Spectrum Occupancy Prediction Based on Functional Link Artificial Neural Network (FLANN) in ISM Band
Recent advancements in artificial neural networks (ANNs) motivated us to design a simple and faster spectrum prediction model termed the functional link artificial neural network (FLANN). The main objective of this paper is to gather realistic data to obtain utilization statistics for the industrial, scientific and medical band of 2.4–2.5 GHz. To present the occupancy statistics, we conducted measurement in indoors at the Swearingen Engineering Center, University of South Carolina. Further, we introduce different threshold-based spectrum prediction schemes to show the impact of threshold on occupancy, and propose a spectrum prediction algorithm based on FLANN to forecast a future spectrum usage profile from historical occupancy statistics. Spectrum occupancy is estimated and predicted by employing different ANN models including the Feed-forward multilayer perceptron (MLP), Recurrent MLP, Chebyshev FLANN and Trigonometric FLANN. It is observed that the absence of a hidden layer in FLANN makes it more efficient than the MLP model in predicting the occupancy faster and with less complexity. A set of illustrative results are presented to validate the performance of our proposed learning scheme.
Postprint version. Published in Neural Computing and Applications, 2016, pages 1-14.
© Neural Computing and Applications, 2016, Springer
Das, D., Matolak, D., Das, S. (2016). Spectrum Occupancy Prediction Based on Functional Link Artificial Neural Network (FLANN) in ISM Band. Neural Computing and Applications, 1-14.