Journal articles: 'Spectrum sensing ; cognitive radio'

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Vinod Kumar Reddy, M., and K. Arun Kumar. "Cognitive Radio Networks- Spectrum Sensing." CVR Journal of Science & Technology 8, no. 1 (2015): 31–34. http://dx.doi.org/10.32377/cvrjst0806.

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Haykin, S., D. J. Thomson, and J. H. Reed. "Spectrum Sensing for Cognitive Radio." Proceedings of the IEEE 97, no. 5 (2009): 849–77. http://dx.doi.org/10.1109/jproc.2009.2015711.

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Khobragade, A. S., and R. D. Raut. "Hybrid Spectrum Sensing Method for Cognitive Radio." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 5 (2017): 2683. http://dx.doi.org/10.11591/ijece.v7i5.pp2683-2695.

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With exponential rise in the internet applications and wireless communications, higher and efficient data transfer rates are required. Hence proper and effective spectrum is the need of the hour, As spectrum demand increases there are limited number of bands available to send and receive the data. Optimizing the use of these bands efficiently is one of the tedious tasks. Various techniques are used to send the data at same time, but for that we have to know which bands are free before sending the data. For this purpose various spectrum sensing approaches came with variety of solutions. In this paper the sensing problem is tackled with the use of hybrid spectrum sensing method, This new networking paradox uses the Centralized concept of spectrum sensing and creates one of the most trusted spectrums sensing mechanism. This proposed technique is simulated using MATLAB software.This paper also provides comparative study of various spectrum sensing methodologies

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Kim, Joo-Seok, Hyun-So Lee, Sung-Ho Hwang, Jun-Ki Min, Ki-Hong Kim, and Kyung-Seok Kim. "Development of MAC Function for the Spectrum Sensing based on Cognitive Radio." Journal of the Korea Contents Association 8, no. 8 (2008): 28–36. http://dx.doi.org/10.5392/jkca.2008.8.8.028.

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Dev, Jenish, B. Mathumitha, M. Arul Bharathi, M. Benisha Rachel, A. Anu, and M. Benisha Rachel. "COOPERATIVE SPECTRUM SENSING IN COGNITIVE RADIO." INTERNATIONAL JOURNAL OF RECENT TRENDS IN ENGINEERING & RESEARCH 05, Special Issue 07 (2019): 145–50. http://dx.doi.org/10.23883/ijrter.conf.20190304.024.mrhue.

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C., Jayasri. "Spectrum Sensing in Cognitive Radio Network Using Cuckoo Search with Energy Heuristic." Journal of Advanced Research in Dynamical and Control Systems 51, SP3 (2020): 339–48. http://dx.doi.org/10.5373/jardcs/v12sp3/20201270.

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S., Vyshnavi Jyothi. "Spectrum Sensing based on Energy Detection for Cognitive Radio Using Poission Traffic." International Journal of Psychosocial Rehabilitation 24, no. 5 (2020): 2761–68. http://dx.doi.org/10.37200/ijpr/v24i5/pr201979.

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Kaarthik, K., P. T. Sivagurunathan, and S. Sivaranjani. "A REVIEW ON SPECTRUM SENSING METHODS FOR COGNITIVE RADIO NETWORKS." JOURNAL OF ADVANCES IN CHEMISTRY 12, no. 18 (2016): 5053–57. http://dx.doi.org/10.24297/jac.v12i18.5380.

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In Wireless Communication, Radio Spectrum is doing a vital role; for the future need it should use efficient. The existing system, it is not possible to use it efficiently where the allocation of spectrum is done based on fixed spectrum access (FSA) policy. Several surveys prove that it show the way to inefficient use of spectrum. An innovative technique is needed for spectrum utilization effectively. Using Dynamic spectrum access (DSA) policy, available spectrum can be exploited. Cognitive radio arises to be an attractive solution which introduces opportunistic usage of the frequency bands that are not commonly occupied by licensed users. Cognitive radios promote open spectrum allocation which is a clear departure from habitual command and control allocation process for radio spectrum usage. In short, it permits the formation of â€œinfrastructure-lessâ€ joint network clusters which is called Cognitive Radio Networks (CRN). Conversely the spectrum sensing techniques are needed to detect free spectrum. In this paper, different spectrum sensing techniques are analyzed.

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M, Anusha, Srikanth Vemuru, and T. Gunasekhar. "Transmission protocols in Cognitive Radio Mesh Networks." International Journal of Electrical and Computer Engineering (IJECE) 5, no. 6 (2015): 1446. http://dx.doi.org/10.11591/ijece.v5i6.pp1446-1451.

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A Cognitive Radio (CR) is a radio that can adjust its transmission limit based on available spectrum in its operational surroundings. Cognitive Radio Network (CRN) is made up of both the licensed users and unlicensed users with CR enable and disabled radios. CR’S supports to access dynamic spectrum and supports secondary user to access underutilized spectrum efficiently, which was allocated to primary users. In CRN’S most of the research was done on spectrum allocation, spectrum sensing and spectrum sharing. In this literature, we present various Medium Access (MAC) protocols of CRN’S. This study would provide an excellent study of MAC strategies.

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Cui, Chua Nan. "Study of Cognitive Radio Spectrum Sensing Algorithm Based on Compressed Sensing." Applied Mechanics and Materials 602-605 (August 2014): 3639–42. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.3639.

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As the latest progress in modern signal processing, Compressed Sensing (CS) has great potential for application in the field of cognitive radio and other fields. In this paper, the basic principle of compressed sensing technology and its application in cognitive radio spectrum perception key technologies are studied, achieving innovative research results. Spectrum sensing technology in cognitive radio

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Guo, Jie, Da Hai Jing, and Yan Gu. "Spectrum Sensing with Clustering in Cognitive Radio Networks." Applied Mechanics and Materials 651-653 (September 2014): 1941–44. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.1941.

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The main task of spectrum sensing in cognitive radio network is to decide whether the primary user is occupying the specific spectrum band or not. So the main purpose of spectrum sensing is to design a detector with better detection performance. This paper studies a spectrum sensing method with clustering under cognitive radio networks. We studied the cooperative spectrum sensing model with clustering by hard fusion rule, and also proposed the simulation model and steps of this cluster-based spectrum sensing problem under Majority rule. Simulation results show that the spectrum sensing method with clustering has better performance than the other methods.

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Timothy, R. Joash Paul, and J. Christopher Clement. "Spectrum Sensing of Cognitive Radio – A Survey." International Journal of Electrical and Electronics Research 4, no. 1 (2016): 20–29. http://dx.doi.org/10.37391/ijeer.040105.

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Cognitive radio is emerging as one of the most promising aspects regarding the efficient usage of the radio spectrum and also on a non-interference basis. However the most challenging part is the effective detection of primary users (PUs). Nowadays there are a lot of threats from attackers who use techniques like data falsification, primary user emulations to cause harm to the users, so we need to address them with proper and efficient solutions. So in this survey we address the various threats and the challenges faced in cognitive radio environments and also we are here to discuss the various sampling techniques that could be used for the purpose of proper detection.

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Sridhar, Bommidi, and Srinivasulu Tadisetty. "Efficient Recursive Least Square Technique for Spectrum Sensing in Cognitive Radio Networks." International Journal of Business Data Communications and Networking 15, no. 2 (2019): 1–14. http://dx.doi.org/10.4018/ijbdcn.2019070101.

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Cognitive radio-based systems rely on spectrum sensing techniques to detect whitespaces to exploit. Cognitive radio (CR) is an attractive approach to face the shortage in the electromagnetic spectrum resources and improve the overall spectrum utilization. However, Energy detectors perform far from optimally by affecting the performance of the underlying system. In this article, two spectrum-sensing techniques are considered for CR networks; one based on energy detection and the other based on multi-taper spectral estimation (MSE). This article proposes a new method to optimize the overall performance in cooperative spectrum sensing in cognitive radio (CR) networks. An efficient recursive least square (ERLS)-based approach is proposed in order to optimize the overall performance to monitor the primary user active or inactive stage with use of secondary user while receiving data. An energy detector (ED) and multi-taper (MTM) spectrum sensing techniques are examined as local spectrum sensing techniques. Finally, a genetic algorithm is compared with the proposed system to show the system effectiveness.

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Solanki, Surendra, Vasudev Dehalwar, and Jaytrilok Choudhary. "Deep Learning for Spectrum Sensing in Cognitive Radio." Symmetry 13, no. 1 (2021): 147. http://dx.doi.org/10.3390/sym13010147.

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The detection of primary user signals is essential for optimum utilization of a spectrum by secondary users in cognitive radio (CR). The conventional spectrum sensing schemes have the problem of missed detection/false alarm, which hampers the proper utilization of spectrum. Spectrum sensing through deep learning minimizes the margin of error in the detection of the free spectrum. This research provides an insight into using a deep neural network for spectrum sensing. A deep learning based model, “DLSenseNet”, is proposed, which exploits structural information of received modulated signals for spectrum sensing. The experiments were performed using RadioML2016.10b dataset and the outcome was studied. It was found that “DLSenseNet” provides better spectrum detection than other sensing models.

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Kochhar, Shewangi, and Roopali Garg. "Spectrum Sensing for Cognitive Radio Using Genetic Algorithm." International Journal of Online Engineering (iJOE) 14, no. 09 (2018): 190. http://dx.doi.org/10.3991/ijoe.v14i09.9064.

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Cognitive Radio has been skillful technology to improve the spectrum sensing as it enables Cognitive Radio to find Primary User (PU) and let secondary User (SU) to utilize the spectrum holes. However detection of PU leads to longer sensing time and interference. Spectrum sensing is done in specific “time frame” and it is further divided into Sensing time and transmission time. Higher the sensing time better will be detection and lesser will be the probability of false alarm. So optimization technique is highly required to address the issue of trade-off between sensing time and throughput. This paper proposed an application of Genetic Algorithm technique for spectrum sensing in cognitive radio. Here results shows that ROC curve of GA is better than PSO in terms of normalized throughput and sensing time. The parameters that are evaluated are throughput, probability of false alarm, sensing time, cost and iteration.

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ShahnawazShaikh, Md, and Kamlesh Gupta. "Analysis of Cognitive Radio Spectrum Sensing Techniques." International Journal of Computer Applications 102, no. 12 (2014): 1–7. http://dx.doi.org/10.5120/17864-8805.

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Jasim, Sabbar Insaif, Mustafa Mahmood Akawee, and Raed Abdulkareem Hasan. "Spectrum sensing approaches in cognitive radio network." Periodicals of Engineering and Natural Sciences (PEN) 7, no. 4 (2019): 1555. http://dx.doi.org/10.21533/pen.v7i4.824.

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Mohammed, Gamal Abdel Fadeel. "ADAPTIVE SPECTRUM SENSING IN COGNITIVE RADIO NETWORKS." JES. Journal of Engineering Sciences 40, no. 3 (2012): 867–75. http://dx.doi.org/10.21608/jesaun.2012.114416.

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Zhang, Wen, Jiawei Yang, Qi Yan, and Li Zhang. "Optimal multiband spectrum sensing in cognitive radio." IEICE Electronics Express 7, no. 20 (2010): 1557–63. http://dx.doi.org/10.1587/elex.7.1557.

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Abdul, Rishbiya, Riya Kuriakose, Sibila M., Lakshmi C., and Reshmi S. "Adaptive Spectrum Sensing in Cognitive Radio Networks." International Journal of Computer Applications 179, no. 45 (2018): 10–16. http://dx.doi.org/10.5120/ijca2018917117.

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Karthikeyan, C. S., and M. Suganthi. "Optimized Spectrum Sensing Algorithm for Cognitive Radio." Wireless Personal Communications 94, no. 4 (2016): 2533–47. http://dx.doi.org/10.1007/s11277-016-3642-9.

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Suresh, Shanthan, Shankar Prakriya, and Manav R. Bhatnagar. "Kurtosis based spectrum sensing in cognitive radio." Physical Communication 5, no. 3 (2012): 230–39. http://dx.doi.org/10.1016/j.phycom.2012.02.001.

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Pu Wang, Jun Fang, Ning Han, and Hongbin Li. "Multiantenna-Assisted Spectrum Sensing for Cognitive Radio." IEEE Transactions on Vehicular Technology 59, no. 4 (2010): 1791–800. http://dx.doi.org/10.1109/tvt.2009.2037912.

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Nagaraj, Santosh V. "Entropy-based spectrum sensing in cognitive radio." Signal Processing 89, no. 2 (2009): 174–80. http://dx.doi.org/10.1016/j.sigpro.2008.07.022.

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Han, Zhu, Rongfei Fan, and Hai Jiang. "Replacement of Spectrum Sensing in Cognitive Radio." IEEE Transactions on Wireless Communications 8, no. 6 (2009): 2819–26. http://dx.doi.org/10.1109/twc.2009.080603.

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Zhao, Bingxuan, and Shigenobu Sasaki. "Active spectrum sensing for cognitive radio networks." Transactions on Emerging Telecommunications Technologies 26, no. 5 (2013): 789–99. http://dx.doi.org/10.1002/ett.2731.

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Roy, Sanjay Dhar, Sumit Kundu, Gianluigi Ferrari, and Riccardo Raheli. "Cognitive radio CDMA networking with spectrum sensing." International Journal of Communication Systems 27, no. 10 (2012): 1582–600. http://dx.doi.org/10.1002/dac.2421.

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Guo, Chen, Ming Jin, Qinghua Guo, and Youming Li. "Antieigenvalue-Based Spectrum Sensing for Cognitive Radio." IEEE Wireless Communications Letters 8, no. 2 (2019): 544–47. http://dx.doi.org/10.1109/lwc.2018.2879339.

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Kokare, Sheetal, and R. D. Kamble. "Spectrum Sensing Techniques in Cognitive Radio Cycle." International Journal of Engineering Trends and Technology 9, no. 1 (2014): 16–20. http://dx.doi.org/10.14445/22315381/ijett-v9p204.

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Gavrilovska, Liljana, and Vladimir Atanasovski. "Spectrum Sensing Framework for Cognitive Radio Networks." Wireless Personal Communications 59, no. 3 (2011): 447–69. http://dx.doi.org/10.1007/s11277-011-0239-1.

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Mao, Hong Yan. "Research of Spectrum Detecting Technology on Cognitive Radio." Applied Mechanics and Materials 433-435 (October 2013): 911–14. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.911.

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Cognitive radio (CR) is an intelligent spectrum sharing technology. It can improve the spectrum utilization by sensing spectrum environment, learning intelligently and adjusting the transmission parameters. The discussion is focused on spectrum detecting technology in cognitive radio. Spectrum detecting algorithms are analyzed and compared .The centralized cooperative spectrum detection method, distributed cooperative spectrum sensing method and relay cooperative spectrum detection method are analyzed also.

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Xu, Bin Bin. "Gradual Compressive Spectrum Sensing for Wideband Cognitive Radio Network." Applied Mechanics and Materials 441 (December 2013): 915–19. http://dx.doi.org/10.4028/www.scientific.net/amm.441.915.

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In this paper, a Gradual Compressive spectrum sensing method is presented for collaborated users in wideband cognitive radio (CR) network. By taking the advantage of compressive sensing (CS), we can reconstructs the wideband spectrum using sub-Nyquist samples. Furthermore, we employ gradual signal acquisition and recovery that can terminate the process once the result of spectral recovery converge. This proposed method is flexible for different wideband signal sparsity and signal to noise ratio, leading to enhanced network throughput. Simulations testify the effectiveness of the proposed method in CR networks.

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Kobayashi, Ricardo Tadashi, Aislan Gabriel Hernandes, Mario Lemes Proença, and Taufik Abrao. "Improved MB Cognitive Radio Spectrum Sensing Using Wavelet Spectrum Filtering." Journal of Circuits, Systems and Computers 28, no. 08 (2019): 1950136. http://dx.doi.org/10.1142/s0218126619501366.

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In cognitive radio (CR), the sensed aggregate bandwidth could be as large as several GHz. This is especially challenging if the bandwidths and central frequencies of the sensed signals are unknown and need to be estimated. This work discusses a new improved method for MB spectrum sensing (iMB-SS) based on edge detection and using Wavelet Spectrum Filtering. The proposed iMB-SS method uses a Welch power spectrum density (PSD) estimate and a multi-scale Wavelet approach to reveal the spectrum transition (edges), which is deployed to characterize the spectrum occupancy in CR scenarios where the operation frequencies of the primary users (PUs) are unknown. The focus of this work lies in improving the performance of the MB spectrum sensor, particularly by refining the spectral edge location and reducing misleading detection. A comprehensive analytical description and numerical analysis have been carried out by focusing on orthogonal-frequency-division-multiplexing (OFDM) signal applications in CR networks. Numerical results corroborate the effectiveness of the proposed iMB-SS approach. The simulated results for the multiple-PU’s OFDM-based transmission CR system demonstrate that the proposed iMB-SS method can achieve high performance even under low signal-to-noise ratio (SNR) regime, turning it out as an attractive choice for SS in the MB CR systems.

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Guo, Jie, Lei Tang, and Yan Dong. "Single-User Cyclic Spectrum Detector Design in Cognitive Radio." Applied Mechanics and Materials 670-671 (October 2014): 1301–4. http://dx.doi.org/10.4028/www.scientific.net/amm.670-671.1301.

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Cognitive radio can improve the spectrum utilization efficiency of spectrum band. So spectrum sensing is the key technology in cognitive radio network. Our main task is to design a cyclic detector with better detection performance than energy detector. This paper studies single-user spectrum detector in cognitive radio networks, including cyclic spectrum detector and energy detector. We firstly analyzed the energy spectrum sensing mathematic model for comparison, then we proposed the new simulation model and detailed simulation steps of this cyclic spectrum sensing detector. Simulation results demonstrate that the cyclic spectrum detector has better performance than energy detector while the SNR of channel condition is low.

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Wasonga, Fidel, Thomas O. Olwal, and Adnan Abu-Mahfouz. "Improved Two-Stage Spectrum Sensing for Cognitive Radio Networks." Journal of Advanced Computational Intelligence and Intelligent Informatics 23, no. 6 (2019): 1052–62. http://dx.doi.org/10.20965/jaciii.2019.p1052.

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Cognitive radio employs an opportunistic spectrum access approach to ensure efficient utilization of the available spectrum by secondary users (SUs). To allow SUs to access the spectrum opportunistically, the spectrum sensing process must be fast and accurate to avoid possible interference with the primary users. Previously, two-stage spectrum sensing methods were proposed that consider the sensing time and sensing accuracy parameters independently at the cost of a non-optimal spectrum sensing performance. To resolve this non-optimality issue, we consider both parameters in the design of our spectrum sensing scheme. In our scheme, we first derive optimal thresholds using an optimization equation with an objective function of maximizing the probability of detection, subject to the minimal probability of error. We then minimize the average spectrum sensing time using signal-to-noise ratio estimation. Our simulation results show that the proposed improved two-stage spectrum sensing (ITSS) scheme provides a 4%, 7%, and 6% better probability of detection accuracy rate than two-stage combinations of energy detection (ED) and maximum eigenvalue detection, energy detection and cyclostationary feature detection (CFD), and ED and combination of maximum-minimum eigenvalue (CMME) detection, respectively. The ITSS is superior also to single-stage ED by 19% and shows an improved average spectrum sensing time.

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Wilfred, Adigwe, and Okonkwo O.R. "A review of cyclostationary feature detection based spectrum sensing technique in cognitive radio networks." E3 Journal of Scientific Research 4, no. 3 (2016): 041–47. http://dx.doi.org/10.18685/ejsr(4)3_ejsr-16-010.

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Khader, Ammar Abdul-Hamed, and Zozan Azeez Ayoub. "The Cognitive Radio and Internet of Things." European Journal of Engineering Research and Science 5, no. 8 (2020): 899–903. http://dx.doi.org/10.24018/ejers.2020.5.8.2012.

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Cognitive Radio (CR) and Internet of Things (IoT) is an effective step into the smart technology world. Several frameworks are proposed to build CR and IoT. The phases of the interconnection between IoT and CR is; spectrum sensing, spectrum sharing, and spectrum management. This paper presents a survey of CR based IoT and mentions some previous works. It highlights with details the spectrum sensing stage for both narrowband and wideband.

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Dayana, R., and R. Kumar. "Modified isotropic orthogonal transform algorithm-universal filtered multicarrier transceiver for 5G cognitive radio application." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 4 (2019): 3100. http://dx.doi.org/10.11591/ijece.v9i4.pp3100-3107.

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Rapid developments in modern wireless communication permit the trade of spectrum scarcity. Higher data rate and wider bandwidth emerge the development in growing demand of wireless communication system. The innovative solution for the spectrum scarcity is cognitive radio (CR). Cognitive radio is the significant technology used to utilize the spectrum effectively. The important aspect of CR is sensing the spectrum band and detects the presence or absence of the primary user in the licensed band. Moreover, another serious issue in next generation (5G) wireless communication is to decide the less complex 5G waveform candidate for achieving higher data rate, low latency and better spectral efficiency. Universal filtered multi-carrier (UFMC) is one of the noticeable waveform candidates for 5G and its applications. In this article, we investigate the spectrum sensing methods in multi-carrier transmission for cognitive radio network applications. Especially, we integrate the sensing algorithm into UFMC transceiver to analyze the spectral efficiency, higher data rates and system complexity. Through the simulation results, we prove that the UFMC based cognitive radio applications outperform the existing Orthogonal Frequency Division Multiplexing (OFDM) based CR applications.

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Khan, Muhammad Sajjad, Liaqat Khan, Noor Gul, Muhammad Amir, Junsu Kim, and Su Min Kim. "Support Vector Machine-Based Classification of Malicious Users in Cognitive Radio Networks." Wireless Communications and Mobile Computing 2020 (July 18, 2020): 1–11. http://dx.doi.org/10.1155/2020/8846948.

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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.

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Wang, Weiwei, Jun Cai, Attahiru S. Alfa, Anthony C. K. Soong, and Simin Li. "Adaptive dual-radio spectrum-sensing scheme in cognitive radio networks." Wireless Communications and Mobile Computing 13, no. 14 (2011): 1247–62. http://dx.doi.org/10.1002/wcm.1178.

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Kaszuba-Chęcińska, Anna, Radosław Chęciński, Piotr Gajewski, and Jerzy Łopatka. "Cognitive Radio MANET Waveform Design and Evaluation." Sensors 21, no. 4 (2021): 1052. http://dx.doi.org/10.3390/s21041052.

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The problem of waveform construction for mobile ad hoc networks with cognitive radio (MANET-CR) is discussed. This is the main limitation to widely use this very attractive technique, which does not need the deployment of expensive communication infrastructure. Two main questions correspond to MANET-CR effectiveness: spectrum sensing and spectrum sharing. The paper presents the structure of CR nodes that enables Opportunistic Spectrum Sharing. Procedures for advanced Dynamic Spectrum Management together with the concept of policy-based radio and a sensing method are presented. In the proposed system, the basic policy is to avoid interference generated by other users or jammers. The experiments were performed in a real environment, using the elaborated testbed. The results show that the use of sensing and cognitive management mechanisms enable more efficient use of the spectrum while maintaining reasonable overhead values related to the management procedures.

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Talajiya, Preet Hitesh, Aniket Pramod Gangurde, U. Ragavendran, and Hariharan Murali. "Cognitive Radio Networks and Spectrum Sensing: A Review." International Journal of Online and Biomedical Engineering (iJOE) 16, no. 13 (2020): 4. http://dx.doi.org/10.3991/ijoe.v16i13.18549.

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The wireless spectrum demand is at a constant rise in contrast to its scarcity. Cognitive Radio Networks(CRN’s) as a notion was first brought into the light to tackle this issue. A CRN is an intelligent network that dynamically changes its characteristics through the process of Spectrum Sensing and adapts to the convenience of the environment it is in. This article presents a basic overview of what CRN’s are. The major contributions of this paper lie in a comparative study of CRN’s and Spectrum Sensing in recent years as well as its main challenges and applications. This review shall help current and new researchers in the field to look for future outlooks and give them a basic run-through of CRN’s and Spectrum Sensing and their characteristics.

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Khalid, Waqas, and Heejung Yu. "Spatial–Temporal Sensing and Utilization in Full Duplex Spectrum-Heterogeneous Cognitive Radio Networks for the Internet of Things." Sensors 19, no. 6 (2019): 1441. http://dx.doi.org/10.3390/s19061441.

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The continuous growth of interconnected devices in the Internet of Things (IoT) presents a challenge in terms of network resources. Cognitive radio (CR) is a promising technology thatcan address the IoT spectral demands by enabling an opportunistic spectrum access (OSA) scheme. The application of full duplex (FD) radios in spectrum sensing enables secondary users (SUs) to perform sensing and transmission simultaneously, and improves the utilization of the spectrum. However, random and dense distributions of FD-enabled SU transmitters (FD-SU TXs) with sensing capabilities in small-cell CR-IoT environments poses new challenges, and creates heterogeneous environments with different spectral opportunities. In this paper, we propose a spatial and temporal spectral-hole sensing framework for FD-SU TXs deployed in CR-IoT spectrum-heterogeneous environment. Incorporating the proposed sensing model, we present the analytical formulation and an evaluation of a utilization of spectrum (UoS) scheme for FD-SU TXs present at different spatialpositions. The numerical results are evaluated under different network and sensing parameters to examine the sensitivities of different parameters. It is demonstrated that self-interference, primary user activity level, and the sensing outcomes in spatial and temporal domains have a significant influence on the utilization performance of spectrum.

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Ranganathan, Raghuram, Robert Qiu, Zhen Hu, et al. "Cognitive Radio for Smart Grid: Theory, Algorithms, and Security." International Journal of Digital Multimedia Broadcasting 2011 (2011): 1–14. http://dx.doi.org/10.1155/2011/502087.

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Recently, cognitive radio and smart grid are two areas which have received considerable research impetus. Cognitive radios are intelligent software defined radios (SDRs) that efficiently utilize the unused regions of the spectrum, to achieve higher data rates. The smart grid is an automated electric power system that monitors and controls grid activities. In this paper, the novel concept of incorporating a cognitive radio network as the communications infrastructure for the smart grid is presented. A brief overview of the cognitive radio, IEEE 802.22 standard and smart grid, is provided. Experimental results obtained by using dimensionality reduction techniques such as principal component analysis (PCA), kernel PCA, and landmark maximum variance unfolding (LMVU) on Wi-Fi signal measurements are presented in a spectrum sensing context. Furthermore, compressed sensing algorithms such as Bayesian compressed sensing and the compressed sensing Kalman filter is employed for recovering the sparse smart meter transmissions. From the power system point of view, a supervised learning method called support vector machine (SVM) is used for the automated classification of power system disturbances. The impending problem of securing the smart grid is also addressed, in addition to the possibility of applying FPGA-based fuzzy logic intrusion detection for the smart grid.

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., Shewangi. "COGNITIVE RADIO: CONCEPTS, SPECTRUM SENSING AND ITS STANDARDS." International Journal of Advanced Research in Computer Science 8, no. 8 (2017): 39–41. http://dx.doi.org/10.26483/ijarcs.v8i8.4629.

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Shobana, S., R. Saravanan, and R. Muthaiah. "Optimal Spectrum Sensing Approach on Cognitive Radio Systems." Research Journal of Applied Sciences, Engineering and Technology 6, no. 18 (2013): 3419–22. http://dx.doi.org/10.19026/rjaset.6.3659.

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LU, Luxi, Wei JIANG, Haige XIANG, and Wu LUO. "Adaptive Spectrum Sensing/Transmission Scheduling for Cognitive Radio." IEICE Transactions on Communications E95-B, no. 2 (2012): 635–38. http://dx.doi.org/10.1587/transcom.e95.b.635.

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NARIEDA, Shusuke. "Improved MCAS Based Spectrum Sensing in Cognitive Radio." IEICE Transactions on Communications E101.B, no. 3 (2018): 915–23. http://dx.doi.org/10.1587/transcom.2017ebp3134.

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Saifan, Ramzi, Ghazi Al-Sukar, and Rawaa Al-Ameer. "Energy Efficient Cooperative Spectrum Sensing in Cognitive Radio." International journal of Computer Networks & Communications 8, no. 2 (2016): 13–24. http://dx.doi.org/10.5121/ijcnc.2016.8202.

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Jain, Praneeth P., Pradeep R. Pawar, Prajwal Patil, and Devasis Pradhan. "Narrowband Spectrum Sensing in Cognitive Radio Detection Methodologies." International Journal of Computer Sciences and Engineering 7, no. 11 (2019): 105–13. http://dx.doi.org/10.26438/ijcse/v7i11.105113.

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Journal articles on the topic 'Spectrum sensing ; cognitive radio'

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