scholarly journals Residual Energy of Energy Harvesting Cognitive Radio Networks

2019 ◽  
Vol 8 (6) ◽  
pp. 1376-1378
Keyword(s):  
Energy Harvesting ◽  
Cooperative Spectrum Sensing ◽  
Primary User ◽  
Residual Energy ◽  
Probability Of Detection ◽  
Power Splitting ◽  
Secondary Users ◽  
Splitting Ratio ◽  
Mathematical Expressions ◽  
Mode Of Operation

This paper analyzes cooperative spectrum sensing with energy harvesting using power splitting mode of operation simultaneously. Secondary users (SU) will harvests RF energy from primary user (PU) throughout the durations of sensing and transmission. The main aim of this paper is to analyze the residual energy of SU with power splitting ratio, number of samples, number of SUs and probability of detection. Mathematical expressions of energy consumption, harvested energy and residual energy are developed. The simulation results of residual energy with different parameters are verified and have proved that residual energy of SUs is increased with increase in power splitting ratio, number of SUs, number of samples of SU for sensing and probability of detection

scholarly journals Performance Analysis of Cooperative Spectrum Sensing under Guaranteed Throughput Constraints for Cognitive Radio Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
H. F. Al-Doseri ◽  
M. A. Mangoud
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Cooperative Spectrum Sensing ◽  
Primary User ◽  
Radio Networks ◽  
Probability Of Detection ◽  
Cooperative Sensing ◽  
Soft Decision ◽  
Secondary Users ◽  
Sensing Time

One of the main challenges in cognitive radio networks is the ability of secondary users to detect the primary user presence with high probability of detection. In previous research, optimizing cooperative sensing in cognitive radio networks is performed for either a targeted probability of detection or a false alarm. After setting one of the probabilities as an optimization constraint, the other is optimized. In this paper, a guaranteed constant throughput at the secondary users is introduced as a target while optimizing probability of detection for cooperative sensing. Both sensing time values and number of cooperated cognitive radio secondary users are investigated to maximize the probability of detection of primary user. AND and OR hard decision schemes are considered and compared with soft decision scheme which is weighted modified deflection coefficient scheme (W-MDC). It is illustrated that cooperation of all users and utilizing full frames for sensing time will not provide maximum probability of detection. A tradeoff between performances of cognitive radio networks with and without optimization is presented. The effects of varying network sizes, normalized target throughput, maximum frame duration times, and received signal-to-noise ratio at the fusion center are investigated for different fusion rules.

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.

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.

A Novel Weighted-Cooperative Spectrum Sensing Scheme Using Clustering

2012 ◽  
Vol 195-196 ◽  
pp. 277-282
Author(s):  
Hui Heng Liu ◽  
Wei Chen
Keyword(s):  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Cluster Head ◽  
Probability Of Detection ◽  
Fusion Center ◽  
Secondary Users ◽  
Probability Of Error ◽  
Radio System ◽  
Simulation Results ◽  
Observation Results

This paper proposes a novel weighted-cooperative spectrum sensing scheme using clustering for cognitive radio system. We firstly classify the secondary users into a few clusters according to several existent methods, and then use cluster-head to collect the observation results come from different secondary users in the same cluster and make a cluster-decision. Considering the different distances between the clusters and the fusion center, different weightings are used to weight the cluster-decisions before combining. The simulation results show that our proposed method improve the probability of detection and reduce the probability of error.

scholarly journals Use Hierarchical Codebook to Improve the Primary User Detection in the Cognitive Radio’s Cooperative Spectrum Sensing

2021 ◽  
Vol 5 (6) ◽  
pp. 22-28
Author(s):  
Hoai Trung Tran
Keyword(s):  
Spectrum Sensing ◽  
Channel Model ◽  
Cooperative Spectrum Sensing ◽  
Signal To Noise Ratio ◽  
Multiple Input Multiple Output ◽  
Primary User ◽  
Mimo Channel ◽  
High Signal ◽  
Secondary Users ◽  
Input Multiple Output

Currently, the cognitive network is receiving much attention due to the advantages it brings to users. An important method in cognitive radio networks is spectrum sensing, as it allows secondary users (SUs) to detect the existence of a primary user (PU). Information of probability of false detection or warning about the PU is sent to a fusion center (FC) by the SUs, from which the FC will decide whether or not to allow the SUs to use the PU spectrum to obtain information. The transmission of information with a high signal to noise ratio (SNR) will increase the FC's ability to detect the existence of the PU. However, researchers are currently focusing on probabilistic formulas assuming that the channel is known ideally or there is nominal channel information at the FC; moreover, one model where the FC only knows the channel correlation matrix. Furthermore, studies are still assuming this is a simple multiple input – multiple output (MIMO) channel model but do not pay much attention to the signal processing at the transmitting and receiving antennas between the SUs and the FCs. A new method introduced in this paper when combining beamforming and hierarchical codebook makes the ability to detect the existence of the PU at the FC significantly increased compared to traditional methods.

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.

scholarly journals On the Retrial-Queuing Model for Strategic Access and Equilibrium-Joining Strategies of Cognitive Users in Cognitive-Radio Networks with Energy Harvesting

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2088
Author(s):  
Kalpana Devarajan ◽  
Muthukrishnan Senthilkumar
Keyword(s):  
Cognitive Radio ◽  
Energy Harvesting ◽  
Performance Metrics ◽  
Stackelberg Game ◽  
Retrial Queue ◽  
Primary User ◽  
Secondary User ◽  
Single Server ◽  
Queuing Model ◽  
Secondary Users

This article studies the strategic access of single-server retrial queue with two types of customers, where priority is given according to their category. On the basis of this concept, a cognitive-radio network was developed as retrial queue with energy harvesting. Cognitive radio allows for a secondary user to opportunistically access the idle spectrum of a primary user (PU). Upon arrival of a primary user, the service given to the secondary user by the cognitive radio is interrupted, and the PU band is available for the primary user. After completion of service for the primary user, the PU band is again available to secondary users. Performance metrics are derived to study the equilibrium strategies of secondary users. A Stackelberg game was formulated and Nash equilibrium was derived for the noncooperative strategy of the secondary user. Game-theory concepts are incorporated with queuing theory ideas to obtain the net benefit for the noncooperative strategy and social benefit for cooperative strategy. Lastly, analytical results are verified with numerical examples, and the effects of energy-harvesting rate are discussed.

scholarly journals Throughput Maximization by Optimizing Secondary Users in a Cooperative Cognitive Radio Network using Hard Fusion Rules

2020 ◽  
Vol 9 (7) ◽  
pp. 801-804
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Network ◽  
Cooperative Spectrum Sensing ◽  
Primary User ◽  
Spectrum Efficiency ◽  
Throughput Maximization ◽  
Radio Network ◽  
Secondary Users ◽  
Fusion Rules ◽  
Channel Throughput

Cognitive radio network is a promising technology for enabling secondary users to utilize the licensed spectrum of the primary user without causing interference. The data trans- mitted by the secondary users through primary channel without affecting the primary user is known as channel throughput. In cooperative spectrum sensing(CSS) as the number of secondary users increases the channel throughput increases which in turn reduces the spectrum efficiency due to more spectrum wastage. Therefore in this paper, channel throughput is maximized by optimizing secondary users proposed and throughput for variable secondary users for OR and AND fusion rules is investigated. The optimal secondary users is estimated mathematically and simulation results shows the variation of throughput for variable number of secondaryusers

scholarly journals One-to-Many Relationship Based Kullback Leibler Divergence against Malicious Users in Cooperative Spectrum Sensing

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Noor Gul ◽  
Ijaz Mansoor Qureshi ◽  
Sadiq Akbar ◽  
Muhammad Kamran ◽  
Imtiaz Rasool
Keyword(s):  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Primary User ◽  
Performance Degradation ◽  
Fusion Center ◽  
Secondary Users ◽  
Malicious User ◽  
Leibler Divergence ◽  
Higher Weights ◽  
The Individual

The centralized cooperative spectrum sensing (CSS) allows unlicensed users to share their local sensing observations with the fusion center (FC) for sensing the licensed user spectrum. Although collaboration leads to better sensing, malicious user (MU) participation in CSS results in performance degradation. The proposed technique is based on Kullback Leibler Divergence (KLD) algorithm for mitigating the MUs attack in CSS. The secondary users (SUs) inform FC about the primary user (PU) spectrum availability by sending received energy statistics. Unlike the previous KLD algorithm where the individual SU sensing information is utilized for measuring the KLD, in this work MUs are identified and separated based on the individual SU decision and the average sensing statistics received from all other users. The proposed KLD assigns lower weights to the sensing information of MUs, while the normal SUs information receives higher weights. The proposed method has been tested in the presence of always yes, always no, opposite, and random opposite MUs. Simulations confirm that the proposed KLD scheme has surpassed the existing soft combination schemes in estimating the PU activity.

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