scholarly journals An Optimized and Trained Model of Cooperative Sensing for Cognitive Radio Networks

2019 ◽  
Vol 8 (3) ◽  
pp. 5176-5182
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Search Algorithm ◽  
Decision Threshold ◽  
Secondary Users ◽  
Communication Problems ◽  
Local Decision ◽  
Huge Impact ◽  
Radio Technology

Sensing based spectrum allocation is one of the solutions to bridge the gap between spectrum scarcity and underutilization of allocated spectrum. In this context, cognitive radio technology has become the prominent solution for future wireless communication problems. To accurately detect the spectrum availability, CRN uses cooperative spectrum sensing where N number of selected nodes will be involved in making a decision on spectrum occupation. Various sensing parameters such as sensing duration (τ), decision threshold (λ), number of nodes (N) and decision rule (K) have huge impact on the performance of cooperative spectrum sensing. In addition, there are constraints on energy consumption and protection of licensed user’s needs to be considered. Our work focuses on optimization of sensing parameters to maximize the throughput of the cognitive radio network maintaining the energy efficiency and protecting the licensed users from the interference caused by the secondary users. The proposed work uses convex optimization to optimize sensing duration and two-dimensional search algorithm to find the values N and K. Further optimization is done by comparing local decision with cooperative decision.

Efficient Cooperative Spectrum Sensing with Minimum Sensing Error Probability in Heterogeneous Cognitive Radio Networks

Frequenz ◽  
2012 ◽  
Vol 66 (7-8) ◽  
Author(s):  
Hang Hu ◽  
Ning Li ◽  
Youyun Xu
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Error Probability ◽  
Cooperative Spectrum Sensing ◽  
Radio Networks ◽  
Decision Threshold ◽  
Cognitive Network ◽  
Secondary Users ◽  
Sensing Performance

AbstractTo improve the sensing performance, cooperation among secondary users can be utilized to collect space diversity. We focus on the optimization of cooperative spectrum sensing in which multiple secondary users efficiently cooperate to achieve superior detection accuracy with minimum sensing error probability in heterogeneous cognitive radio (CR) networks. Rayleigh fading and Nakagami fading are considered respectively in cognitive network I and cognitive network II. For each cognitive network, we derive the optimal randomized rule for different decision threshold. Then, the optimal decision threshold is derived according to the rule of minimum sensing error (MSE). MSE rule shows better performance on improving the final false alarm and detection probability simultaneously. By simulations, our proposed strategy optimizes the sensing performance for each secondary user which is randomly distributed in the heterogeneous cognitive radio networks.

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 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. 

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 Cooperative Spectrum Sensing with Selection Diversity Reception in Cognitive Radio

2021 ◽  
pp. 1-7
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Recognition Performance ◽  
Fusion Rule ◽  
Diversity Reception ◽  
Selection Diversity ◽  
Simulation Evaluation ◽  
Radio Technology ◽  
Cognitive Radio Technology

Spectrum sensing is the key component of cognitive radio technology. But, detection is compromised when a user reports shadowing or fading consequences. In such instances, the customer cannot apprehend between an unexploited band and a profound fade. Hence, communal spectrum sensing is suggested to optimize sensing overall performance. We recognition performance of communal Spectrum Sensing with Selection Diversity Reception in Cognitive Radio. This study presents a simulation evaluation of choice diversity Reception based totally on the fusion rule. The fusion rule is finished at the fusion center (FC) to make the very last selection about the presence of PU. This leads that spectrum sensing is enthusiastically in the presence of Rayleigh.

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 Development and Demonstration of Graphical User Interface Spectrum Sensing Algorithm using some Wireless Systems in South Africa

2016 ◽  
Vol 2 (2) ◽  
Author(s):  
Jide Julius Popoola ◽  
Rex van Olst
Keyword(s):  
Cognitive Radio ◽  
User Interface ◽  
Spectrum Sensing ◽  
Graphical User Interface ◽  
Dynamic Spectrum Access ◽  
Cooperative Spectrum Sensing ◽  
Radio Spectrum ◽  
Spectrum Utilization ◽  
Application Program ◽  
Radio Technology

The wireless communication industry using radio spectrum is recently going through major innovations and advancements. With this transformation, the demand for and usage of radio spectrum has increased exponentially making radio spectrum indeed a scarce natural resource. In order to solve this problem, the possibility of opening up the unused portions of licensed spectrum by sharing using cognitive radio technology has been in the spotlight for maximizing radio spectrum utilization as well to as ensure sufficient radio spectrum availability for future wireless services and applications. With this objective in mind, this paper looks at the principles and technologies of cooperative spectrum sensing in cognitive radio environment in improving radio spectrum utilization. The paper provides a comprehensive review on spectrum sensing as a key functional requirement for cognitive radio technology by focusing on its application on dynamic spectrum access that enables unused portions of licensed spectrum to be used in an opportunistic manner as long as the operation of the unlicensed user will not affect that of the licensed user. In satisfying this dynamic spectrum access requirement, a friendly interactive graphical user interface (GUI) spectrum sensing application program was developed. The detail activities involve in the development of the application program, also known as spectrum sensing and detection algorithm (SSADA), was fully documented and presented in the paper. The developed graphical user interface application program after successfully developed was evaluated. The performance evaluations of developed graphical user interface sensing algorithm show that the algorithm performs favourably well. The program overall evaluation results provide bedrock information on how to improve cooperative spectrum sensing gain without incurring a cooperative overhead.

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 Machine Learning-Based Cooperative Spectrum Sensing in Dynamic Segmentation Enabled Cognitive Radio Vehicular Network

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1169
Author(s):  
Mohammad Asif Hossain ◽  
Rafidah Md Noor ◽  
Kok-Lim Alvin Yau ◽  
Saaidal Razalli Azzuhri ◽  
Muhammad Reza Z’aba ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Ad Hoc ◽  
Cooperative Spectrum Sensing ◽  
Learning Algorithm ◽  
Congestion Management ◽  
Secondary Users ◽  
Dynamic Segmentation ◽  
Services Integration

A vehicle ad hoc network (VANET) is a solution for road safety, congestion management, and infotainment services. Integration of cognitive radio (CR), known as CR-VANET, is needed to solve the spectrum scarcity problems of VANET. Several research efforts have addressed the concerns of CR-VANET. However, more reliable, robust, and faster spectrum sensing is still a challenge. A novel segment-based CR-VANET (Seg-CR-VANET) architecture is therefore proposed in this paper. Roads are divided equally into segments, and they are sub-segmented based on the probability value. Individual vehicles or secondary users produce local sensing results by choosing an optimal spectrum sensing (SS) technique using a hybrid machine learning algorithm that includes fuzzy and naïve Bayes algorithms. We used dynamic threshold values for the sensing techniques. In this proposed cooperative SS, the segment spectrum agent (SSA) made the global decision using the tri-agent reinforcement learning (TA-RL) algorithm. Three environments (network, signal, and vehicle) are learned by this proposed algorithm to determine primary (licensed) users’ activities. The simulation results indicate that, compared to current works, the proposed Seg-CR-VANET produces better results in spectrum sensing.

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