Faulty Node Detection in HMM-Based Cooperative Spectrum Sensing For Cognitive Radio Networks

Das, Soumya; Acharya, Tamaghna

doi:10.1093/comjnl/bxx127

Cited by 4 publications

(2 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It also can prove that the work that has been extending the actual number of hidden states while at the time of formulating the problem of prediction that will enable another span of many slots used in future with a fine spectrum sensing resolution. This type of a suitability of the approach to that of the industrial wireless by means of making extensive simulations was checked and the model was specifically tailor made for automotive settings in the industry Das and Acharya (2018) had evaluated its reliability with a cooperative spectrum sensing (CSS) in the low signal-to-noise ratio (SNR) values and the work has proposed another new scheme that can detect faulty nodes within the system of the CSS having a high accuracy and will quarantine them for the maintenance of reliability of the process of spectrum prediction. This scheme that has been proposed has suggested a very novel integration of that of the forward algorithm of the HMM using the fuzzy-c means (FCM) based clustering technique for designing a robust spectrum prediction that will assist the CSS in the CRNs.…”

Section: Related Workmentioning

confidence: 99%

An optimised neural network-based spectrum prediction scheme for cognitive radio

Bhuvaneswari¹,

Meeradevi²

2020

IJENM

View full text Add to dashboard Cite

A cognitive radio (CR) technology enables all the users to utilise spectrum without interference. There will be a spectrum sensing for all the non-authorised users to perceive the other possibilities of getting a channel. The traffic feature will be unknown to be a priori to design the spectrum predictor with the back propagation (BP) neural network (NN) model and the multi-layer perceptron (MLP).This work proposed an optimised neural network to obtain improved results. The BP algorithm will not require prior knowledge of the real world problems that are trapped within the local minima. This is used widely to solve the problems and found in literature as an evolutionary algorithm like the bacterial foraging optimisation algorithm (BFOA) used for the MLP NN for enhancing the process of learning and improving the rate of convergence as well as accuracy of classification. Performing this spectrum predictor will be analysed using some extensive simulations.

show abstract

Section: Related Workmentioning

confidence: 99%

An optimised neural network-based spectrum prediction scheme for cognitive radio

Bhuvaneswari¹,

Meeradevi²

2020

IJENM

View full text Add to dashboard Cite

show abstract

“…In recent years, CSS based on machine learning (ML) techniques such as the hidden Markov model (HMM) [22], artificial neural network (ANN) [23], reinforcement learning (RL) [24], genetic algorithms (GA) [25], have been proved as effective solutions for the CR task. GA, HMM, and ANN are suitable for transceiver parameters like spectrum sensing and channel selection.…”

Section: Introductionmentioning

confidence: 99%