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 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 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.

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.

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.

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