基于SCADA-DWWAN的神经网络自适应均衡带宽优化模型

Priyanko Raj Mudiar, K. K. Sarma, N. Mastorakis
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引用次数: 0

摘要

人工训练和学习算法,增强了半监督或自监督特征提取能力,采用自适应决策优化模型。这些通常比复杂的深度学习算法更受青睐,因为它们可以实现更好的可控性和易于观察,在模拟、构建或设计以及自动网络资源管理(ANRM)标准的虚拟原型方面具有较低的复杂性。在与分布式控制系统(SCADA/DCS-Net)集成的虚拟监控与数据采集(SCADA)框架中,对基于多锥度机器学习方法的自适应线性神经元型人工神经网络(ADALINE-ANN)进行了仿真。采用基于卡尔曼优化的马尔可夫训练最陡梯度下降(HMM-SGD)机器学习模型,采用自适应均衡学习和决策方法对该系统进行了虚拟仿真。采用亲和聚类技术从经过AWGN和Rayleigh衰落以及同信道干扰的m -正交调幅(QAM)信号中提取星座奈奎斯特频带进行频谱感知,并利用信道状态空间图进行集成分析,在自适应正交频分多址(Adaptive- ofdma)布局中进行最优频谱分配。实现了一种利用最小二乘误差(MLSE)最小化模型的自适应均衡自动重复请求(ARQ)流水线模型。目标是改善带宽分配和使用,确保频谱浪费或损失最小。连续干扰消除(SIC)实现了最小的静态缓冲和干扰损失。因此,通过改进网络资源跟踪和分配,可以最大限度地减少由带宽拥塞引起的延迟和抖动造成的频谱损失。它的结果是改进和稳定的带宽均衡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptively Equalized Bandwidth Optimization Model using SCADA-DWWAN based Neural Network
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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