Distributed cooperative spectrum sensing based on reinforcement learning in cognitive radio networks

Zhang, Mengbo; Wang, Lunwen; Feng, Yanqing

doi:10.1016/j.aeue.2018.07.029

Cited by 26 publications

(11 citation statements)

References 23 publications

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“…The sensing policy employs 𝜀-greedy method for selecting frequency bands to be sensed and for the selection of sensing assignments as well as this sensing policy guides the SU to concentrate the search of vacant spectrum to the frequencies that provide high data rates. Zhang et al 71 proposed a distributed CSS model based on RL to solve data fusion data between users having different reputations. The main contribution of this work is to reduce the interference of malicious users by selecting honest users and improves the performance of the whole CRN to make it more intelligent and stable.…”

Section: F I G U R E 6 Reinforcement Learning Framework For Cssmentioning

confidence: 99%

Machine learning for cooperative spectrum sensing and sharing: A survey

Janu

Singh

Kumar

2021

Trans Emerging Tel Tech

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With the rapid development of next-generation wireless communication technologies and the increasing demand of spectrum resources, it becomes necessary to introduce learning and reasoning capabilities in cognitive radio networks (CRN). In particular, our focus is on two fundamental applications in CRNs, namely spectrum sensing (SS) and spectrum sharing. The application of machine learning (ML) techniques has added new aspects to SS and spectrum sharing. This paper offers a survey on various ML-based algorithms in the cooperative spectrum sensing (CSS) and dynamic spectrum sharing (DSS) domain, with its emphasis on types of features extracted from primary user signal, types of ML algorithm, and performance metrics utilized for evaluation of ML algorithms. Starting with the basic principles and challenges of SS, this paper also justifies the applicability of supervised, unsupervised, and reinforcement ML algorithms in the CSS domain. The application of ML algorithms, to solve the DSS problem has also been reviewed. Finally, the survey paper is concluded with some suggested open research challenges and future directions for ML application in next-generation communication technologies.

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Section: F I G U R E 6 Reinforcement Learning Framework For Cssmentioning

confidence: 99%

Machine learning for cooperative spectrum sensing and sharing: A survey

Janu

Singh

Kumar

2021

Trans Emerging Tel Tech

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“…Decision process depends on the secondary user, which is a major limitation of this approach. Trust based cooperative spectrum sensing [7] and reinforcement learning based sensing models [8,9] are introduced for autonomous decision making process and reduces the false reports due to attacks. This improves the success rate in spectrum sensing process and makes the system stable.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Optimized Secure Cooperative Spectrum Sensing for Cognitive Radio Networks

Rangaraj¹,

Jothiraj²,

Sridevi³

2021

Wireless Pers Commun

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Spectrum utilization is an important factor in Cognitive radio networks, which is accomplished by analyzing the unused spectrum bands of primary users (PU). The secondary users are allowed to access the resources, when the spectral bands are vacant by sensing the spectrum status and thus it reduces the spectrum scarcity among the users. Researchers have paid more attention towards spectrum sensing along with its security factors in cognitive radio networks. In this process, cooperative spectrum sensing is widely adopted in cognitive radio networks due to its robustness. However, the security concerns in cooperative spectrum sensing against attacks must be addressed. The performance of cooperative spectrum sensing will get affected if the fusion center gets wrong information from malicious user. This leads to wrong decision in the fusion center and results into false observations and affects the decision process. In order to overcome these challenges, this research work proposes a hybrid nature inspired and optimized cooperative spectrum sensing against attacks in cognitive radio networks. The proposed model allows the fusion center to remove the uncharacteristic data in the fusion process, which results from the malicious users. The performance analysis of spectrum sensing process under different attacks are analyzed through simulation and later it is compared against conventional methods such as genetic algorithm, particle swarm optimization and differential evolution schemes to validate the improved performance.

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“…This intrusion may intrude the final outcome also the author proposes a reinforcement learning model to substantiate the working principles of CR networks to analyse the false sensing data. This method proposes a detailed analysis on the adjacent nodes estimation for the agent to merge the high reputation nodes [28]. The former models proposes various game applications but the Proposed solution takes the Q Learning mechanism and Coalition game Modelling to support the security at a greater level with the coalition game modelling…”

Section: Information Technology and Controlmentioning

confidence: 99%

EECCRN: Energy Enhancement with CSS Approach Using Q-Learning and Coalition Game Modelling in CRN

Vimal¹,

Kalaivani²,

Kadry

et al. 2021

ITC

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The Cognitive radio network (CR) is a widespread technology in which the Secondary users are assumed to be of the winning users to acquire the spectrum by reducing the false alarm possibilities and the false detection of the user assumed to be original user in nature is restricted with the usage of Spectrum monitoring agents. The collaborative spectrum sensing (CSS) is an approach that will identify the false intruder in the CR networks, here it is proposed with the Enhanced Q-Learning model with Coalition Game approach (EQLCG) to outline the energy enhancement. Besides an approach on Greedy Bidding is used to allocate the spectrum to the winning secondary user (SU) based on the idle primary user to strengthen the spectrum sensing. The winning secondary user forms a communication establishment with the neighbouring SU to eradicate the miss detection probability based on group level cooperation. The simulation experiment analyses the cluster level security with energy monitoring that has been performed using the analysis of interference by applying the coalition game theory modelling and the information obscured by the attacker is reduced with the usage of enhanced Q-learning, and the results prove that overhead is substantially monitored. The proposed paper enhances the security in physical layer with energy conservation and maintains the spectrum usage for application purpose. The proposed simulation approach reduces the miss detection and false alarm probabilistic approach while compared with Stackelberg and Bayesian game models.

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Distributed cooperative spectrum sensing based on reinforcement learning in cognitive radio networks

Cited by 26 publications

References 23 publications

Machine learning for cooperative spectrum sensing and sharing: A survey

Machine learning for cooperative spectrum sensing and sharing: A survey

Hybrid Optimized Secure Cooperative Spectrum Sensing for Cognitive Radio Networks

EECCRN: Energy Enhancement with CSS Approach Using Q-Learning and Coalition Game Modelling in CRN

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