A new transmission model in cognitive radio based on cyclic generalized polynomial codes for bandwidth reduction

2014 ◽  
Vol 06 (04) ◽  
pp. 1450059 ◽  
Author(s):  
Naveed Ahmed Azam ◽  
Tariq Shah ◽  
Antonio Aparecido de Andrade
Keyword(s):  
Cognitive Radio ◽  
Data Transmission ◽  
Primary User ◽  
Transmission Model ◽  
Secondary Users ◽  
Proposed Model ◽  
Generalized Polynomial ◽  
Polynomial Codes ◽  
Full Utilization ◽  
Minimum Interference

The frequency spectrums are inefficiently utilized and cognitive radio has been proposed for full utilization of these spectrums. The central idea of cognitive radio is to allow the secondary user to use the spectrum concurrently with the primary user with the compulsion of minimum interference. However, designing a model with minimum interference is a challenging task. In this paper, a transmission model based on cyclic generalized polynomial codes discussed in [2] and [15], is proposed for the improvement in utilization of spectrum. The proposed model assures a non interference data transmission of the primary and secondary users. Furthermore, analytical results are presented to show that the proposed model utilizes spectrum more efficiently as compared to traditional models.

scholarly journals Secure Data Transmission by Detecting Different Attacks in CRN to improve the throughput

2019 ◽  
Vol 9 (2) ◽  
pp. 5488-5494
Keyword(s):  
Resource Allocation ◽  
Cognitive Radio ◽  
Data Transmission ◽  
Cognitive Radio Network ◽  
Primary User ◽  
Radio Networks ◽  
Radio Network ◽  
Secondary Users ◽  
Security Issues ◽  
Secure Data Transmission

Cognitive Radio (CR) is a technology that promises to solve the data transmission problem by allowing secondary users to coexist with primary user without causing any interference to the communication. It means to improve the usage of the radio assets to improve the throughput. Despite the fact that the operational parts of CR are being investigated broadly, its security viewpoints have increased little consideration. In this work, present a CRN architecture , Different Protocol, with complete rundown of major known security dangers and assaults inside a Cognitive Radio Network (CRN). Our goal in this paper is to dissect the distinctive security issues of the primary ongoing advancements of Cognitive Radio Networks with proper resource allocation to improve the throughput.

Data Ownership and Secure Medical Data Transmission using Optimal Multiple Key-Based Homomorphic Encryption with Hyperledger Blockchain

2021 ◽  
Author(s):  
Naresh Sammeta ◽  
Latha Parthiban
Keyword(s):  
Data Transmission ◽  
Homomorphic Encryption ◽  
Medical Data ◽  
Healthcare Sector ◽  
Transmission Model ◽  
Generation Process ◽  
Suggested Model ◽  
Blockchain Technology ◽  
Proposed Model ◽  
Data Ownership

Recent healthcare systems are defined as highly complex and expensive. But it can be decreased with enhanced electronic health records (EHR) management, using blockchain technology. The healthcare sector in today’s world needs to address two major issues, namely data ownership and data security. Therefore, blockchain technology is employed to access and distribute the EHRs. With this motivation, this paper presents novel data ownership and secure medical data transmission model using optimal multiple key-based homomorphic encryption (MHE) with Hyperledger blockchain (OMHE-HBC). The presented OMHE-HBC model enables the patients to access their own data, provide permission to hospital authorities, revoke permission from hospital authorities, and permit emergency contacts. The proposed model involves the MHE technique to securely transmit the data to the cloud and prevent unauthorized access to it. Besides, the optimal key generation process in the MHE technique takes place using a hosted cuckoo optimization (HCO) algorithm. In addition, the proposed model enables sharing of EHRs by the use of multi-channel HBC, which makes use of one blockchain to save patient visits and another one for the medical institutions in recoding links that point to EHRs stored in external systems. A complete set of experiments were carried out in order to validate the performance of the suggested model, and the results were analyzed under many aspects. A comprehensive comparison of results analysis reveals that the suggested model outperforms the other techniques.

Cross-Layer Performance Analysis of the Unreliable Data Transmission for Decentralized Cognitive MAC Protocol under Multiple Secondary Users Scenario

2013 ◽  
Vol 1 (1) ◽  
pp. 74-85
Author(s):  
Xiaofeng Hu ◽  
Xianzhong Xie ◽  
Ting Song
Keyword(s):  
Data Transmission ◽  
Mac Protocol ◽  
Energy Detection ◽  
Primary User ◽  
Sampling Time ◽  
Probability Of Detection ◽  
Cross Layer ◽  
Secondary Users ◽  
Target Probability ◽  
A Minor

AbstractThe effect of multiple secondary users detecting the same primary user channel during a minor period on the performance of traditional energy detection is investigated. In order to achieve the target probability of detection, the minimum sampling time (MST) for energy detection is derived under the limitation of energy detection (LED), when the random arrival of the secondary users is taken into account. Jointly considering both spectrum sensing and access, we derive the probability of unreliable data transmission for decentralized cognitive MAC (DC-MAC) protocol based on MST under LED. The numerical results show that the simulation and theoretical analysis match very well. The reliability of data transmission is significantly degraded by the secondary users’ traffic. With the target probability of detection increasing, the reliable throughput reduces.

scholarly journals 5G cognitive radio system design with new algorithm asynchronous spectrum sensing

2021 ◽  
Vol 10 (4) ◽  
pp. 2046-2054
Author(s):  
Mohammed Mehdi Saleh ◽  
Ahmed A. Abbas ◽  
Ahmed Hammoodi
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Energy Detection ◽  
Primary User ◽  
Spectrum Access ◽  
Secondary Users ◽  
Radio System ◽  
Wireless Applications ◽  
Fifth Generation ◽  
Time Required

Due to the rapid increase in wireless applications and the number of users, spectrum scarcity, energy consumption and latency issues will emerge, notably in the fifth generation (5G) system. Cognitive radio (CR) has emerged as the primary technology to address these challenges, allowing opportunist spectrum access as well as the ability to analyze, observe, and learn how to respond to environmental 5G conditions. The CR has the ability to sense the spectrum and detect empty bands in order to use underutilized frequency bands without causing unwanted interference with legacy networks. In this paper, we presented a spectrum sensing algorithm based on energy detection that allows secondary user SU to transmit asynchronously with primary user PU without causing harmful interference. This algorithm reduced the sensing time required to scan the whole frequency band by dividing it into n sub-bands that are all scanned at the same time. Also, this algorithm allows cognitive radio networks (CRN) nodes to select their operating band without requiring cooperation with licensed users. According to the BER, secondary users have better performance compared with primary users.

Deep Cooperative Spectrum Sensing Utilizing Recurrent Convolutional Neural Networks

2020 ◽  
Author(s):  
Rahil Sarikhani ◽  
Farshid Keynia
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Recurrent Neural Network ◽  
Cooperative Spectrum Sensing ◽  
Primary User ◽  
Secondary Users ◽  
Spectrum Holes

Abstract Cognitive Radio (CR) network was introduced as a promising approach in utilizing spectrum holes. Spectrum sensing is the first stage of this utilization which could be improved using cooperation, namely Cooperative Spectrum Sensing (CSS), where some Secondary Users (SUs) collaborate to detect the existence of the Primary User (PU). In this paper, to improve the accuracy of detection Deep Learning (DL) is used. In order to make it more practical, Recurrent Neural Network (RNN) is used since there are some memory in the channel and the state of the PUs in the network. Hence, the proposed RNN is compared with the Convolutional Neural Network (CNN), and it represents useful advantages to the contrast one, which is demonstrated by simulation.

Game Theory for Cognitive Radio Networks

Game Theory ◽  
2017 ◽  
pp. 487-502
Author(s):  
Sungwook Kim
Keyword(s):  
Cognitive Radio ◽  
Cognitive Abilities ◽  
Cognitive Radios ◽  
Primary User ◽  
Spectrum Management ◽  
Cognitive Networks ◽  
Wireless Devices ◽  
New Paradigm ◽  
Secondary Users ◽  
Dynamic Spectrum Management

A cognitive radio is an intelligent radio that can be programmed and configured dynamically. Its transceiver is designed to use the best wireless channels in its vicinity. Such a radio automatically detects available channels in the wireless spectrum, then accordingly changes its transmission or reception parameters to allow more concurrent wireless communications in a given spectrum band at one location. This process is a form of dynamic spectrum management. In recent years, the development of intelligent, adaptive wireless devices called cognitive radios, together with the introduction of secondary spectrum licensing, has led to a new paradigm in communications: cognitive networks. Cognitive networks are wireless networks that consist of several types of users: often a primary user and secondary users. These cognitive users employ their cognitive abilities to communicate without harming the primary users. The study of cognitive networks is relatively new and many questions are yet to be answered. This chapter furthers the study.

scholarly journals Secure Beamforming in 5G-Based Cognitive Radio Network

Symmetry ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1260
Author(s):  
Hyils Sharon Magdalene Antony ◽  
Thulasimani Lakshmanan
Keyword(s):  
Cognitive Radio ◽  
Information Exchange ◽  
Cognitive Radio Network ◽  
Multiple Input Multiple Output ◽  
Primary User ◽  
Radio Network ◽  
Secrecy Rate ◽  
Secondary Users ◽  
Input Multiple Output ◽  
The Impact

Cognitive radio network (CRN) and non-orthogonal multiple-access (NOMA) is a significant system in the 5G wireless communication system. However, the system is an exceptional way for the cognitive users to secure a communication from the interferences in multiple-input multiple-output (MIMO)-NOMA-based cognitive radio network. In this article, a new beamforming technique is proposed to secure an information exchange within the same cells and neighboring cells from all intervened users. The interference is caused by an imperfect spectrum sensing of the secondary users (SUs). The SUs are intended to access the primary channels. At the same time, the primary user also returns to the channel before the SUs access ends. This similar way of accessing the primary channel will cause interference between the users. Thus, we predicted that the impact of interferences would be greatly reduced by the proposed technique, and that the proposed technique would maximize the entire secrecy rate in the 5G-based cognitive radio network. The simulation result provides better evidence for the performance of the proposed technique.

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

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1282
Author(s):  
Ernesto Cadena Muñoz ◽  
Luis Fernando Pedraza Martínez ◽  
Jorge Eduardo Ortiz Triviño
Keyword(s):  
Support Vector Machine ◽  
Cognitive Radio ◽  
Software Defined Radio ◽  
Denial Of Service ◽  
Primary User ◽  
Support Vector ◽  
Electromagnetic Spectrum ◽  
Secondary Users ◽  
Low Snr ◽  
Primary User Emulation

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.

scholarly journals Support Vector Machine-Based Classification of Malicious Users in Cognitive Radio Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Muhammad Sajjad Khan ◽  
Liaqat Khan ◽  
Noor Gul ◽  
Muhammad Amir ◽  
Junsu Kim ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Learning Algorithm ◽  
Evidence Theory ◽  
Primary User ◽  
Support Vector ◽  
Radio Network ◽  
Secondary Users

Cognitive radio is an intelligent radio network that has advancement over traditional radio. The difference between the traditional radio and the cognitive radio is that all the unused frequency spectrum can be utilized to the best of available resources in the cognitive radio unlike the traditional radio. The core technology of cognitive radio is spectrum sensing, in which secondary users (SUs) opportunistically access the spectrum while avoiding interference to primary user (PU) channels. Various aspects of the spectrum sensing have been studied from the perspective of cognitive radio. Cooperative spectrum sensing (CSS) technique provides a promising performance, compared with individual sensing techniques. However, the existence of malicious users (MUs) highly degrades the performance of cognitive radio network (CRN) by sending falsified results to a fusion center (FC). In this paper, we propose a machine learning algorithm based on support vector machine (SVM) to classify legitimate SUs and MUs in the CRN. The proposed SVM-based algorithm is used for both classification and regression. It clearly classifies legitimate SUs and MUs by drawing a hyperplane on the base of maximal margin. After successful classification, the sensing results from the legitimate SUs are combined at the FC by utilizing Dempster-Shafer (DS) evidence theory. The effectiveness of the proposed SVM-based classification algorithm is demonstrated through simulations, compared with existing schemes.

Improved Learning Scheme for Cognitive Radio using Artificial Neural Networks

2016 ◽  
Vol 6 (1) ◽  
pp. 257
Author(s):  
Rita Mahajan ◽  
Deepak Bagai
Keyword(s):  
Cognitive Radio ◽  
Primary User ◽  
Predictive Capability ◽  
Wireless System ◽  
Secondary Users ◽  
Learning Capability ◽  
Artificial Neural ◽  
Improved Learning ◽  
Time And Energy ◽  
Cognitive User

<p>The future of wireless system is facing the problem of spectrum scarcity. Number of users is increasing rapidly but available spectrum is limited. The Cognitive Radio (CR) network technology can enable the unlicensed users to share the frequency spectrum with the licensed users on a dynamic basis without creating any interference to primary user. Whenever secondary user finds that primary user is not transmitting and channel is free then it uses channel opportunistically. In this paper cognitive radio with predictive capability using artificial neural network has been proposed. The advantage of such cognitive user is saving of time and energy for spectrum sensing. Proposed radio will sense only that channel which is predicted to be free and channel is selected on the basis of maximum vacant time. Performance has been evaluated in the term of mean square error. The results show that this learning capability can be embedded in secondary users for better performance of future wireless technologies.  </p>

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