Multicast Convolutional Network Codes via Local Encoding Kernels | Zendy

Morteza Rekab-Eslami | Zendy; Morteza Esmaeili | Zendy; Thomas Aaron Gulliver | Zendy

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Multicast Convolutional Network Codes via Local Encoding Kernels

Author(s) -

Morteza Rekab-Eslami,

Morteza Esmaeili,

Thomas Aaron Gulliver

Publication year - 2017

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2017.2689781

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

A convolutional network (CN) code can be described by either global encoding kernels (GEKs) or local encoding kernels (LEKs). In the literature, the multicast property of a CN code is described using GEKs, so the design algorithms for multicast CN codes employ GEKs to check this property. For cyclic networks, using GEKs makes the design algorithms time-consuming. In this paper, a new approach is proposed for the design of multicast CN codes for networks with cycles. First, a formula is presented to describe the multicast property using LEKs rather than GEKs. Then, this formula is used to develop a design algorithm for multicast CN codes. This algorithm does not use GEKs, which makes it more efficient than GEK-based algorithms, particularly for large cyclic networks.

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