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      The Impact of Noise Models on Capacity Performance of Distribution Broadband over Power Lines Networks

      Journal of Computer Networks and Communications
      Hindawi Limited

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          Abstract

          This paper considers broadband potential of distribution Broadband over Power Lines (BPL) networks when different well-known noise models of the BPL literature are applied. The contribution of this paper is twofold. First, the seven most representative and used noise models of the BPL literature are synopsized in this paper. With reference to this set, the broadband performance of a great number of distribution BPL topologies either Overhead (OV) or Underground (UN), either Medium-Voltage (MV) or Low-Voltage (LV), is investigated in terms of suitable capacity metrics. Second, based on the proposed capacity metrics, a comparative capacity analysis is performed among various well-validated noise models. Through the careful study of its results, it is demonstrated that during capacity computations of distribution BPL networks, the flat Additive White Gaussian Noise (FL noise model) can be comfortably assumed as an efficient noise model either in 3–30 MHz or in 3–88 MHz frequency range since its capacity differences with the other well-proven noise models are negligible.

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          For the Grid and Through the Grid: The Role of Power Line Communications in the Smart Grid

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            Analysis and modeling of impulsive noise in broad-band powerline communications

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              Modeling and Analysis of Noise Effects on Broadband Power-Line Communications

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                Author and article information

                Journal
                Journal of Computer Networks and Communications
                Journal of Computer Networks and Communications
                Hindawi Limited
                2090-7141
                2090-715X
                2016
                2016
                : 2016
                :
                : 1-14
                Article
                10.1155/2016/5680850
                ea64089e-c9df-4ab4-9ce9-777d2c3c5f68
                © 2016

                http://creativecommons.org/licenses/by/4.0/

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