Technical Issues on Cognitive Radio-Based Internet of Things Systems: A Survey

Awin, Faroq; Alginahi, Yasser M.; Abdel-Raheem, Esam; Tepe, Kemal

doi:10.1109/access.2019.2929915

Cited by 147 publications

(74 citation statements)

References 141 publications

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“…In other words, noise samples cannot reach the threshold when adding margin to the average noise floor level, therefore, false alarm will be reduced as only a PU signal can exceed it. However, such thresholds prevent detecting weak PU signals which might result in missed detection, therefore, a margin value should be selected carefully [28,29]. In Figure 5 we can observe X( f ), sampled with RFE in panel (b) and with SA in panel (d).…”

Section: Threshold Considerationsmentioning

confidence: 99%

An Accurate Probabilistic Model for TVWS Identification

et al. 2019

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Television White Spaces (TVWS)-based cognitive radio systems can improve spectrum efficiency by facilitating opportunistic usage of television broadcasting spectrum by secondary users without interfering with primary users. Previously applied models introduce missed detection errors, giving a limited estimation of the spectrum occupancy, which does not correspond to the reality of its usage, hence resulting in a partial waste of this resource. Considering jointly parameters like false alarm probability and detection probability, this article proposes a probabilistic model that can identify TVWS with improved accuracy. The proposed model considers energy detection criteria, combined with simultaneous sensing of the noise and of the signal from primary users. In order to demonstrate the model effectiveness, a low-cost Mobile Spectrum Sensing Station prototype was designed, implemented, and subsequently mounted on a vehicle. More than eight million spatio-temporally variant data samples were collected by scanning the UHF-TV spectrum of 500–698 MHz in the city of Windsor, ON, Canada. Analysis of the collected data showed that the proposed model achieves an accuracy improvement of about 9.6% compared to existing models, demonstrating that TVWS vary with spatial displacement and increasing significantly in the rural areas. Even in the most crowded spectrum zone, about 28% of the channels are identified as TVWS, and this number increases to a maximum of 60% in less crowded regions in urban areas. We conclude that the proposed model improves the TVWS detection compared with other used models, and also that the elements considered in this research contribute to reduce the complexity of the mathematical calculations while maintaining the accuracy. A low-cost open-source sensing station has been designed and tested, which represents an operative and useful data source in this setting.

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Section: Threshold Considerationsmentioning

confidence: 99%

An Accurate Probabilistic Model for TVWS Identification

et al. 2019

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“…In non-centralized scheme, individual CRs detect the existence of the PU and then share their local observations between each other and come up with a final decision about the existence of the PU through a consensus. Finally, in relay-assisted scheme, after detection, a CR amplifies or decodes its observation and forwards it to another CR [23].…”

Section: Detection Performance Of Cooperative Sensingmentioning

confidence: 99%

Combined Fusion Rules in Cognitive Radio Networks Using Different Threshold Strategies

2019

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Compromising the performance and overhead is a crucial factor in designing cognitive radio networks (CRNs). One way to achieve this goal is to combine different fusion rules for a CRN with multiple clusters of cognitive radios (CRs). This paper proposes a new adaptive combination algorithm to balance between detection performance of a CRN and its reporting overhead through combining different fusion rules over the CRN. Initially, the paper describes how to combine hard decision, i.e., one-bit, and soften-hard decision, i.e., two-bit, fusion rules over a CRN with multiple clusters of CRs using different strategies. Simple combination and modified combination strategies, to consider a trade off between performance improvement and incurred reporting overhead, are considered. The paper adopts different threshold strategies to implement the proposed combinations. Moreover, the proposed algorithms are examined under the Rayleigh fading channel model and simulated to investigate their detection performance and to compare their detection performance with existing works. The simulation results show that the adaptive threshold strategy outperforms the two proposed fixed threshold strategies and conventional fusion schemes.

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“…Therefore, efficient spectrum management and resource allocation are crucial for CRNs to solve the shortage of spectrum resources and improve spectrum utilization [3,4]. DSA in CRNs can be categorized into three modes: overlay, interweave, and underlay [4][5][6]. Theoretically, underlay mode can significantly improve spectrum efficiency due to the fact that it allows secondary users (SUs) to access the licensed spectrum along with active primary users (PUs) at the same time [4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…DSA in CRNs can be categorized into three modes: overlay, interweave, and underlay [4][5][6]. Theoretically, underlay mode can significantly improve spectrum efficiency due to the fact that it allows secondary users (SUs) to access the licensed spectrum along with active primary users (PUs) at the same time [4][5][6][7][8]. In interweave mode [5,6,9], SUs can be allowed to access the vacant spectrum which is not occupied by any active PUs.…”

Section: Introductionmentioning

confidence: 99%

“…Whenever a PU becomes active, SUs must vacate the licensed spectrum immediately. In overlay mode, SUs are allowed to simultaneously share the licensed spectrum bands with PUs without imposing any constraint on SUs' transmission power if SUs have a full knowledge of PUs' signals characteristics [5,6], which is infeasible in practice due to the difficulty to obtain all the prior knowledge of PU's signals. Compared to overlay mode, interweave and underlay technologies can achieve higher spectrum utilization.…”

Section: Introductionmentioning

confidence: 99%

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Spectrum Handoff Based on DQN Predictive Decision for Hybrid Cognitive Radio Networks

Cao

Qian

2020

Sensors

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Spectrum handoff is one of the key techniques in a cognitive radio system. In order to improve the agility and the reliability of spectrum handoffs as well as the system throughput in hybrid cognitive radio networks (HCRNs) combing interweave mode with underlay mode, a predictive (or proactive) spectrum handoff scheme based on a deep Q-network (DQN) for HCRNs is proposed in this paper. In the proposed spectrum handoff approach, spectrum handoff success rate is introduced into an optimal spectrum resource allocation model to ensure the reliability of spectrum handoff, and the closed-form expression for the spectrum handoff success rate is obtained based on the Poisson distribution. Furthermore, we exploit the transfer learning strategy to further improve the DQN learning process and finally achieve a priority sequence of target available channels for spectrum handoffs, which can maximize the overall HCRNs throughput while satisfying constraints on secondary users’ interference with primary user, limits on the spectrum handoff success rate, and the secondary users’ performance requirements. Simulation results show that the proposed spectrum handoff scheme outperforms the state-of-the-art spectrum handoff algorithms based on predictive decision in terms of the convergence rate, the handoff success rate and the system throughput.

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Technical Issues on Cognitive Radio-Based Internet of Things Systems: A Survey

Cited by 147 publications

References 141 publications

An Accurate Probabilistic Model for TVWS Identification

An Accurate Probabilistic Model for TVWS Identification

Combined Fusion Rules in Cognitive Radio Networks Using Different Threshold Strategies

Spectrum Handoff Based on DQN Predictive Decision for Hybrid Cognitive Radio Networks

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