scholarly journals Quantized Cooperative Spectrum Sensing in Bandwidth-Constrained Cognitive V2X Based on Deep Learning

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1315
Author(s):  
Jingxian Li ◽  
Bin-Jie Hu
Keyword(s):  
Deep Learning ◽  
Spectrum Sensing ◽  
Communication Channel ◽  
Cooperative Spectrum Sensing ◽  
Detection Performance ◽  
Optimal Detection ◽  
Score Vector ◽  
Single User ◽  
The Impact ◽  
Better Than

The output of the network in a deep learning (DL) based single-user signal detector, which is a normalized 2 × 1 class score vector, needs to be transmitted to the fusion center (FC) by occupying a large amount of the communication channel (CCH) bandwidth in the cooperative spectrum sensing (CSS). Obviously, in cognitive radio for vehicle to everything (CR-V2X), it is particularly important to propose a method that makes full use of the bandwidth-constrained CCH to obtain the optimal detection performance. In this paper, we firstly propose a novel single-user spectrum sensing method based on modified-ResNeXt in CR-V2X. The simulation results show that our proposed method performs better than two advanced DL based spectrum sensing methods with shorter inference time. We then introduce a quantization-based cooperative spectrum sensing (QBCSS) algorithm based on DL in CR-V2X, and the impact of the number of reported bits on the sensing results is also discussed. Through the experimental results, we conclude that the QBCSS algorithm reaches the optimal detection performance when the number of bits for quantizing local sensing data is 4. Finally, according to the conclusion, a bandwidth-constrained QBCSS scheme based on DL is proposed to make full use of the CCH with limited capacity to achieve the optimal detection performance.

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 SSDF-Attack-Exclusion Cooperative Spectrum Sensing Scheme in Cognitive Radio Networks

2014 ◽  
Vol 556-562 ◽  
pp. 5219-5222
Author(s):  
Wei Wu ◽  
Xiao Fei Zhang ◽  
Xiao Ming Chen
Keyword(s):  
Cognitive Radio ◽  
Utility Function ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Cooperative Spectrum Sensing ◽  
Radio Networks ◽  
Fusion Center ◽  
Sensing Data ◽  
Single User ◽  
Simulation Results

Compared with the single user spectrum sensing, cooperative spectrum sensing is a promising way to improve the detection precision. However, cooperative spectrum sensing is vulnerable to a variety of attacks, such as the spectrum sensing data falsification attack (SSDF attack). In this paper, we propose a concise cooperative spectrum sensing scheme based on a reliability threshold. We analyze the utility function of SSDF attacker in this scheme, and present the least reliability threshold for the fusion center against SSDF attack. Simulation results show that compared with the traditional cooperative spectrum sensing scheme, the SSDF attacker has a much lower utility in our proposed scheme, which drives it not to attack any more.

Detection Reliability and Throughput Analysis for Cooperative Spectrum Sensing in Cognitive Radio Networks

2012 ◽  
Vol 457-458 ◽  
pp. 668-674
Author(s):  
Hong Du ◽  
Zai Xue Wei ◽  
Yu Wang ◽  
Da Cheng Yang
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Cooperative Spectrum Sensing ◽  
High Reliability ◽  
Radio Networks ◽  
Shadow Fading ◽  
Sensing Technology ◽  
The Impact ◽  
Cooperative Algorithm

In cognitive radio networks (CRNs), cooperative spectrum sensing technology could overcome the impact from shadow fading and noise uncertainty; however, cognitive radio users with different signal-to-noise ratios (SNRs) would cause the unreliable detection performance when making a decision in the information fusion center. Therefore, a novel cooperative spectrum sensing scheme which focus on the reliability of cognitive radio users is presented. The proposed approach does not select all of the cognitive radio users but the ones whose SNR is beyond the average SNR of the whole users for high reliability. Moreover, the detection and throughput performance is investigated. Simulation results illustrate this approach could enhances the detection probability by comparing to the conventional cooperative algorithm. Besides, it also could lead to higher throughput within a short spectrum sensing time.

scholarly journals CSS using Energy Detector in AWGN and Flat-Fading Channels in Cognitive Radio Networks: A Complete Analysis

2020 ◽  
Vol 8 (6) ◽  
pp. 5042-5046
Keyword(s):  
Cognitive Radio ◽  
Fading Channels ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Cooperative Spectrum Sensing ◽  
Radio Networks ◽  
Complete Analysis ◽  
Flat Fading ◽  
Single User ◽  
Simulation Results

In this work, various spectrum sensing methods and algorithms are analyzed and their performance is been evaluated based on the different values of probabilities as obtained through MATLAB simulations. The work is been started from the analysis of the simplest single user sensing to advanced cooperative spectrum sensing and is further extended to CSS in AWGN noise and flat-fading channels. The results indicates that advanced cooperative spectrum sensing gives much better sensing decisions as compared to the results obtained by simulating single user sensing method. Simulation results obtained shows that Pd increases with Pf and also shows good values for SNR more than 0 dB. Also the Pd increases from 0.7 to 0.84 as we go from single user detection to CSS.

scholarly journals A Coalition Formation Game for Cooperative Spectrum Sensing in Cognitive Radio Network under the Constraint of Overhead

2021 ◽  
Vol 13 (3) ◽  
Author(s):  
Utpala Borgohain ◽  
Surajit Borkotokey ◽  
S.K Deka
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Coalition Formation ◽  
Cooperative Spectrum Sensing ◽  
Spatial Diversity ◽  
Detection Performance ◽  
Secondary Users ◽  
Selection Scheme ◽  
Coalition Formation Game ◽  
Formation Game

Cooperative spectrum sensing improves the sensing performance of secondary users by exploiting spatial diversity in cognitive radio networks. However, the cooperation of secondary users introduces some overhead also that may degrade the overall performance of cooperative spectrum sensing.  The trade-off between cooperation gain and overhead plays a vital role in modeling cooperative spectrum sensing.  This paper considers overhead in terms of reporting energy and reporting time. We propose a cooperative spectrum sensing based coalitional game model where the utility of the game is formulated as a function of throughput gain and overhead. To achieve a rational average throughput of secondary users, the overhead incurred is to be optimized. This work emphasizes on optimization of the overhead incurred. In cooperative spectrum sensing, the large number of cooperating users improve the detection performance, on the contrary, it increases overhead too. So, to limit the maximum coalition size we propose a formulation under the constraint of the probability of false alarm. An efficient fusion center selection scheme and an algorithm to select eligible secondary users for reporting are proposed to reduce the reporting overhead. We also outline a distributed cooperative spectrum sensing algorithm using the properties of the coalition formation game and prove that the utility of the proposed game has non-transferable properties.  The simulation results show that the proposed schemes reduce the overhead of reporting without compromising the overall detection performance of cooperative spectrum sensing.

scholarly journals A Novel Semi-Soft Decision Scheme for Cooperative Spectrum Sensing in Cognitive Radio Networks

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2522 ◽  
Author(s):  
Yin Mi ◽  
Guangyue Lu ◽  
Yuxin Li ◽  
Zhiqiang Bao
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Transmission Band ◽  
Primary User ◽  
Detection Performance ◽  
Final Decision ◽  
Soft Decision ◽  
Hidden Terminal ◽  
Sensing Performance

Spectrum sensing (SS) is an essential part of cognitive radio (CR) technology, and cooperative spectrum sensing (CSS) could efficiently improve the detection performance in environments with fading and shadowing effects, solving hidden terminal problems. Hard and Soft decision detection are usually employed at the fusion center (FC) to detect the presence or absence of the primary user (PU). However, soft decision detection achieves better sensing performance than hard decision detection at the expense of the local transmission band. In this paper, we propose a tradeoff scheme between the sensing performance and band cost. The sensing strategy is designed based on three modules. Firstly, a local detection module is used to detect the PU signal by energy detection (ED) and send decision results in terms of 1-bit or 2-bit information. Secondly, and most importantly, the FC estimates the received decision data through a data reconstruction module based on the statistical distribution such that the extra thresholds are not needed. Finally, a global decision module is in charge of fusing the estimated data and making a final decision. The results from a simulation show that the detection performance of the proposed scheme outperforms that of other algorithms. Moreover, savings on the transmission band cost can be made compared with soft decision detection.

Metal artefact reduction in the oral cavity using deep learning reconstruction algorithm in ultra-high-resolution computed tomography: a phantom study

2021 ◽  
pp. 20200553
Author(s):  
Yuki Sakai ◽  
Erina Kitamoto ◽  
Kazutoshi Okamura ◽  
Masato Tatsumi ◽  
Takashi Shirasaka ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Oral Cavity ◽  
Image Noise ◽  
Ct Number ◽  
Metal Artefact Reduction ◽  
Metal Artefact ◽  
Artefact Reduction ◽  
The Impact ◽  
Better Than

Objectives: This study aimed to improve the impact of the metal artefact reduction (MAR) algorithm for the oral cavity by assessing the effect of acquisition and reconstruction parameters on an ultra-high-resolution CT (UHRCT) scanner. Methods: The mandible tooth phantom with and without the lesion was scanned using super-high-resolution, high-resolution (HR), and normal-resolution (NR) modes. Images were reconstructed with deep learning-based reconstruction (DLR) and hybrid iterative reconstruction (HIR) using the MAR algorithm. Two dental radiologists independently graded the degree of metal artefact (1, very severe; 5, minimum) and lesion shape reproducibility (1, slight; 5, almost perfect). The signal-to-artefact ratio (SAR), accuracy of the CT number of the lesion, and image noise were calculated quantitatively. The Tukey-Kramer method with a p-value of less than 0.05 was used to determine statistical significance. Results: The HRDLR visual score was better than the NRHIR score in terms of degree of metal artefact (4.6 ± 0.5 and 2.6 ± 0.5, p < 0.0001) and lesion shape reproducibility (4.5 ± 0.5 and 2.9 ± 1.1, p = 0.0005). The SAR of HRDLR was significantly better than that of NRHIR (4.9 ± 0.4 and 2.1 ± 0.2, p < 0.0001), and the absolute percentage error of the CT number in HRDLR was lower than that in NRHIR (0.8% in HRDLR and 23.8% in NRIR). The image noise of HRDLR was lower than that of NRHIR (15.7 ± 1.4 and 51.6 ± 15.3, p < 0.0001). Conclusions: Our study demonstrated that the combination of HR mode and DLR in UHRCT scanner improved the impact of the MAR algorithm in the oral cavity.

Improving multi-class Boosting-based object detection

2020 ◽  
Vol 28 (1) ◽  
pp. 81-96
Author(s):  
José Miguel Buenaposada ◽  
Luis Baumela
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Data Augmentation ◽  
Detection Performance ◽  
Significant Progress ◽  
Training Techniques ◽  
Multi Scale ◽  
Bounding Box ◽  
Open Issue ◽  
The Impact

In recent years we have witnessed significant progress in the performance of object detection in images. This advance stems from the use of rich discriminative features produced by deep models and the adoption of new training techniques. Although these techniques have been extensively used in the mainstream deep learning-based models, it is still an open issue to analyze their impact in alternative, and computationally more efficient, ensemble-based approaches. In this paper we evaluate the impact of the adoption of data augmentation, bounding box refinement and multi-scale processing in the context of multi-class Boosting-based object detection. In our experiments we show that use of these training advancements significantly improves the object detection performance.

scholarly journals An Input-aware Factorization Machine for Sparse Prediction

2019 ◽  
Author(s):  
Yantao Yu ◽  
Zhen Wang ◽  
Bo Yuan
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real World ◽  
State Of The Art ◽  
Overall Performance ◽  
Factorization Machine ◽  
The Impact ◽  
Novel Model ◽  
Individual Input ◽  
Better Than

Factorization machines (FMs) are a class of general predictors working effectively with sparse data, which represents features using factorized parameters and weights. However, the accuracy of FMs can be adversely affected by the fixed representation trained for each feature, as the same feature is usually not equally predictive and useful in different instances. In fact, the inaccurate representation of features may even introduce noise and degrade the overall performance. In this work, we improve FMs by explicitly considering the impact of individual input upon the representation of features. We propose a novel model named \textit{Input-aware Factorization Machine} (IFM), which learns a unique input-aware factor for the same feature in different instances via a neural network. Comprehensive experiments on three real-world recommendation datasets are used to demonstrate the effectiveness and mechanism of IFM. Empirical results indicate that IFM is significantly better than the standard FM model and consistently outperforms four state-of-the-art deep learning based methods.

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