"Artificial Intelligence Based Model for Channel Status Prediction: A New Spectrum Sensing Technique for Cognitive Radio"
written by Sandhya Pattanayak, Palanaindavar Venkateswaran, Rabindranath Nandi,
published by International Journal of Communications, Network and System Sciences, Vol.6 No.3, 2013
has been cited by the following article(s):
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[2] An optimised neural network-based spectrum prediction scheme for cognitive radio
[3] Implementation of a machine learning based modulation scheme in GNURadio for over-the-air packet communications
[4] Machine Learning Applied to an RF Communication Channel
[5] Modeling and Prediction Primary Nodes in Wireless Networks of Cognitive Radio Using Recurrent Neural Networks
[6] Probability Density Function Estimation in OFDM Transmitter and Receiver in Radio Cognitive Networks based on Recurrent Neural Network
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[10] From Sensing to Predictions and Database Technique: A Review of TV White Space Information Acquisition in Cognitive Radio Networks
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[11] From Sensing to Predictions and Database Technique
[12] Implementación de un modelo predictor para la toma de decisiones en redes inalámbricas de radio cognitiva
[13] Soft Decision based Spectrum Sensing for Cognitive Radio Networks
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[14] Spectrum hole detection in TV band using ANN model for opportunistic radio communication
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[15] Identification of spectrum holes using ANN model in TV bands with AWGN
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[16] Cooperative spectrum sensing optimization based adaptive neuro‑fuzzy inference system (ANFIS) in cognitive radio networks