Initialization Algorithms for Convolutional Network Coding

Abstract

We present algorithms for initializing a convolutional network coding scheme in networks that may contain cycles. An initialization process is needed if the network is unknown or if local encoding kernels are chosen randomly. During the initialization process every source node transmits basis vectors and every sink node measures the impulse response of the network. The impulse response is then used to find a relationship between the transmitted and the received symbols, which is needed for a decoding algorithm and to find the set of all achievable rates.Unlike acyclic networks, for which it is enough to transmit basis vectors one after another, the initialization of cyclic networks is more involved, as pilot symbols interfere with each other and the impulse response is of infinite duration.

A Straight Construction Algorithm for a Delay Invariant Convolutional Network Coding on Cyclic Networks

“…Initially, [11][12][13]. Note that when we process the nodes in P 1 , the matrix I − K is upper triangular and on its diagonal all elements are equal to 1, then det(I − K) = 1 0.…”

The Network Coding Algorithm Based on Rate Selection for Device-to-Device Communications

“…For example, Content-and Loss-Aware IDNC (Instantly Decodable Network Coding) [19] is proposed to minimize the completion time under the quality constraint by selecting the maximal weighted clique, the TS-MIS (Two-Stage Maximal Independent Set) [20] selection algorithm is proposed to efficiently reduce the mean video distortion by selecting the maximal independent set. There are two main network coding schemes: one allows the combination of all source packets using random coefficients, i.e., linear network coding [23], random network coding [24], [25], convolutional network coding [26]; the other detects coding opportunities and exploits them to combine the appropriate packets, i.e., COPE. In this paper, we are interested in the latter.…”