WSEAS Transactions on Electronics
Print ISSN: 1109-9445, E-ISSN: 2415-1513
Volume 13, 2022
Adaptively Equalized Bandwidth Optimization Model using SCADA-DWWAN based Neural Network
Authors: , ,
Abstract: Artificial training and learning algorithms, enhanced with semi-supervised or self-supervised feature extraction capacities, employ adaptive decision optimization models. These are often favored over complex deep learning algorithms for achieving better controllability and ease of observation, lower complexity in simulating, building or designing and virtual prototyping of automatic network resource management (ANRM) standards. An Adaptive Linear Neuron type Artificial Neural Network (ADALINE-ANN) which is based on multi-tapered machine learning approach has been simulated in a virtual Supervisory Control and Data Acquisition (SCADA) framework integrated with a Distributed Control System (SCADA/DCS-Net). The system has been virtually simulated considering an adaptively equalized learning and decision approach which utilizes Markov Trained-Steepest Gradient Descent (HMM-SGD) based machine learning model employing Kalman optimization. Affinity clustering is employed for spectrum sensing by extracting the Constellation Nyquist Bands from an M-Quadrature Amplitude Modulation (QAM) orthogonal signal undergoing AWGN and Rayleigh fading as well as co-channel interference (CCI), and ensemble analysis using Channel State-Space plots are used for optimal spectrum allocation in an Adaptive Orthogonal Frequency Division Multiple Access (Adaptive-OFDMA) layout. It has been done by implementing an adaptively equalized Automatic Repeat Request (ARQ) pipelining model which utilizes minimum least square error (MLSE) minimization model. The objective is to improve the bandwidth allocation and usage ensuring most minimum spectrum wastage or loss. Successive Interference Cancellation (SIC) has been implemented to minimize static buffer and interference loss. Thus, spectrum loss due to latency and jitter which occurs from bandwidth congestion is minimized by improving the network resource tracking and allocation. It results in improved and stable bandwidth equalization.
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Keywords: 256-Quaternary Amplitude Modulation(256-QAM), ADALINE, Kalman-Least Mean Square Algorithm (Kalman- LMS), Kalman optimized Kernel Recursive Least Square Algorithm (Kalman-KRLS), Affinity Propagation Clustering (AP- Clustering), Maximum Correntropy, ANOVEE, Eigen-Plot, Fourier-Bessel Transform, Huang-Hilbert Transform, Hidden Markov Model (HMM), Steepest Gradient Descent (SGD), Adaptive Automatic Repeat Request (Hybrid-ARQ), Maximal Ratio Combination Channel Diversity (MRCD), Successive Interference Cancellation (SIC).
Pages: 107-114
DOI: 10.37394/232017.2022.13.14