scholarly journals A Cepstrum-Based Spectrum Sensing Approach for Detecting Spread Spectrum Signals

2021 ◽  
Vol 2128 (1) ◽  
pp. 012003
Author(s):  
Azza Moawad ◽  
Koffi-Clément Yao ◽  
Ali Mansour ◽  
Roland Gautier
Keyword(s):  
Spectrum Sensing ◽  
Spread Spectrum ◽  
Energy Detection ◽  
Cognitive Systems ◽  
Detection Accuracy ◽  
Detection Techniques ◽  
Legitimate User ◽  
Cepstral Analysis ◽  
Spread Spectrum Signals

Abstract In this manuscript, we introduce a semi-blind spectrum sensing technique based on cepstral analysis for interweave cognitive systems. The misdetection problem of spread spectrum signals leads to erroneous sensing results, which affect the quality-of-service of a legitimate user. The simplicity and accuracy of cepstral analysis approaches make them reliable for signals detection. Therefore, we formulate the averaged autocepstrum detection technique that utilizes the strength of the autocepstral features of spread spectrum signals. The proposed technique is compared with the energy detection and eigenvalue-based detection techniques and shows reliability and efficacy in terms of detection accuracy.

scholarly journals Design of Hybrid Blind Detection Based Spectrum Sensing Technique

2020 ◽  
Vol 12 (4) ◽  
pp. 575-583
Author(s):  
V. Sharma ◽  
S. Joshi
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Primary User ◽  
Research Area ◽  
Detection Accuracy ◽  
Blind Detection ◽  
Spectrum Utilization ◽  
Detection Techniques ◽  
Generation Spectrum

Cognitive Radio is a boon to efficient utilization of spectrum to meet the demand of next generation. Spectrum Sensing (SS) is an active research area, essential to meet the requirement of efficient spectrum utilization as it detects the vacant bands. This paper develops a Hybrid Blind Detection (HBD) technique for cooperative spectrum sensing which combines the Energy Detector (ED) and the Anti-Eigen Value Detection (AVD) techniques together to enhance the detection accuracy of a cognitive radio. Collaboration among the cognitive users is achieved to reduce the error and hard fusion based detection is implemented to detect the existence of primary user. The detection accuracy of the design is evaluated with respect to detection probabilities and the results are examined for improvements with the traditional two stage detection techniques. Fusion rules for the cooperative environment are implemented and compared to detect majority rule suitable for the proposed design.

Improved Two-Stage Spectrum Sensing for Cognitive Radio Networks

2019 ◽  
Vol 23 (6) ◽  
pp. 1052-1062
Author(s):  
Fidel Wasonga ◽  
Thomas O. Olwal ◽  
Adnan Abu-Mahfouz ◽  
◽  
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Feature Detection ◽  
Energy Detection ◽  
Probability Of Detection ◽  
Detection Accuracy ◽  
Two Stage ◽  
Probability Of Error ◽  
Average Spectrum ◽  
Sensing Time

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.

scholarly journals Enhanced Sensing and Sum-Rate Analysis in a Cognitive Radio-Based Internet of Things

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2525 ◽  
Author(s):  
Md. Sipon Miah ◽  
Kazi Mowdud Ahmed ◽  
Md. Khairul Islam ◽  
Md. Ashek Raihan Mahmud ◽  
Md. Mahbubur Rahman ◽  
...  
Keyword(s):  
Cognitive Radio ◽  
Internet Of Things ◽  
Spectrum Sensing ◽  
Energy Detection ◽  
Uncertain Environment ◽  
Detection Techniques ◽  
Leibler Divergence ◽  
Local Decision ◽  
Sum Rate ◽  
Sensing Performance

Spectrum sensing plays a vital role in cognitive radio networks (CRNs) for identifying the spectrum hole. However, an individual cognitive radio user in a CRN does not obtain sufficient sensing performance and sum rate of the primary and secondary links to support the future Internet of Things (IoT) using conventional detection techniques such as the energy detection (ED) technique in a noise-uncertain environment. In an environment comprising noise uncertainty, the performance of conventional energy detection techniques is significantly degraded owing to the noise fluctuation caused by the noise temperature, interference, and filtering. To mitigate this problem, we present a cooperative spectrum sensing technique that comprises the use of the Kullback–Leibler divergence (KLD) in cognitive radio-based IoT (CR-IoT). In the proposed method, each unlicensed IoT device that is capable of spectrum sensing, which is called a CR-IoT user, makes a local decision using the KLD technique. The spectrum sensing performed with the KLD requires a smaller number of samples than other conventional approaches, e.g., energy detection, for reliable sensing even in a noise uncertain environment. After the local decision is made, each CR-IoT user sends its own local decision result to the corresponding fusion center, which makes a global decision using the soft fusion rule. The results obtained through simulations show that the proposed KLD scheme achieves a better sensing performance, i.e., higher detection and lower false-alarm probabilities, enhances the sum rate, and reduces the total time as compared to the conventional ED scheme under various fading channels.

Review of Spectrum Sensing Techniques in Cognitive Radio Networks

2019 ◽  
pp. 85-107
Author(s):  
Ala Eldin Omer
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Feature Detection ◽  
Matched Filter ◽  
Internal State ◽  
Energy Detection ◽  
Detection Sensitivity ◽  
Detection Techniques ◽  
Radio Technology ◽  
Cyclostationary Feature Detection

Most frequency spectrum bands are licensed to certain services to avoid the interference between various networks, but the spectrum occupancy measurements show that few portions of this spectrum are fully efficiently used. Cognitive radio is a future radio technology that is aware of its environment, internal state, and can change its operating behavior (transmitter parameters) accordingly. Through this technology the unlicensed users can use the underutilized spectrum without causing any harmful interference to the licensed users. Its key domains are sensing, cognition, and adaptation. The spectrum sensing problem is one of the most challenging issues in cognitive radio systems to detect the available frequency bands. This chapter introduces the concepts of various transmitter detection techniques, namely energy detection, matched filter detection, and cyclostationary feature detection. The chapter also discusses other sensing techniques that are introduced to enhance the detection performance of the conventional energy detector. Additionally, the introduced sensing techniques are implemented using extensive MATLAB simulations and their performances are evaluated and compared in terms of sensing time, detection sensitivity, and ease of implementation. The implementation is based on BPSK and QPSK modulation schemes under various SNR values for AWGN noisy channel with Rayleigh fading.

SIMULATION OF DIRECT SPREAD SPECTRUM COMMUNICATION SYSTEM ON THE HARDWARE MODULE EMONA DATEX. EVALUATION OF THE EFFECT OF NOISE ON THE QUALITY OF DEMODULATION

2019 ◽  
Author(s):  
Maksim Zhmakin ◽  
Irina Chadyuk ◽  
Aleksey Nadymov
Keyword(s):  
Spread Spectrum ◽  
Communication System ◽  
Spread Spectrum Communication ◽  
Direct Spread ◽  
Simulation Results

A variant of implementation of a communication system with direct spread spectrum is presented in this article, simulation results are also presented, the main parameters of the system are taken, and conclusions are drawn.

scholarly journals A Set of Single YOLO Modalities to Detect Occluded Entities via Viewpoint Conversion

2021 ◽  
Vol 11 (13) ◽  
pp. 6016
Author(s):  
Jinsoo Kim ◽  
Jeongho Cho
Keyword(s):  
Object Detection ◽  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Cloud Data ◽  
Detection Techniques ◽  
Bounding Boxes ◽  
Partially Occluded ◽  
Rgb Image

For autonomous vehicles, it is critical to be aware of the driving environment to avoid collisions and drive safely. The recent evolution of convolutional neural networks has contributed significantly to accelerating the development of object detection techniques that enable autonomous vehicles to handle rapid changes in various driving environments. However, collisions in an autonomous driving environment can still occur due to undetected obstacles and various perception problems, particularly occlusion. Thus, we propose a robust object detection algorithm for environments in which objects are truncated or occluded by employing RGB image and light detection and ranging (LiDAR) bird’s eye view (BEV) representations. This structure combines independent detection results obtained in parallel through “you only look once” networks using an RGB image and a height map converted from the BEV representations of LiDAR’s point cloud data (PCD). The region proposal of an object is determined via non-maximum suppression, which suppresses the bounding boxes of adjacent regions. A performance evaluation of the proposed scheme was performed using the KITTI vision benchmark suite dataset. The results demonstrate the detection accuracy in the case of integration of PCD BEV representations is superior to when only an RGB camera is used. In addition, robustness is improved by significantly enhancing detection accuracy even when the target objects are partially occluded when viewed from the front, which demonstrates that the proposed algorithm outperforms the conventional RGB-based model.

scholarly journals An Automatic Concrete Crack-Detection Method Fusing Point Clouds and Images Based on Improved Otsu’s Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1581
Author(s):  
Xiaolong Chen ◽  
Jian Li ◽  
Shuowen Huang ◽  
Hao Cui ◽  
Peirong Liu ◽  
...  
Keyword(s):  
Crack Detection ◽  
Detection Method ◽  
Multiple Scales ◽  
Point Clouds ◽  
Depth Image ◽  
Detection Accuracy ◽  
Detection Techniques ◽  
Concrete Crack ◽  
3D Point Clouds ◽  
2D Images

Cracks are one of the main distresses that occur on concrete surfaces. Traditional methods for detecting cracks based on two-dimensional (2D) images can be hampered by stains, shadows, and other artifacts, while various three-dimensional (3D) crack-detection techniques, using point clouds, are less affected in this regard but are limited by the measurement accuracy of the 3D laser scanner. In this study, we propose an automatic crack-detection method that fuses 3D point clouds and 2D images based on an improved Otsu algorithm, which consists of the following four major procedures. First, a high-precision registration of a depth image projected from 3D point clouds and 2D images is performed. Second, pixel-level image fusion is performed, which fuses the depth and gray information. Third, a rough crack image is obtained from the fusion image using the improved Otsu method. Finally, the connected domain labeling and morphological methods are used to finely extract the cracks. Experimentally, the proposed method was tested at multiple scales and with various types of concrete crack. The results demonstrate that the proposed method can achieve an average precision of 89.0%, recall of 84.8%, and F1 score of 86.7%, performing significantly better than the single image (average F1 score of 67.6%) and single point cloud (average F1 score of 76.0%) methods. Accordingly, the proposed method has high detection accuracy and universality, indicating its wide potential application as an automatic method for concrete-crack detection.

Sign in / Sign up

Export Citation Format

Share Document