A Framework for Secure Cooperative Spectrum Sensing based with Blockchain and Deep Learning model in Cognitive Radio

Dewangan, Neelam; Kumar, Arun; Patel, R. N.

doi:10.1109/iceconf57129.2023.10083887

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…This system ensures secure and benefit-distributed participation by encrypting transaction information through a public-private encryption scheme, thereby rendering the decryption process virtually impossible. [91] proposes a blockchain-based method for detecting malicious users in CRNs, which can reduce the accuracy of spectrum sensing, especially in cooperative spectrum sensing. The method uses cryptographic keys to distinguish between trustworthy users and MUs, thereby improving the performance of cognitive radios.…”

Section: ) How Blockchain Helpsmentioning

confidence: 99%

A Survey on Blockchain for Dynamic Spectrum Sharing

Perera,

Ranaweera,

Kusaladharma

et al. 2024

IEEE Open J. Commun. Soc.

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The rapid increase in mobile users, the IoT, and data-hungry applications have brought forth unprecedented demand on the spectrum, which is scarce; on top of that, the existing static spectrum allocation schemes have resulted in a heavily underutilized spectrum which can be mitigated with a Dynamic Spectrum Access (DSA) scheme with unlicensed users gaining access to the idle spectrum bands of licensed spectrum users opportunistically. Such a DSA and Dynamic Spectrum Management (DSM) scheme would significantly increase spectral efficiency while facilitating new services and applications beyond 5G (B5G) networks. Even with access to new spectrum bands like terahertz (THz) and Visible light communication and enabling technologies such as Software Defined Networks (SDN) and Cognitive Radio (CR), implementing a fully realized DSM requires rapid sensing, coordination, and management, and sharing of idle spectrum bands in a fair manner while preserving the security and privacy aspects, limiting interferences. With their decentralized, immutable nature, blockchains promise the execution of spectrum access and sharing in a fully transparent, fair manner while preserving privacy and security. Furthermore, blockchain-based Smart Contracts (SCs) allow automation of DSM, cryptocurrencies, and tokens to facilitate the trading of spectrum and related resources. In addition to that, blockchains act as an interface for integrating AI and Machine Learning (ML) techniques into DSM, which provides a certain level of intelligence to the underlying architecture. Although several attempts have been carried out to analyze the research gaps in DSM, a comprehensive analysis addressing the blockchains as the primary solution to address DSM has not been carried out. In this survey, we address the potential of a blockchainbased approach toward realizing a decentralized DSM while presenting future directives to improve the use of blockchains for DSM.

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Section: ) How Blockchain Helpsmentioning

confidence: 99%

A Survey on Blockchain for Dynamic Spectrum Sharing

Perera,

Ranaweera,

Kusaladharma

et al. 2024

IEEE Open J. Commun. Soc.

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show abstract

“…Cooperative spectrum sensing (CSS) has been suggested as a potential remedy to lessen these effects by increasing the detection rate by making use of spatial diversity via cooperation among multiple sensing nodes [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]. These include various techniques, such as distributed compressive SS for cooperative multihop CRN [24], wideband SS on real-time signal via sub-Nyquist sampling [25], CSS for energy-harvesting CRN [26], centralized CSS for CRN [27], semi-supervised deep-learning-network-based CSS [28], deep-reinforcementlearning-based decentralized CSS [29], and various machine-learning-based CSS [30][31][32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Optimized Statistical Beamforming for Cooperative Spectrum Sensing in Cognitive Radio Networks

Al‐Saggaf

Jafarian²,

Alrefaei

et al. 2023

Mathematics

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In cognitive radio (CR), cooperative spectrum sensing (CSS) employs a fusion of multiple decisions from various secondary user (SU) nodes at a central fusion center (FC) to detect spectral holes not utilized by the primary user (PU). The energy detector (ED) is a well-established technique of spectrum sensing (SS). However, a major challenge in designing an energy detector-based SS is the requirement of correct knowledge for the distribution of decision statistics. Usually, the Gaussian assumption is employed for the received statistics, which is not true in real practice, particularly with a limited number of samples. Another big challenge in the CSS task is choosing an optimal fusion strategy. To tackle these issues, we have proposed a beamforming-assisted ED with a heuristic-optimized CSS technique that utilizes a more accurate distribution of decision statistics by employing the characterization of the indefinite quadratic form (IQF). Two heuristic algorithms, genetic algorithm with multi-parent crossover (GA-MPC) and constriction factor particle swarm-based optimization (CF-PSO), are developed to design optimum beamforming and optimum fusion weights that can maximize the global probability of detection pd while constraining the global probability of false alarm pf to below a required level. The simulation results are presented to validate the theoretical findings and to asses the performance of the proposed algorithm.

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“…Since smart contracts are relatively new, their legal status is still unclear. To solve this problem, governments and regulatory bodies must work together to create a legal framework that supports the use of smart contracts [3].…”

Section: Introductionmentioning

confidence: 99%

Research of the application of blockchain and smart contract technologies in spectrum management and trading in cognitive radio networks

Reypnazarov,

Khujamatov,

Das

et al. 2023

E3S Web of Conf.

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The rapid growth of wireless communication technologies has led to an exponential increase in the demand for spectrum resources. Spectrum management, the process of allocation and control of limited radio frequency spectrum, is critical to the efficient and equitable distribution of this scarce resource. However, traditional methods of spectrum management face many problems, including inefficiency, lack of transparency and proneness to fraud. In recent years, blockchain technology has emerged as a potential solution to these problems. The emergence of smart contracts using blockchain technology has the potential to revolutionize spectrum trading and improve spectrum management practices. This paper explores the application of blockchain in spectrum management and discusses its potential to revolutionize the way spectrum resources are allocated and managed. Also, the concept of smart contracts and their application in spectrum trading are studied. It discusses the benefits of using smart contracts for transparent and efficient spectrum transactions, implementation challenges, and potential solutions. Through the use of smart contracts, a secure and decentralized market for spectrum resources can be created, facilitating dynamic and flexible allocation of spectrum.

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A Framework for Secure Cooperative Spectrum Sensing based with Blockchain and Deep Learning model in Cognitive Radio

Cited by 5 publications

References 14 publications

A Survey on Blockchain for Dynamic Spectrum Sharing

A Survey on Blockchain for Dynamic Spectrum Sharing

Optimized Statistical Beamforming for Cooperative Spectrum Sensing in Cognitive Radio Networks

Research of the application of blockchain and smart contract technologies in spectrum management and trading in cognitive radio networks

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