Improved Learning Scheme for Cognitive Radio using Artificial Neural Networks

Mahajan, Rita; Bagai, Deepak

doi:10.11591/ijece.v6i1.pp257-267

Cited by 4 publications

(4 citation statements)

References 15 publications

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“…The proposed work over here is much different than [4] and [6], as they make use of Hidden Markov Model (HMM) and MAXNET based classifier. It is much different from the work proposed in [23] which focuses mainly on the spectrum sensing of predicted free channels using ANN. Instead, in this work we have proposed an enhanced algorithm for signal detection and classification using ANN which is implemented using LabVIEW and Matlab softwares.…”

mentioning

confidence: 78%

“…In [22], authors have provided an experimental performance evaluation of the Energy Detector based sensing using NI USRP-2930, which is a Software Defined Radio (SDR) transceiver. In [23], problem of future wireless network has been examined and new learning scheme using artificial neural network is proposed. The author of [24] has discussed a modulation classification method capable of classifying MFSK digital signals without a priori information using modified covariance method.…”

Section: Literature Reviewmentioning

confidence: 99%

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Enhanced signal detection slgorithm using trained neural network for cognitive radio receiver

Borde

Joshi

2019

IJECE

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<p>Over the past few years, Cognitive Radio has become an important research area in the field of wireless communications. It can play an important role in dynamic spectrum management and interference identification. There are many spectrum sensing techniques proposed in literature for cognitive radio, but all those techniques detect only presence or absence of the primary user in the designated band and do not give any information about the used modulation scheme. In certain applications, in cognitive radio receiver, it is necessary to identify the modulation type of the signal so that the receiver parameters can be adjusted accordingly. Most of the modulated signals exhibit the property of Cyclostationarity that can be used for the purpose of correct detection of primary user and the modulation type. In this paper, we have proposed an enhanced signal detection algorithm for cognitive radio receiver which makes use of cyclostationarity property of the modulated signal to exactly detect, the modulation type of the received signal using a trained neural network. The algorithm gives better accuracy of signal detection even in low SNR conditions. The use of a trained neural network makes it more flexible and extendible for future applications</p>

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confidence: 78%

Section: Literature Reviewmentioning

confidence: 99%

Enhanced signal detection slgorithm using trained neural network for cognitive radio receiver

Borde

Joshi

2019

IJECE

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“…Another small number of neurons in hidden layers result in poor amount of accuracy and large number of neurons in the hidden layer may not be able to generalise its efficiency. As soon as this architecture is decided it may also be used as in the cases of the cognitive users that have a prediction and also a performance of this network improving significantly (Mahajan and Bagai, 2016a).…”

Section: Mlp Without Structure Optimisationmentioning

confidence: 99%

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

Bhuvaneswari¹,

Meeradevi²

2020

IJENM

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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.

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“…Previously, this method of allocation worked perfectly however, due to an increase and variation in the demand for the use of the radio spectrum, this scheme is leading to "artificial spectrum scarcity" [4]. This is because the paucity of the radio spectrum depends on location (space) and the time of the day (time) [5][6][7]. In addition to this, fixed spectrum allocation creates spectrum holes also known as white space thereby, degrading the spectral efficiency of the radio spectrum.…”

Section: Introductionmentioning

confidence: 99%

Energy Efficient Dynamic Bid Learning Model for Future Wireless Network

Oloyede¹,

Olawoyin²,

Bello³

et al. 2018

J. Sib. Fed. Univ. Eng. technol.

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In this paper, an energy efficient learning model for spectrum auction based on dynamic spectrum auction process is proposed. The proposed learning model is based on artificial intelligence. This paper examines and establishes the need for the users to learn their bid price based on information about the previous bids of the other users in the system. The paper shows that using Q reinforcement learning to learn about the bids of the users during the auction process helps to reduce the amount of energy consumed per file sent for the learning users. The paper went further to modify the traditional Q reinforcement learning process and combined it with Bayesian learning because of the deficiencies associated with Q reinforcement learning. This helps the exploration process to converge faster thereby, further reducing the energy consumption by the system.

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Improved Learning Scheme for Cognitive Radio using Artificial Neural Networks

Cited by 4 publications

References 15 publications

Enhanced signal detection slgorithm using trained neural network for cognitive radio receiver

Enhanced signal detection slgorithm using trained neural network for cognitive radio receiver

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

Energy Efficient Dynamic Bid Learning Model for Future Wireless Network

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