Optimized neural network for spectrum prediction using genetic algorithm in cognitive radio networks

Supraja, P.; Gayathri, V. M.; Pitchai, R.

doi:10.1007/s10586-018-1978-5

Cited by 29 publications

(19 citation statements)

References 16 publications

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“…In that for comparative results of proposed algorithm, here we check the interarrival time = 15 when probability of idle channel status is 0.5. The results obtained are 0.0581, 0.0572, and 0.0497 with GA optimization, 16 SFLA optimization, 25 and proposed GA‐SFLA optimization, respectively. Thus, the results show that the probability error of prediction has reduced to 1.7% while in GA Optimization and 3.4% while in SFLA optimization, whereas proposed hybrid GA‐SFLA has achieved reducing 17% of error rate during idle channel status.…”

Section: Resultsmentioning

confidence: 96%

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Hybrid genetic and shuffled frog‐leaping algorithm for neural network structure optimization and learning model to predict free spectrum in cognitive radio

Supraja

Babu

Gayathri

et al. 2020

Int J Communication

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Summary Cognitive radio, which is called an intelligent radio, will dynamically access the available spectrum. This mechanism will bring revolt in wireless communication, which lightens the spectrum utilization problem. Machine learning plays a vital role in every technology. Here, in cognitive radio, it helps train the model to predict the free spectrum. The cognitive clients utilize range‐detecting procedures to detect the groups previously transmitting on them to stay away from impact with the authorized clients that prompts deferral and moderates more vitality. To decrease postponement and vitality utilization and to foresee the future use of channels, range expectation procedures are utilized. Spectrum prediction is used to anticipate the future channel status in light of gathered chronicled information; here, to take care of the issue, the neural system‐based spectrum prediction utilizes a backpropagation training model that has been proposed. To enhance the structure of the neural system and to decrease the forceful weight auxiliary pattern, genetic algorithm (GA) along with the hybrid combination of shuffled frog‐leaping algorithm (SFLA) is proposed. Here, GA has been utilized to abstain from catching nearby ideal solutions. The selection, crossover, and mutation functions were performed to build the haphazardness, which stretches out the populace unite to the set that contains the global ideal solution. SFLA has been proposed for structure improvement; the paired structure has been recommended to demonstrate the memes with the motivation behind building up a subaccumulation with lesser measurements than that of the original group where recognizing affectability and precision would be versatile with that of the primary status. Simulation results show the GA‐SFLA‐based hybrid algorithm that is used has increased the results of getting the best weights by improving the system; additionally, the proposed conspire results show high forecast accuracy.

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Section: Resultsmentioning

confidence: 96%

“…The pair of string values called 0's and 1's 13 speak to each solution. Numerous analyses here are examined about the application 14 and streamlining procedures in methods for GA 15–17 . Here, the collection of individual chromosomes is known as populace.…”

Section: Introductionmentioning

confidence: 99%

Hybrid genetic and shuffled frog‐leaping algorithm for neural network structure optimization and learning model to predict free spectrum in cognitive radio

Supraja

Babu

Gayathri

et al. 2020

Int J Communication

Self Cite

View full text Add to dashboard Cite

show abstract

“…Furthermore, ML and DL are used for spectrum perceptions, i.e., prediction of the occupancy of the PU channels [ 89 , 103 , 104 ]. For example, a geo-frequency-temporal map on the PU activities can be constructed using learning techniques.…”

Section: Learning Techniques For Spectrum Sensingmentioning

confidence: 99%

Spectrum Sensing for Cognitive Radio: Recent Advances and Future Challenge

Nasser

Hassan

Chaaya

et al. 2021

Sensors

124

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Spectrum Sensing (SS) plays an essential role in Cognitive Radio (CR) networks to diagnose the availability of frequency resources. In this paper, we aim to provide an in-depth survey on the most recent advances in SS for CR. We start by explaining the Half-Duplex and Full-Duplex paradigms, while focusing on the operating modes in the Full-Duplex. A thorough discussion of Full-Duplex operation modes from collision and throughput points of view is presented. Then, we discuss the use of learning techniques in enhancing the SS performance considering both local and cooperative sensing scenarios. In addition, recent SS applications for CR-based Internet of Things and Wireless Sensors Networks are presented. Furthermore, we survey the latest achievements in Spectrum Sensing as a Service, where the Internet of Things or the Wireless Sensor Networks may play an essential role in providing the CR network with the SS data. We also discuss the utilisation of CR for the 5th Generation and Beyond and its possible role in frequency allocation. With the advancement of telecommunication technologies, additional features should be ensured by SS such as the ability to explore different available channels and free space for transmission. As such, we highlight important future research axes and challenging points in SS for CR based on the current and emerging techniques in wireless communications.

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“…A backward propagation training model using neural networks was proposed to predict the future channel state from historic data [18]. In order to reduce the aggressive structural pattern and optimize the structure of a neural network, the genetic algorithm was used to avoid local optimal solution.…”

Section: Related Workmentioning

confidence: 99%

Investigative analysis of channel selection algorithms in cooperative spectrum sensing in cognitive radio networks

Tlouyamma

Velempini

2021

SAIEE Afr. Res. J.

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The proliferation of wireless mobile devices has led to a number of challenges in mobile data communication. The world is experiencing an increasing usage of finite spectrum bands for social media and other data communication services. It is due to this high usage that the Federal Communications Commission (FCC) sought to open up some spectrum bands to be used opportunistically by secondary users (SUs). However, the coexistence of Primary Users (PUs) and SUs may cause interference which leads to wastage of spectrum resources. This study investigates the impact of interferences between PUs and SUs. To ensure higher detection of PU signal, a cooperative rule was used to decide which SU to share and make a final decision about the availability of the spectrum band. To maximize the throughput of SU, a maximum likelihood function was designed to reduce delays in searching for the next available channel for data transmission. To discover more transmission opportunities and ensuring that a good number of free channels are detected, a parallel sensing technique was employed. Matlab was used to simulate and generate the results in a distributed cognitive radio environment. The proposed extended generalized predictive channel selection algorithm (EXGPCSA) outperformed other schemes in literature in terms of throughput, service time and probability of detection.

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Optimized neural network for spectrum prediction using genetic algorithm in cognitive radio networks

Cited by 29 publications

References 16 publications

Hybrid genetic and shuffled frog‐leaping algorithm for neural network structure optimization and learning model to predict free spectrum in cognitive radio

Hybrid genetic and shuffled frog‐leaping algorithm for neural network structure optimization and learning model to predict free spectrum in cognitive radio

Spectrum Sensing for Cognitive Radio: Recent Advances and Future Challenge

Investigative analysis of channel selection algorithms in cooperative spectrum sensing in cognitive radio networks

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