Radio Spectrum Management for Cognitive Radio Based on Fuzzy Neural Methodology

Yang, Hang; Liang, Yuan; Miao, Jingcheng; Zhao, Dongmei

doi:10.1007/978-3-319-61566-0_56

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…The architecture simulated the objective of focusing on dynamic spectrum management which has been tested under pragmatic conditions for achieving the outcome in more efficient ways of channel selection (Yang et al , 2017). It has even been proposed the negotiation-based scheme for the prerogatives of channel switch (Jacob et al , 2015), wherein the emphasis of the fuzzy logic controllers were primarily about price and duration negotiations.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy guided integrative factors-based spectrum decision-making in cognitive radio networks

S²,

Ramesh

2020

IJIUS

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PurposeElectronic devices aid communication during new communication phases and the scope of cognitive radio networks has changed communication paradigms through efficient use of spectrums. The communication prototype of cognitive radio networks defines user roles as primary user and secondary user in the context of the spectrum allocation and use. The users who have licensed authority of the spectrum are denoted as primary users, while other eligible users who access the corresponding spectrum are secondary users.Design/methodology/approachThe multiple factors of transmission service quality can have a negative influence due to improper scheduling of spectrum bands between primary users and secondary users. There are considerable contributions in contemporary literature concerning spectrum band scheduling under spectrum sensing. However, the majority of the scheduling models are intended to manage a limited number of transmission service quality factors. Moreover, these service quality factors are functional and derived algorithmically from the current corresponding spectrum. However, there is evidence of credible performance deficiency regarding contemporary spectrum sensing methodsFindingsThis article intends to portray a fuzzy guided integrated factors-based spectrum band sharing within the spectrum used by secondary users. This study attempts to explain the significance of this proposal compared to other contemporary models.Originality/valueThis article intends to portray a fuzzy guided integrated factors-based spectrum band sharing within the spectrum used by secondary users. This study attempts to explain the significance of this proposal compared to other contemporary models.

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Section: Related Workmentioning

confidence: 99%

Fuzzy guided integrative factors-based spectrum decision-making in cognitive radio networks

S²,

Ramesh

2020

IJIUS

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“…However, density is measured as the frequency of any channel used by the PU, and the distance between the PU and the SU base stations is represented as mobility. In [17], an architecture was simulated for dynamic spectrum management and was then implemented in a realtime scenario to achieve results in which distance, signal strength, and node velocity are the input parameters for fuzzy logic reinforcement learning to make the licensed channel selection. Jacob et al [18] proposed a negotiation-based scheme to reduce the channel handoff rates.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Fuzzy Logic Scheme for Efficient Channel Utilization in Cognitive Radio Networks

et al. 2019

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The proliferation of mobile devices and the heterogeneous environment of wireless communications have increased the need for additional spectrum for data transmission. It is not possible to altogether allocate a new band to all networks, which is why fully efficient use of the already available spectrum is the demand of the day. Cognitive radio (CR) technology is a promising solution for efficient spectrum utilization, where CR devices, or secondary users (SUs), can opportunistically exploit white spaces available in the licensed channels. SUs have to immediately vacate the licensed channel and switch to another available channel when they detect the arrival of the incumbent primary user. However, performance for the SU severely degrades if successive channel switching happens. Moreover, taking the channel-switching decisions based on crisp logic is not a suitable approach in the brain-empowered CR networks (CRNs) where sensing information is not only imprecise and inaccurate but also involves a major uncertainty factor. In this paper, we propose a fuzzy logic-based decision support system (FLB-DSS) that jointly deals with channel selection and channel switching to enhance the overall throughput of CRNs. The proposed scheme reduces the SU channel switching rate and makes channel selection more adaptable. The performance of the proposed scheme is evaluated using a Matlab simulator, and a comprehensive comparison study with a baseline scheme is presented. The simulation results are promising in terms of the throughput and the number of handoffs and making our proposed FLB-DSS a good candidate mechanism for SUs while making judicious decisions in the CR environment. INDEX TERMS Cognitive radio network, fuzzy logic, resource allocation, channel selection, handoff rate.

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“…This framework provides the best convergence stability but suffered from overfitting as well as overtraining issues in various nodes. Yang et al (2017) designed fuzzy neural network system for CR that offered feasible, time-saving computations, and simple, but usage of spectrum is inefficient. Reyes et al (2016) developed SS based on autocorrelation of the received samples.…”

Section: Motivationmentioning

confidence: 99%

A hybrid model for energy efficient spectrum sensing in cognitive radio

Bhowmik

Malathi

2019

IJICC

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Purpose Cognitive radio (CR) plays a very important role in enabling spectral efficiency in wireless communication networks, where the secondary user (SU) allows the licensed primary users (PUs). The purpose of this paper is to develop a prediction model for spectrum sensing in CR. Design/methodology/approach This paper proposes a hybrid prediction model, called krill-herd whale optimization-based actor critic neural network and hidden Markov model (KHWO-ACNN-HMM). The spectral bands are determined optimally using the proposed hybrid prediction model for allocating the spectrum bands to the PUs. For better sensing, the eigenvalue based on cooperative sensing used in CR. Finally, a hybrid model is designed by hybridizing KHWO-ACNN and HMM to enhance the accuracy of sensing. The predicted results of KHWO-ACNN and HMM are combined by a fusion model, for which a weighted entropy fusion is employed to determine the free spectrum available in CRs. Findings The performance of the prediction model is evaluated based on metrics, such as probability of detection, probability of false alarm, throughput and sensing time. The proposed spectrum sensing method achieves maximum probability of detection of 0.9696, minimum probability of false alarm rate as 0.78, minimum throughput of 0.0303 and the maximum sensing time of 650.08 s. Research implications The proposed method is useful in various applications, including authentication applications, wireless medical networks and so on. Originality/value A hybrid prediction model is introduced for energy efficient spectrum sensing in CR and the performance of the proposed model is evaluated with the existing models. The proposed hybrid model outperformed the other techniques.

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Radio Spectrum Management for Cognitive Radio Based on Fuzzy Neural Methodology

Cited by 4 publications

References 4 publications

Fuzzy guided integrative factors-based spectrum decision-making in cognitive radio networks

Fuzzy guided integrative factors-based spectrum decision-making in cognitive radio networks

Hybrid Fuzzy Logic Scheme for Efficient Channel Utilization in Cognitive Radio Networks

A hybrid model for energy efficient spectrum sensing in cognitive radio

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