selection to achieve reliable and secure communications in a two-hop amplify-and-forward (AF) network with an eavesdropper. Due to the fading of wireless signals and the broadcast nature of wireless channels, transmitted signals over the network may be undecodable at the receiver end or have been eavesdropped by eavesdroppers. Most available buffer-aided relay selection schemes consider either reliability or security issues in wireless communications; rarely is work conducted on both reliability and security issues. This paper proposes a buffer-aided relay selection scheme based on deep Q-learning (DQL) that considers both reliability and security. By conducting Monte Carlo simulations, we then verify the reliability and security performances of the proposed scheme in terms of the connection outage probability (COP) and secrecy outage probability (SOP), respectively. The simulation results show that two-hop wireless relay network can achieve reliable and secure communications by using our proposed scheme. We also performed comparison experiments between our proposed scheme and two benchmark schemes. The comparison results indicate that our proposed scheme outperforms the max-ratio scheme in terms of the SOP. Full article

(This article belongs to the Special Issue Cognitive Radio Networks: Technologies, Challenges and Applications)

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20 pages, 4200 KiB

Open AccessArticle

A High-Precision Vehicle Detection and Tracking Method Based on the Attention Mechanism

by Jiandong Wang, Yahui Dong, Shuangrui Zhao and Zhiwei Zhang

Sensors 2023, 23(2), 724; https://doi.org/10.3390/s23020724 - 8 Jan 2023

Cited by 32 | Viewed by 4719

Abstract

Vehicle detection and tracking technology plays an important role in intelligent transportation management and control systems. This paper proposes a novel vehicle detection and tracking method for small target vehicles to achieve high detection and tracking accuracy based on the attention mechanism. We first develop a new vehicle detection model (YOLOv5-NAM) by adding the normalization-based attention module (NAM) to the classical YOLOv5s model. By exploiting the YOLOv5-NAM model as the vehicle detector, we then propose a real-time small target vehicle tracking method (JDE-YN), where the feature extraction process is embedded in the prediction head for joint training. Finally, we present extensive experimental results to verify our method on the UA-DETRAC dataset and to demonstrate that the method can effectively detect small target vehicles in real time. It is shown that compared with the original YOLOv5s model, the mAP value of the YOLOv5-NAM vehicle detection model is improved by 1.6%, while the MOTA value of the JDE-YN method improved by 0.9% compared with the original JDE method. Full article

(This article belongs to the Special Issue Cognitive Radio Networks: Technologies, Challenges and Applications)

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