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.

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 Efficient Utilization of Cooperative Spectrum Sensing (CSS) in Cognitive Radio Network (CRN) Using Non-Orthogonal Multiple Access (NOMA)

2021 ◽  
Author(s):  
BALACHANDER T ◽  
Mukesh Krishnan M B
Keyword(s):  
Cognitive Radio ◽  
Wireless Communication ◽  
Spectrum Sensing ◽  
Multiple Access ◽  
Cognitive Radio Network ◽  
Cooperative Spectrum Sensing ◽  
Primary User ◽  
The Novel ◽  
Radio Network ◽  
Secondary Users

Abstract In the recent past, efficient cooperative spectrum sensing and usage are playing a vital role in wireless communication because of the significant progress of mobile devices. There is a recent surge and interest on Non-Orthogonal Multiple Access (NOMA) focused on communication powered by wireless mode. In modern research, more attention has been focused on efficient and accurate Non-Orthogonal Multiple Access (NOMA). NOMA wireless communication is highly adapted with Cognitive Radio Network (CRN) for improving performance. In the existing cognitive radio network, the secondary users could be able to access the idle available spectrum while primary users are engaged. In the traditional CRN, the primary user’s frequency bands are sensed as free, the secondary users could be utilized those bands of frequency resources. In this research, the novel methodology is proposed for cooperative spectrum sensing in CRN for 5G wireless communication using NOMA. The higher cooperative spectrum efficiency can be detected in the presence of channel noise. Cooperative spectrum sensing is used to improve the efficient utilization of spectrum. The spectrum bands with license authority primary user are shared by Secondary Users (SU) by simultaneously transmitting information with Primary Users (PU). The cooperative spectrum sensing provides well under the circumstances that the different channel interference to the primary user can be guaranteed to be negligible than an assured thresholding value. The Noisy Channel State Information (CSI) like AWGN and Rayleigh fading channels are considered as wireless transmission mediums for transmitting a signal using Multiple-Input-Multiple-Output (MIMO) NOMA to increase the number of users. The proposed NOMA is fascinated with significant benefits in CRN is an essential wireless communication method for upcoming 5G technology. From experimental results it has been proved that the novel methodology performance is efficient and accurate than existing methodologies by showing graphical representations and tabulated parameters.

scholarly journals Performance of p-Norm Detector in Cognitive Radio Networks with Cooperative Spectrum Sensing in Presence of Malicious Users

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Nandita Lavanis ◽  
Devendra Jalihal
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Random Variable ◽  
Energy Detection ◽  
Primary User ◽  
Test Statistic ◽  
Secondary Users ◽  
Probability Of False Alarm ◽  
Standard Energy

A cognitive radio network (CRN) with a cooperative spectrum sensing scheme is considered. This CRN has a primary user and multiple secondary users, some of which are malicious secondary users (MSUs). Energy detection at each SU is performed using a p-norm detector with p≥2, where p=2 corresponds to the standard energy detector. The MSUs are capable of perpetrating spectrum sensing data falsification (SSDF) attacks. At the fusion center (FC), an algorithm is used to suppress these MSUs which could be either an adaptive weighing algorithm or one of the following: Tietjen-Moore (TM) test or Peirce’s criterion. This is followed by computation of a test statistic (TS) which is a random variable. In this paper, we assume TS to have either a Gamma or a Gaussian distribution and calculate the threshold accordingly. We provide closed-form expressions of probability of false alarm and probability of miss-detection under both assumptions. We show that Gaussian assumption of TS is more suited in presence of an SSDF attack when compared with the Gamma assumption. We also compare the detection performance for various values of p and show that p=3 along with the Gaussian assumption is the best amongst all the cases considered.

scholarly journals Relay Assisted Spectrum Sensing in Presence of IQ Imbalance in Multi-Channel System

2021 ◽  
Author(s):  
Amaresh Kumar Sahu ◽  
Arunanshu Mahapatro ◽  
Radheshyam Patra
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Dynamic Spectrum Access ◽  
Cooperative Spectrum Sensing ◽  
Low Cost ◽  
Energy Detection ◽  
Primary User ◽  
Detection Scheme ◽  
Robust Detection ◽  
Spectrum Holes

Abstract The increasing demand of wireless technology leads to a need for dynamic spectrum access that is positively accomplished by cognitive radio (CR) technology. For its acceptable efficiency it needs a robust detection scheme which detect the spectrum holes. This paper investigates a cooperative spectrum sensing method for a relay-based cognitive radio network. In general relay is a low cost device and is more prone to hardware impairment such as In-phase and Quadrature-phase Imbalance (IQI), which can considerably limit the capabilities of sensing spectrum holes. This work studies the energy detection (ED) based spectrum sensing in multi-channel receiver scenarios that are affected by IQI. The detection and false alarm probabilities considering Gaussian primary user signal models are derived. Our results are simulation based and verified with theoretical findings.

Joint Detection of Spatial-Temporal Spectrum Holes for Cognitive Radio Networks

2014 ◽  
Vol 556-562 ◽  
pp. 2802-2805
Author(s):  
Fu Lai Liu ◽  
Shou Ming Guo ◽  
Rui Yan Du
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Primary User ◽  
Spatial Spectrum ◽  
Radio Networks ◽  
Spectrum Utilization ◽  
Secondary Users ◽  
Secondary Network ◽  
Spectrum Holes

Spectrum sensing is a key technology to reliably detect spectrum holes in multi-dimensions for cognitive radio networks. In this paper, a joint spatial-temporal spectrum sensing scheme is proposed. First of all, the secondary users (SUs) located inside the primary exclusive region (PER) perform temporal sensing. When the primary user (PU) is present, the SUs located outside the PER perform spatial spectrum sensing. The proposed method can improve the spectrum utilization by exploiting both temporal and spatial spectrum holes. The achievable throughput for the secondary network of joint spatial-temporal sensing is higher than that of pure temporal sensing. Simulation results demonstrate the effectiveness of the proposed approach.

scholarly journals Deep Learning for Spectrum Sensing in Cognitive Radio

Symmetry ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 147
Author(s):  
Surendra Solanki ◽  
Vasudev Dehalwar ◽  
Jaytrilok Choudhary
Keyword(s):  
Deep Learning ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Structural Information ◽  
Primary User ◽  
Secondary Users ◽  
Spectrum Detection ◽  
Free Spectrum ◽  
Margin Of Error ◽  
Insight Into

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.

scholarly journals An SETM Algorithm for Combating SSDF Attack in Cognitive Radio Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
S. Tephillah ◽  
J. Martin Leo Manickam
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Trust Management ◽  
Cooperative Spectrum Sensing ◽  
Radio Networks ◽  
Second Phase ◽  
Common Channel ◽  
Secondary Users ◽  
Management Algorithm ◽  
Different Types

Security is a pending challenge in cooperative spectrum sensing (CSS) as it employs a common channel and a controller. Spectrum sensing data falsification (SSDF) attacks are challenging as different types of attackers use them. To address this issue, the sifting and evaluation trust management algorithm (SETM) is proposed. The necessity of computing the trust for all the secondary users (SUs) is eliminated based on the use of the first phase of the algorithm. The second phase is executed to differentiate the random attacker and the genuine SUs. This reduces the computation and overhead costs. Simulations and complexity analyses have been performed to prove the efficiency and appropriateness of the proposed algorithm for combating SSDF attacks.

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 Cooperative Spectrum Sensing Based on HML and Vector Quantization for Cognitive Radio Networks

2018 ◽  
Vol 7 (2.20) ◽  
pp. 335
Author(s):  
Shweta Alpna ◽  
Amrit Mukherjee ◽  
Amlan Datta
Keyword(s):  
Communication Network ◽  
Cognitive Radio ◽  
Real Time ◽  
Spectrum Sensing ◽  
Vector Quantization ◽  
Cooperative Spectrum Sensing ◽  
Radio Networks ◽  
Secondary Users ◽  
The Real ◽  
Novel Technique

The proposed work illustrates a novel technique for cooperative spectrum sensing in a cognitive radio (CR) network. The work includes an approach of identifying secondary users (SUs) based on Hierarchical Maximum Likelihood (HML) technique followed by Vector Quantization. Initially, the arrangement of the SUs are been observed using HML with respect to a spatial domain and then the active SUs among them are identified using VQ. The approach will not only save the energy, but the decision of the real-time and dynamic cooperative communication network becomes more accurate as we can predict the behavior of SUs movement and spectrum sensing by each individual SU at that particular  place. The results and simulations of the real-time experiment justifies with the proposed approach. 

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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