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.

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 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.

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.

Secondary Data Transmissions with Two-Way Relay in Cognitive Radio Networks

2013 ◽  
Vol 336-338 ◽  
pp. 1650-1655
Author(s):  
Yuan Gao ◽  
Chang Ping Zhu ◽  
Yi Bin Tamg
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Secondary Data ◽  
Primary User ◽  
Radio Networks ◽  
Secondary Users ◽  
Step Model ◽  
Spectrum Hole ◽  
User Protection

For improving the spectrum hole utilization in cognitive radio networks, two-way relaying is used in this paper to assist two secondary users in exchanging information. The 2-step and 3-step two-way relaying models are respectively discussed with imperfect spectrum sensing. Moreover, the closed-form expressions of outage probability for the two models are derived with a primary user protection constraint. Simulation shows that 3-step model outperforms 2-step in terms of the outage performance.

scholarly journals Fuzzy-based reliable and secure cooperative spectrum sensing for the smart grid

2021 ◽  
Vol 8 (2) ◽  
pp. 92-100
Author(s):  
Laila Nassef ◽  
Reemah Alhebshi ◽  
Keyword(s):  
Cognitive Radio ◽  
Smart Grid ◽  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Probability Of Detection ◽  
Fusion Center ◽  
Secondary Users ◽  
Sensing Data ◽  
Spectrum Detection ◽  
Fuzzy Fusion

Cognitive radio is a promising technology to solve the spectrum scarcity problem caused by inefficient utilization of radio spectrum bands. It allows secondary users to opportunistically access the underutilized spectrum bands assigned to licensed primary users. The local individual spectrum detection is inefficient, and cooperative spectrum sensing is employed to enhance spectrum detection accuracy. However, cooperative spectrum sensing opens up opportunities for new types of security attacks related to the cognitive cycle. One of these attacks is the spectrum sensing data falsification attack, where malicious secondary users send falsified sensing reports about spectrum availability to mislead the fusion center. This internal attack cannot be prevented using traditional cryptography mechanisms. To the best of our knowledge, none of the previous work has considered both unreliable communication environments and the spectrum sensing data falsification attack for cognitive radio based smart grid applications. This paper proposes a fuzzy inference system based on four conflicting descriptors. An attack model is formulated to determine the probability of detection for both honest and malicious secondary users. It considers four independent malicious secondary users’ attacking strategies of always yes, always no, random, and opposite attacks. The performance of the proposed fuzzy fusion system is simulated and compared with the conventional fusion rules of AND, OR, Majority, and the reliable fuzzy fusion that does not consider the secondary user’s sensing reputation. The results indicate that incorporating sensing reputation in the fusion center has enhanced the accuracy of spectrum detection and have prevented malicious secondary users from participating in the spectrum detection fusion

scholarly journals A Novel Swarm Intelligence Optimized Spectrum Sensing Approach For Cognitive Radio Network

2021 ◽  
Vol 12 (6) ◽  
pp. 136-143
Author(s):  
C.Jayasri Et.al
Keyword(s):  
Cognitive Radio ◽  
Greedy Algorithm ◽  
Spectrum Sensing ◽  
Spectrum Sharing ◽  
Heuristic Algorithms ◽  
Primary User ◽  
Primary Component ◽  
Tuning Parameter ◽  
Secondary Users ◽  
Heuristic Solution

Spectrum sensing technique have been employed for the detection of various spectrum holes in the transmission of data for the secondary users that do not interfere with the transmission of data of the primary user. The technique known as Cognitive Radio (CR) is the one that efficiently uses the entire spectrum. The primary component of the CR is Spectrum sensing. There are certainly other factors that are considered to be important such as capabilities of cognition and awareness of sensing as well. Identified are different heuristic algorithms that are developed for solving numeric problems in optimization. As the problem has been established as NP-hard, it is essential to bring a low computation complexity heuristic solution. A greedy algorithm is used for optimizing spectrum sharing. Particle Swarm Optimization (PSO) remains an efficient and popular algorithm due to its low need for a tuning parameter, high accuracy, low time for processing, fast convergence, and simplicity. In this work, the PSO has been proposed for spectrum sensing, and this has shown better performance than the Greedy Algorithm used for the CR network spectrum sensing.

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.

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 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.

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