The performance evaluation of a spectrum sensing implementation using an automatic modulation classification detection method with a Universal Software Radio Peripheral

Popoola, Jide Julius; Olst, Rex van

doi:10.1016/j.eswa.2012.10.047

Cited by 17 publications

(19 citation statements)

References 22 publications

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“…Proposed model in [100] resulted that it can forecast the precise detection probability at the end of training and FC can obtain outstanding performance on judgment (satisfactory detection and false alarm probability) as compared to the conventional spectrum sensing techniques. Papoola and Van Olst in [107] proposed ANN-based modulation classifier for spectrum sensing. The Authors implemented proposed algorithm on GNU Radio and Universal Software Radio Peripheral 2 (USRP 2) for developing CRE.…”

Section: B Swarm Intelligence Algorithm: Swarm Intelligencementioning

confidence: 99%

A Survey on Soft Computing Techniques for Spectrum Sensing in a Cognitive Radio Network

Eappen

Shankar

2020

SN COMPUT. SCI.

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The need for faster wireless connectivity is increasing rapidly in all the sectors of the technologies. Whether it is a patient monitoring system, military application, entertainment services, streaming services, or global stock markets, there is a tremendous increase in the need for enhanced wireless telecommunication services. The wireless telecommunication consumers rely on bulk data, and massive growth in the number of users has resulted in the spectrum congestion. To avoid such spectrum congestion and to satisfy the data hunger of the wireless telecommunication users, the possible solution is Cognitive Radio Network (CRN). A CRN, therefore, plays a significant role in the field of wireless communication, and an efficient spectrum sensing enhances the effectiveness of the CRN. In this paper, complete research carried out so far in the field of spectrum sensing for CRN is discussed. Different soft computing techniques (GA, PSO, ABC, ACO, FFA, FSS, Cuckoo Search, ANN, FIS, GFIS) are surveyed in this paper, along with a detailed comparative analysis between conventional and soft computing techniques for spectrum sensing. In addition to that, the challenges faced in the implementation of CRN and its requirements is also addressed. Different spectrum sensing elements and requirements are presented and road map of spectrum sensing with soft computing techniques towards 5G is discussed. Furthermore, the paper also suggests the future prospects, research challenges and open issues associated with soft computing techniques for spectrum sensing in CRN.

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Section: B Swarm Intelligence Algorithm: Swarm Intelligencementioning

confidence: 99%

A Survey on Soft Computing Techniques for Spectrum Sensing in a Cognitive Radio Network

Eappen

Shankar

2020

SN COMPUT. SCI.

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“…In developing the GUI spectrum sensing algorithm, also refers to as the cognitive engine (CE), the activities involved was divided into three stages. The first stage involved the development of combined analog and digital automatic modulation recognition (ADAMR) classifier that was developed to recognize thirteen signals comprises of twelve different modulation schemes and one unmodulated signal [20]. The second stage involved the development of cooperative spectrum sensing time equation adapted from [21], which is expressed as:…”

Section: Development Of the Gui Spectrum Sensingmentioning

confidence: 99%

“…The ideal sensing time parameters obtained from (2) were used in the third stage to ensure that the cooperative spectrum sensing can work effectively without incurring a cooperative overhead. Interested reader(s) can read our early papers [16,20] for details information on these two stages. The third stage was centered on the development of proposed GUI spectrum sensing algorithm, which its architecture is shown in Figure 2.…”

Section: Development Of the Gui Spectrum Sensingmentioning

confidence: 99%

Untitled

2015

JASPE

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The wireless communication industry using radio spectrum is recently going through major innovations and advancements. With this transformation, the demand for and usage of radio spectrum has increased exponentially making radio spectrum indeed a scarce natural resource. In order to solve this problem, the possibility of opening up the unused portions of licensed spectrum by sharing using cognitive radio technology has been in the spotlight for maximizing radio spectrum utilization as well to as ensure sufficient radio spectrum availability for future wireless services and applications. With this objective in mind, this paper looks at the principles and technologies of cooperative spectrum sensing in cognitive radio environment in improving radio spectrum utilization. The paper provides a comprehensive review on spectrum sensing as a key functional requirement for cognitive radio technology by focusing on its application on dynamic spectrum access that enables unused portions of licensed spectrum to be used in an opportunistic manner as long as the operation of the unlicensed user will not affect that of the licensed user. In satisfying this dynamic spectrum access requirement, a friendly interactive graphical user interface (GUI) spectrum sensing application program was developed. The detail activities involve in the development of the application program, also known as spectrum sensing and detection algorithm (SSADA), was fully documented and presented in the paper. The developed graphical user interface application program after successfully developed was evaluated. The performance evaluations of developed graphical user interface sensing algorithm show that the algorithm performs favourably well. The program overall evaluation results provide bedrock information on how to improve cooperative spectrum sensing gain without incurring a cooperative overhead.

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“…The main challenge for a CR is how to detect the presence of the licensed or primary user (PU) in order to prevent interference between the PUs [4]. Here, we present a new method that can detect and track all forms of primary radio signals in a CR environment using automatic modulation classification (AMC) algorithms in a variable Manuscript received April 11, 2013.…”

Section: Introductionmentioning

confidence: 99%

Tracking performance of large margin classifier in automatic modulation classification with a software radio environment

Hosseinzadeh

2014

J. of Syst. Eng. Electron.

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Automatic modulation classification is the process of identification of the modulation type of a signal in a general environment. This paper proposes a new method to evaluate the tracking performance of large margin classifier against signal-tonoise ratio (SNR), and classifies all forms of primary user's signals in a cognitive radio environment. For achieving this objective, two structures of a large margin are developed in additive white Gaussian noise (AWGN) channels with priori unknown SNR. A combination of higher order statistics and instantaneous characteristics is selected as effective features. Simulation results show that the classification rates of the proposed structures are well robust against environmental SNR changes.

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The performance evaluation of a spectrum sensing implementation using an automatic modulation classification detection method with a Universal Software Radio Peripheral

Cited by 17 publications

References 22 publications

A Survey on Soft Computing Techniques for Spectrum Sensing in a Cognitive Radio Network

A Survey on Soft Computing Techniques for Spectrum Sensing in a Cognitive Radio Network

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Tracking performance of large margin classifier in automatic modulation classification with a software radio environment

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