Enhanced Sensing and Sum-Rate Analysis in a Cognitive Radio-Based Internet of Things

Miah, Md Sipon; Ahmed, K.M.; Islam, Khairul; Mahmud, Md. Ashek Raihan; Rahman, Md. Mahbubur; Yu, Heejung

doi:10.3390/s20092525

Cited by 11 publications

(8 citation statements)

References 43 publications

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“…To study efficiency of spectrum usage problem, CR idea was given. This CR technology has potential of being a troublesome force within spectrum management [83,84]. Famous example is opportunistic spectrum or opportunistic radio those rules are spatial, temporal, and geographic reuse of legal spectrum.…”

Section: Key Challenges and Future Discussion On 6g Cr Network Communicationmentioning

confidence: 99%

Sixth Generation (6G) Cognitive Radio Network (CRN) Application, Requirements, Security Issues, and Key Challenges

Aslam

Zhang

et al. 2021

Wireless Communications and Mobile Computing

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Recently, 5G installation has been started globally. Different capabilities are in the consistent procedure, like ultrareliability, mass connectivity, and specific low latency. Though, 5G is insufficient to meet all the necessities of the future technology in 2030 and so on. Next generation information and communication technology is playing an important role in attraction of researchers, industries, and technical people. With respect to 5G networks, sixth-generation (6G) CR networks are anticipated to familiarize innovative use cases and performance metrics, such as to offer worldwide coverage, cost efficiency, enhanced spectral, energy improved intelligence, and safety. To reach such requirements, upcoming 6G CRNs will trust novel empowering technologies. Innovative network architecture and transmission technologies and air interface are of excessive position, like multiple accesses, waveform design, multiantenna technologies, and channel coding schemes. (1) To content, the condition should be of worldwide coverage, there will be no limit on 6G to global CR communication networks that may require to be completed with broadcast networks, like satellite communication networks, therefore, attaining a sea integrated communication network. (2) The spectrums overall will be entirely travelled to the supplementary rise connection density data rates in optical frequency bands, millimeter wave (mmWave), sub-6 GHz, and terahertz (THz). (3) To see big datasets created because of tremendously varied CR communication networks, antenna rush, diverse communication scenarios, new provision necessities, wide bandwidth, and 6G CRNs will allow an innovative variety of intelligent applications with the assistance of big data and AI technologies. (4) Need to improve network security when deploying 6G technology in CR networks. 6G is decentralized, intended, intelligent innovative, and distributed network. In this article, we studied a survey of current developments and upcoming trends. We studied the predicted applications, possible technologies, and security issues for 6G CR network communication. We also discussed predicted future key challenges in 6G.

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Section: Key Challenges and Future Discussion On 6g Cr Network Communicationmentioning

confidence: 99%

Sixth Generation (6G) Cognitive Radio Network (CRN) Application, Requirements, Security Issues, and Key Challenges

Aslam

Zhang

et al. 2021

Wireless Communications and Mobile Computing

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“…When N x,j is large, the probability distribution function (PDF) of y E j can be approximated with mean µ 0j , µ 1j and variance σ 2 0j , σ 2 1j as a Gaussian random variable (Gaussian distribution) using central limit theorem (CLT) under both hypotheses H 0 and H 1 , respectively as follows [56,57]: where γ j is the SNR of the primary signal at the jth CR-IoT user.…”

Section: Fig 2 the Proposed System Modelmentioning

confidence: 99%

A throughput analysis of an energy-efficient spectrum sensing scheme for the cognitive radio-based Internet of things

Miah

Schukat

Barrett

2021

J Wireless Com Network

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Spectrum sensing in a cognitive radio network involves detecting when a primary user vacates their licensed spectrum, to enable secondary users to broadcast on the same band. Accurately sensing the absence of the primary user ensures maximum utilization of the licensed spectrum and is fundamental to building effective cognitive radio networks. In this paper, we address the issues of enhancing sensing gain, average throughput, energy consumption, and network lifetime in a cognitive radio-based Internet of things (CR-IoT) network using the non-sequential approach. As a solution, we propose a Dempster–Shafer theory-based throughput analysis of an energy-efficient spectrum sensing scheme for a heterogeneous CR-IoT network using the sequential approach, which utilizes firstly the signal-to-noise ratio (SNR) to evaluate the degree of reliability and secondly the time slot of reporting to merge as a flexible time slot of sensing to more efficiently assess spectrum sensing. Before a global decision is made on the basis of both the soft decision fusion rule like the Dempster–Shafer theory and hard decision fusion rule like the “n-out-of-k” rule at the fusion center, a flexible time slot of sensing is added to adjust its measuring result. Using the proposed Dempster–Shafer theory, evidence is aggregated during the time slot of reporting and then a global decision is made at the fusion center. In addition, the throughput of the proposed scheme using the sequential approach is analyzed based on both the soft decision fusion rule and hard decision fusion rule. Simulation results indicate that the new approach improves primary user sensing accuracy by $$13\%$$ 13 % over previous approaches, while concurrently increasing detection probability and decreasing false alarm probability. It also improves overall throughput, reduces energy consumption, prolongs expected lifetime, and reduces global error probability compared to the previous approaches under any condition [part of this paper was presented at the EuCAP2018 conference (Md. Sipon Miah et al. 2018)].

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“…Many techniques are employed to reuse or share the spectrum among multiple users without degrading the system performance. Miah et al [ 7 ] proposed an enhanced spectrum sharing technique by utilizing kullback leibler divergence (KLD). Their approach shows a reasonable sensing performance even with the limited sample for sensing and noise uncertainty.…”

Section: A Brief Review Of Articles In Special Issuementioning

confidence: 99%

Cognitive Radio Networks for Internet of Things and Wireless Sensor Networks

Zikria

2020

Sensors

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Recent innovation, growth, and deployment of internet of things (IoT) networks are changing the daily life of people. 5G networks are widely deployed around the world, and they are important for continuous growth of IoT. The next generation cellular networks and wireless sensor networks (WSN) make the road to the target of the next generation IoT networks. The challenges of the next generation IoT networks remain in reducing the overall network latency and increasing throughput without sacrificing reliability. One feasible alternative is coexistence of networks operating on different frequencies. However, data bandwidth support and spectrum availability are the major challenges. Therefore, cognitive radio networks (CRN) are the best available technology to cater to all these challenges for the co-existence of IoT, WSN, 5G, and beyond-5G networks.

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Enhanced Sensing and Sum-Rate Analysis in a Cognitive Radio-Based Internet of Things

Cited by 11 publications

References 43 publications

Sixth Generation (6G) Cognitive Radio Network (CRN) Application, Requirements, Security Issues, and Key Challenges

Sixth Generation (6G) Cognitive Radio Network (CRN) Application, Requirements, Security Issues, and Key Challenges

A throughput analysis of an energy-efficient spectrum sensing scheme for the cognitive radio-based Internet of things

Cognitive Radio Networks for Internet of Things and Wireless Sensor Networks

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