A hybrid spectrum sensing approach to select suitable spectrum band for cognitive users

The main task of spectrum sensing in cognitive radio network is to decide whether the primary user is occupying the specific spectrum band or not. So the main purpose of spectrum sensing is to design a detector with better detection performance. This paper studies a spectrum sensing method with clustering under cognitive radio networks. We studied the cooperative spectrum sensing model with clustering by hard fusion rule, and also proposed the simulation model and steps of this cluster-based spectrum sensing problem under Majority rule. Simulation results show that the spectrum sensing method with clustering has better performance than the other methods.

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Basic Spectrum Sensing Techniques

Advances in Wireless Technologies and Telecommunication - Sensing Techniques for Next Generation Cognitive Radio Networks ◽

10.4018/978-1-5225-5354-0.ch004 ◽

2019 ◽

pp. 70-84

Author(s):

Bhuvaneswari P. T. V. ◽

Bino J.

Keyword(s):

Cognitive Radio ◽

Spectrum Sensing ◽

Spectrum Sharing ◽

Cognitive Radio Network ◽

Radio Spectrum ◽

Spectrum Management ◽

Radio Network ◽

Spectrum Band ◽

Radio Systems ◽

Networking Technology

Cognitive radio network (CRN) is an upcoming networking technology that can utilize both radio spectrum and wireless resources efficiently based on the information gathered from the past experience. There are two types of users in CRN, namely primary and secondary. PUs (PU) have the license to operate in certain spectrum band while the secondary (SU) or cognitive radio (CR) users do not have the license to operate in the desired band. However, they can opportunistically utilize the unused frequency bands. Spectrum sensing, spectrum management, spectrum sharing, and spectrum mobility are the four major functions of cognitive radio systems. The main objective of spectrum sensing is to provide better spectrum access to CR users, without causing any harmful interference to PUs. Sensing accuracy is considered as the most important factor to determine the performance of cognitive radio network. In this chapter, the challenges and requirement involved in spectrum sensing are detailed. Further, various spectrum sensing basic techniques are also discussed in detail.

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

International Journal of Intelligent Unmanned Systems ◽

10.1108/ijius-08-2019-0033 ◽

2020 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Suneetha Ch ◽

Srinivasa Rao S ◽

K.S. Ramesh

Keyword(s):

Cognitive Radio ◽

Service Quality ◽

Spectrum Sensing ◽

Cognitive Radio Networks ◽

Radio Networks ◽

Quality Factors ◽

Secondary Users ◽

Content Type ◽

Primary Users ◽

Spectrum Band

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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Channel State Prediction in Cognitive Radio

Advances in Wireless Technologies and Telecommunication - Cognitive Radio and Interference Management ◽

10.4018/978-1-4666-2005-6.ch015 ◽

2012 ◽

pp. 273-288

Author(s):

Zhe Chen

Keyword(s):

Cognitive Radio ◽

Time Delay ◽

Spectrum Sensing ◽

Negative Impact ◽

Real World Data ◽

Channel State ◽

Current Channel ◽

State Prediction ◽

Spectrum Band ◽

Hardware Platforms

Spectrum sensing is the cornerstone of cognitive radio, which detects the availability of a spectrum band for the current time. In theory, the result of spectrum sensing reflects the current channel state, which is the ideal case. However, according to the author’s measurements, hardware platforms can introduce a non-negligible time delay on the signal path, which undermines the accuracy of spectrum sensing. To reduce the negative impact of the hardware platform time delay, channel state prediction in cognitive radio is proposed and presented in this chapter. As examples, channel state prediction algorithms based on a modified hidden Markov model (HMM) are given and tested using recorded real-world data. Moreover, as a second stage, cooperative channel state prediction is also proposed and experimentally evaluated. The experimental results approve that channel state prediction in cognitive radio indeed helps improve the accuracy of spectrum sensing in practical cases.

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