架空传输多输入多输出宽带电力网的容量性能:噪声的潜在影响和噪声模型的作用

A. Lazaropoulos
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引用次数: 10

摘要

在文献[1]分析的基础上,本文考虑了架空传输多输入多输出(MIMO)宽带电力线(BPL)网络在不同噪声条件下的宽带潜力,并采用了不同的经过验证的噪声模型。本文的贡献是双重的。首先,根据适当的容量指标,研究了大量指示性OV高压(HV) BPL拓扑和MIMO传输方案的宽带潜力。相关的数值结果揭示了ΜΙΜΟ容量指标对噪声条件的显著依赖性。其次,比较了文献中各种知名的BPL噪声模型,基于它们实现的OV HV MIMO BPL容量。通过对噪声模型容量结果的仔细研究,证明频谱平坦的加性高斯白噪声(AWGN)可以很好地假设为传输MIMO BPL网络的有效噪声模型。同样在MIMO BPL网络中,噪声模型的容量比较分析表明,在3-88MHz频率范围内,它们之间的差异很小。引用本文:Lazaropoulos, a.g.(2016)。架空传输多输入多输出宽带电力网的容量性能:噪声的潜在影响和噪声模型的作用。可再生能源发展趋势,2(2),61-82。DOI: 10.17737 / tre.2016.2.2.0023
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Capacity Performance of Overhead Transmission Multiple-Input Multiple-Output Broadband over Power Lines Networks: The Insidious Effect of Noise and the Role of Noise Models
Extending the analysis already presented in [1], this paper considers broadband potential of overhead (OV) transmission multiple-input multiple-output (MIMO) broadband over power lines (BPL) networks when different noise conditions occur and different well-proven noise models are adopted. The contribution of this paper is two-fold. First, the broadband potential of a great number of indicative OV high-voltage (HV) BPL topologies and of MIMO transmission schemes is studied in terms of appropriate capacity metrics. The relevant numerical results reveal the significant dependence of ΜΙΜΟ capacity metrics on noise conditions. Second, various well-known BPL noise models from the literature are compared on the basis of their achieved OV HV MIMO BPL capacity. Through the careful study of the capacity results of noise models, it is demonstrated that spectrally flat additive white Gaussian noise (AWGN) may be comfortably assumed as an efficient noise model in transmission MIMO BPL networks. Also in MIMO BPL networks, the comparative capacity analysis of noise models shows small differences among them in the 3-88MHz frequency range. Citation :Lazaropoulos, A. G. (2016). Capacity Performance of Overhead Transmission Multiple-Input Multiple-Output Broadband over Power Lines Networks: The Insidious Effect of Noise and the Role of Noise Models. Trends in Renewable Energy, 2(2), 61-82. DOI: 10.17737/tre.2016.2.2.0023
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