scholarly journals FPGA Implementation of Adaptive Absolute SCORE Algorithm for Cognitive Radio Spectrum Sensing with WTM and LFA

2021 ◽  
Vol 14 (1) ◽  
pp. 1-11
Author(s):  
Shanigarapu Kumar ◽  
◽  
Kalagadda Bikshalu ◽  
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Finite Impulse Response ◽  
Primary User ◽  
Radio Spectrum ◽  
System Capacity ◽  
Median Filters ◽  
High Area ◽  
Wallace Tree Multiplier ◽  
Radio Sensitivity

Cognitive Radio (CR) is generally a wireless communication system that has the ability to improve the network’s system-capacity. Since, the white space or temporally unused spectrum are used to enhance the systemcapacity and the important operation involved in the cognition cycle is spectrum sensing. This spectrum sensing supports the Cognitive Radio users to adjust with the environment by identifying the white/vacant spaces without creating any interference to the primary user communication. The traditional filters such as Finite Impulse Response (FIR) filters and median filters used in the spectrum sensing obtains high area utilization in Cognitive Radio. In order to overcome this, an Adaptive Absolute SCORE (AAS) technique is developed based on the FIR for improving the sensing function and radio sensitivity. The area and frequency of the AAS are enhanced by using the Wallace tree multiplier (WTM) and Ladner-Fischer Adder (LFA) in the design of the FIR. The proposed architecture used for the spectrum sensing is named as AAS-WTM-LFA. This AAS-WTM-LFA architecture is developed in the Xilinx tool for different Virtex devices. The performance of AAS-WTM-LFA is analyzed in terms of LUT, slices, flip flops, bonded Input and Output Block (IOB), frequency and power. Additionally, the quality of signal processed through the AAS-WTM-LFA architecture is analyzed as Bit Error Rate (BER) and False Acceptance Rate (FAR). Additionally, the AAS-WTM-LFA architecture is compared with ACS, AAS, AAS-CSLA, AAS-R8-CSA and AASR8-CSLA. The number of LUT for AAS-WTM-LFA architecture is 247 for Spartan 6 device, that is less when compared to the remaining architectures.

scholarly journals Cooperative Spectrum Sensing Based on Multi-Features Combination Network in Cognitive Radio Network

Entropy ◽  
2022 ◽  
Vol 24 (1) ◽  
pp. 129
Author(s):  
Mingdong Xu ◽  
Zhendong Yin ◽  
Yanlong Zhao ◽  
Zhilu Wu
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Network ◽  
Cooperative Spectrum Sensing ◽  
Signal To Noise Ratio ◽  
Primary User ◽  
Radio Spectrum ◽  
Local Information ◽  
Channel State ◽  
Single Node

cognitive radio, as a key technology to improve the utilization of radio spectrum, acquired much attention. Moreover, spectrum sensing has an irreplaceable position in the field of cognitive radio and was widely studied. The convolutional neural networks (CNNs) and the gate recurrent unit (GRU) are complementary in their modelling capabilities. In this paper, we introduce a CNN-GRU network to obtain the local information for single-node spectrum sensing, in which CNN is used to extract spatial feature and GRU is used to extract the temporal feature. Then, the combination network receives the features extracted by the CNN-GRU network to achieve multifeatures combination and obtains the final cooperation result. The cooperative spectrum sensing scheme based on Multifeatures Combination Network enhances the sensing reliability by fusing the local information from different sensing nodes. To accommodate the detection of multiple types of signals, we generated 8 kinds of modulation types to train the model. Theoretical analysis and simulation results show that the cooperative spectrum sensing algorithm proposed in this paper improved detection performance with no prior knowledge about the information of primary user or channel state. Our proposed method achieved competitive performance under the condition of large dynamic signal-to-noise ratio.

scholarly journals Spectrum Sensing of Cognitive Radio – A Survey

2016 ◽  
Vol 4 (1) ◽  
pp. 20-29
Author(s):  
R. Joash Paul Timothy ◽  
J. Christopher Clement
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Primary User ◽  
Radio Spectrum ◽  
Efficient Solutions ◽  
Sampling Techniques ◽  
Primary Users

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.

scholarly journals Implementation of Eigenvalue Based Cooperative Spectrum Sensing in Cognitive Radio

2020 ◽  
pp. 495-498
Author(s):  
Aswatha R ◽  
Nithya S ◽  
Dhivya S ◽  
Priyadharsini S ◽  
Soundararaj R D
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Primary User ◽  
Radio Spectrum ◽  
Communication Services ◽  
Good Efficiency ◽  
Remote Areas ◽  
Effective Technology ◽  
New Applications

Wireless communication services have been growing in recent years because of easy implementation and evidence of connectivity in remote areas. With this evolution, high-quality connectivity to the wireless frequency spectrum has led to largespectrum use. Therefore the available radio spectrum is in great demand. Radio spectrum is a finite resource and hard to assign spectrum frequency for new applications. Cognitive radio (CR) is an effective technology which makes it possible to use it effectively.The aim is to introduce cooperative spectrum sensing based on eigenvalue using NI-USRP hardware platform and achieve good efficiency. In this article, a transmitter is used as primary user and implemented in hardwareby using two cognitive radio users. The implementation is achieved with LABVIEW and detection performance is evaluated.

scholarly journals Second order Kalman filtering channel estimation and machine learning methods for spectrum sensing in cognitive radio networks

2021 ◽  
Author(s):  
Olusegun Peter Awe ◽  
Daniel Adebowale Babatunde ◽  
Sangarapillai Lambotharan ◽  
Basil AsSadhan
Keyword(s):  
Machine Learning ◽  
Kalman Filter ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Clustering Algorithm ◽  
Polynomial Regression ◽  
Primary User ◽  
Radio Networks ◽  
Second Order

AbstractWe address the problem of spectrum sensing in decentralized cognitive radio networks using a parametric machine learning method. In particular, to mitigate sensing performance degradation due to the mobility of the secondary users (SUs) in the presence of scatterers, we propose and investigate a classifier that uses a pilot based second order Kalman filter tracker for estimating the slowly varying channel gain between the primary user (PU) transmitter and the mobile SUs. Using the energy measurements at SU terminals as feature vectors, the algorithm is initialized by a K-means clustering algorithm with two centroids corresponding to the active and inactive status of PU transmitter. Under mobility, the centroid corresponding to the active PU status is adapted according to the estimates of the channels given by the Kalman filter and an adaptive K-means clustering technique is used to make classification decisions on the PU activity. Furthermore, to address the possibility that the SU receiver might experience location dependent co-channel interference, we have proposed a quadratic polynomial regression algorithm for estimating the noise plus interference power in the presence of mobility which can be used for adapting the centroid corresponding to inactive PU status. Simulation results demonstrate the efficacy of the proposed algorithm.

scholarly journals Spectrum Sensing for Cognitive Radio Using Genetic Algorithm

2018 ◽  
Vol 14 (09) ◽  
pp. 190 ◽  
Author(s):  
Shewangi Kochhar ◽  
Roopali Garg
Keyword(s):  
Genetic Algorithm ◽  
Cognitive Radio ◽  
False Alarm ◽  
Spectrum Sensing ◽  
Optimization Technique ◽  
Primary User ◽  
Time Frame ◽  
Probability Of False Alarm ◽  
Sensing Time ◽  
Better Than

<p>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.</p>

scholarly journals Analysis of the Impact of Detection Threshold Adjustments and Noise Uncertainty on Energy Detection Performance in MIMO-OFDM Cognitive Radio Systems

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 631
Author(s):  
Josip Lorincz ◽  
Ivana Ramljak ◽  
Dinko Begušić
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Detection Method ◽  
Detection Threshold ◽  
Energy Detection ◽  
Primary User ◽  
Noise Uncertainty ◽  
Cognitive Radio Systems ◽  
Radio Systems ◽  
The Impact

Due to the capability of the effective usage of the radio frequency spectrum, a concept known as cognitive radio has undergone a broad exploitation in real implementations. Spectrum sensing as a core function of the cognitive radio enables secondary users to monitor the frequency band of primary users and its exploitation in periods of availability. In this work, the efficiency of spectrum sensing performed with the energy detection method realized through the square-law combining of the received signals at secondary users has been analyzed. Performance evaluation of the energy detection method was done for the wireless system in which signal transmission is based on Multiple-Input Multiple-Output—Orthogonal Frequency Division Multiplexing. Although such transmission brings different advantages to wireless communication systems, the impact of noise variations known as noise uncertainty and the inability of selecting an optimal signal level threshold for deciding upon the presence of the primary user signal can compromise the sensing precision of the energy detection method. Since the energy detection may be enhanced by dynamic detection threshold adjustments, this manuscript analyses the influence of detection threshold adjustments and noise uncertainty on the performance of the energy detection spectrum sensing method in single-cell cognitive radio systems. For the evaluation of an energy detection method based on the square-law combining technique, the mathematical expressions of the main performance parameters used for the assessment of spectrum sensing efficiency have been derived. The developed expressions were further assessed by executing the algorithm that enabled the simulation of the energy detection method based on the square-law combining technique in Multiple-Input Multiple-Output—Orthogonal Frequency Division Multiplexing cognitive radio systems. The obtained simulation results provide insights into how different levels of detection threshold adjustments and noise uncertainty affect the probability of detection of primary user signals. It is shown that higher signal-to-noise-ratios, the transmitting powers of primary user, the number of primary user transmitting and the secondary user receiving antennas, the number of sampling points and the false alarm probabilities improve detection probability. The presented analyses establish the basis for understanding the energy detection operation through the possibility of exploiting the different combinations of operating parameters which can contribute to the improvement of spectrum sensing efficiency of the energy detection method.

scholarly journals Cognitive Radio Spectrum Sensing Technique Using M-ary QAM for Different Fading Environments

2017 ◽  
Vol 12 (4) ◽  
pp. 43-52
Author(s):  
Jai Sukh Paul Singh ◽  
Mritunjay Kumar Rai ◽  
Gulshan Kumar ◽  
Hye-jin Kim
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Radio Spectrum ◽  
Fading Environments

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.

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