Cognitive Radio Spectrum Classification using FLA-SVM

Gupta, Ayush; Majumder, Saikat

doi:10.1109/iemeconx.2019.8877089

Cited by 6 publications

(5 citation statements)

References 14 publications

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“…For an AWGN channel, the probabilities of false alarms and detection are deliberated in Eqs. ( 18 20) and in reference [12].…”

Section: Soft Decisionmentioning

confidence: 98%

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Cooperative Spectrum Sensing using Rule based Hard Decision and Soft Decision with Bayesian Optimized Support Vector Machine

2022

IJIES

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Cognitive radio technology has emerged as an operational substitute for increasing the number of users of broadband data services in various frequency bands. This article presents the results of an analysis of the effectiveness of the fusion rule for joint spectrum detection in cooperative spectrum sensing (CSS). The energy detectors-based feature vector is considered for ML training purpose. The paper proposes support vector machine (SVM) based modelling for training and testing in CR. Further Bayesian optimized SVM is proposed to claim higher detection rate. The proposed method yields 0.84 detection rate at 0.1 probability of false alarm.

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“…For an AWGN channel, the probabilities of false alarms and detection are deliberated in Eqs. ( 18 20) and in reference [12].…”

Section: Soft Decisionmentioning

confidence: 98%

“…𝑄 𝐹𝐴 = ∏ 𝑃 𝐹𝐴,𝑛 𝑁 𝑛=1 (10) If the probabilities are the same for all users, then the probability of detection and the probability of a false alarm can be determined as indicated in Eqs. (11) and (12).…”

Section: Hard Decisionmentioning

confidence: 99%

Cooperative Spectrum Sensing using Rule based Hard Decision and Soft Decision with Bayesian Optimized Support Vector Machine

2022

IJIES

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“…Spectrum sensing involves the classification of a part of the spectrum or a frequency band as either "occupied" or "unoccupied" [246]. Several types of CR-based schemes are presented recently (e.g., matched filter, energy, and cyclostationary feature detection) [247]. When more accurate information about the primary user is needed, then the best-matched filter is required to perform optimal detection [248].…”

Section: Cognitive Radiomentioning

confidence: 99%

Issues, Challenges, and Research Trends in Spectrum Management: A Comprehensive Overview and New Vision for Designing 6G Networks

et al. 2020

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With an extensive growth in user demand for high throughput, large capacity, and low latency, the ongoing deployment of Fifth-Generation (5G) systems is continuously exposing the inherent limitations of the system, as compared with its original premises. Such limitations are encouraging researchers worldwide to focus on next-generation 6G wireless systems, which are expected to address the constraints. To meet the above demands, future radio network architecture should be effectively designed to utilize its maximum radio spectrum capacity. It must simultaneously utilize various new techniques and technologies, such as Carrier Aggregation (CA), Cognitive Radio (CR), and small cell-based Heterogeneous Networks (HetNet), high-spectrum access (mmWave), and Massive Multiple-Input-Multiple-Output (M-MIMO), to achieve the desired results. However, the concurrent operations of these techniques in current 5G cellular networks create several spectrum management issues; thus, a comprehensive overview of these emerging technologies is presented in detail in this study. Then, the problems involved in the concurrent operations of various technologies for the spectrum management of the current 5G network are highlighted. The study aims to provide a detailed review of cooperative communication among all the techniques and potential problems associated with the spectrum management that has been addressed with the possible solutions proposed by the latest researches. Future research challenges are also discussed to highlight the necessary steps that can help achieve the desired objectives for designing 6G wireless networks.

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“…Depending on this design, computation time and memory will be required to solve the decision problem. Once this learning process is done, the system can distinguish between signals, and only data points which lie at the margin of the hyperplane are qualified to be categorized as support vectors [2].…”

Section: Previous Workmentioning

confidence: 99%

“…The general concept of cognitive radio is an intelligent communication system that adapts in real-time to the radio environment; it is flexible and makes a better use of frequency resources. For this task, it continuously senses the radio frequency environment to find spectral holes where the primary user (PU) is not transmitting, and it involves the identification of PU activity in the spectrum and frequency hopping in case PU signal detected [2]. The first step in MCRN is to detect the PU presence; we can find several spectrum-sensing techniques such as energy detection, location, feature detection, matched filter detection, covariance-based detection, and cooperative spectrum sensing, among others.…”

Section: Introductionmentioning

confidence: 99%

Detection of Malicious Primary User Emulation Based on a Support Vector Machine for a Mobile Cognitive Radio Network Using Software-Defined Radio

2020

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Mobile cognitive radio networks provide a new platform to implement and adapt wireless cellular communications, increasing the use of the electromagnetic spectrum by using it when the primary user is not using it and providing cellular service to secondary users. In these networks, there exist vulnerabilities that can be exploited, such as the malicious primary user emulation (PUE), which tries to imitate the primary user signal to make the cognitive network release the used channel, causing a denial of service to secondary users. We propose a support vector machine (SVM) technique, which classifies if the received signal is a primary user or a malicious primary user emulation signal by using the signal-to-noise ratio (SNR) and Rényi entropy of the energy signal as an input to the SVM. This model improves the detection of the malicious attacker presence in low SNR without the need for a threshold calculation, which can lead to false detection results, especially in orthogonal frequency division multiplexing (OFDM) where the threshold is more difficult to estimate because the signal limit values are very close in low SNR. It is implemented on a software-defined radio (SDR) testbed to emulate the environment of mobile system modulations, such as Gaussian minimum shift keying (GMSK) and OFDM. The SVM made a previous learning process to allow the SVM system to recognize the signal behavior of a primary user in modulations such as GMSK and OFDM and the SNR value, and then the received test signal is analyzed in real-time to decide if a malicious PUE is present. The results show that our solution increases the detection probability compared to traditional techniques such as energy or cyclostationary detection in low SNR values, and it detects malicious PUE signal in MCRN.

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Cognitive Radio Spectrum Classification using FLA-SVM

Cited by 6 publications

References 14 publications

Cooperative Spectrum Sensing using Rule based Hard Decision and Soft Decision with Bayesian Optimized Support Vector Machine

Cooperative Spectrum Sensing using Rule based Hard Decision and Soft Decision with Bayesian Optimized Support Vector Machine

Issues, Challenges, and Research Trends in Spectrum Management: A Comprehensive Overview and New Vision for Designing 6G Networks

Detection of Malicious Primary User Emulation Based on a Support Vector Machine for a Mobile Cognitive Radio Network Using Software-Defined Radio

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