Joint Spectrum Sensing and Resource Allocation Scheme in Cognitive Radio Networks with Spectrum Sensing Data Falsification Attack

Chen, Huifang; Zhou, Ming; Xie, Lei; Wang, Kuang; Li, Jie

doi:10.1109/tvt.2016.2520983

Cited by 63 publications

(32 citation statements)

References 19 publications

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“…Since the neighbors of SUs may suffer from shadow fading and malicious users may exist as shown in Fig. 5 [33] , the fusion of the perception results of all neighbors is often not the best result. In addition, the communication overhead caused by the interaction perception results is also a problem to be considered.…”

Section: Cooperative Spectrum Sensingmentioning

confidence: 99%

Intelligent cognitive spectrum collaboration: Convergence of spectrum sensing, spectrum access, and coding technology

Cai

2020

Intell. and Converged Netw.

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For a future scenario where everything is connected, cognitive technology can be used for spectrum sensing and access, and emerging coding technologies can be used to address the erasure of packets caused by dynamic spectrum access and realize cognitive spectrum collaboration among users in mass connection scenarios. Machine learning technologies are being increasingly used in the implementation of smart networks. In this paper, after an overview of several key technologies in the cognitive spectrum collaboration, a joint optimization algorithm of dynamic spectrum access and coding is proposed and implemented using reinforcement learning, and the effectiveness of the algorithm is verified by simulations, thus providing a feasible research direction for the realization of cognitive spectrum collaboration.

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Section: Cooperative Spectrum Sensingmentioning

confidence: 99%

Intelligent cognitive spectrum collaboration: Convergence of spectrum sensing, spectrum access, and coding technology

Cai

2020

Intell. and Converged Netw.

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“…In [19], a sharing-based resource-allocation algorithm is proposed to design optimal sensing time, bandwidth allocation and power allocation. Chen et al propose a weighted-proportionalfairness-based joint spectrum sensing and resource allocation scheme in cognitive radio networks in [20]. When different spectrum bands are available, spectrum refarming is also an innovative spectrum sharing technique which supports different generations of cellular networks to operate in the same radio spectrum.…”

Section: A Related Workmentioning

confidence: 99%

Distributed Resource Allocation for D2D-Assisted Small Cell Networks With Heterogeneous Spectrum

et al. 2019

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Device-to-device (D2D) communication with increased spectral efficiency and reduced communication delay has undoubtedly become a general trend in future wireless networks. However, when D2D communication is incorporated into small cell networks (SCNs) with large number of randomly overlapped small cells, the co-channel interference between small cell users (SUEs) and D2D users is an inevitable challenge, especially with the heterogeneous spectrum, i.e., licensed spectrum bands and unlicensed spectrum bands. In this paper, we study the downlink channel allocation in D2D-assisted small cell networks with heterogeneous spectrum bands. By taking the required data rate of users and the interference constraint of SUEs into account, we formulate a channel allocation problem integrating channel selection and channel sharing to maximize the network utility, which is the service satisfaction of all users. To derive the solution, we decompose the optimization problem into two games: a potential game and a coalition game. Then, a potential game-based scheme using an interference graph and a coalition scheme with D2D user transferring is proposed to solve these two games, respectively. Based on these schemes, a two-stage distributed channel allocation algorithm is proposed and can converge with low computational complexity. Moreover, the simulation results reveal that the proposed algorithm could achieve high system throughput and network utility.

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“…In [14]- [19], trust models have been applied to channel sensing and channel access in CRNs to detect malicious nodes in order to improve sensing outcomes with reduce missed detection and false alarm rates. In [20], a reputation scheme has been applied to a clusterhead to detect its malicious member nodes in order to provide higher security to the non-manipulability and the agreement property of clusterhead election results, and consequently it maintains the network performance with the increase of member nodes.…”

Section: B Related Workmentioning

confidence: 99%

A Reinforcement Learning-Based Trust Model for Cluster Size Adjustment Scheme in Distributed Cognitive Radio Networks

Ling

Yau

Qadir

et al. 2019

IEEE Trans. Cogn. Commun. Netw.

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Cognitive radio enables secondary users (SUs) to explore and exploit the underutilized licensed channels (or white spaces) owned by the primary users. To improve the network scalability, the SUs are organized into clusters. This article proposes a novel artificial intelligence based trust model approach that uses reinforcement learning (RL) to improve traditional budget-based cluster size adjustment schemes. The RL-based trust model enables the clusterhead to observe and learn about the behaviors of its SU member nodes, and revoke the membership of malicious SUs in order to ameliorate the effects of intelligent and collaborative attacks, while adjusting the cluster size dynamically according to the availability of white spaces. The malicious SUs launch attacks on clusterheads causing the cluster size to become inappropriately sized while learning to remain undetected. In any attack and defense scenario, both the attackers and the clusterhead adopt RL approaches. Simulation results have shown that the single-agent RL (SARL) attackers have caused the cluster size to reduce significantly; while the SARL clusterhead has slightly helped increase its cluster size, and this motivates a rule-based approach to efficiently counterattack. Multi-agent RL attacks have shown to be less effective in an operating environment that is dynamic.Index Terms-Cognitive radio ad hoc networks, clustering methods, trust management, artificial intelligence, security.

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Joint Spectrum Sensing and Resource Allocation Scheme in Cognitive Radio Networks with Spectrum Sensing Data Falsification Attack

Cited by 63 publications

References 19 publications

Intelligent cognitive spectrum collaboration: Convergence of spectrum sensing, spectrum access, and coding technology

Intelligent cognitive spectrum collaboration: Convergence of spectrum sensing, spectrum access, and coding technology

Distributed Resource Allocation for D2D-Assisted Small Cell Networks With Heterogeneous Spectrum

A Reinforcement Learning-Based Trust Model for Cluster Size Adjustment Scheme in Distributed Cognitive Radio Networks

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