DocumentCode :
23006
Title :
Capacity Analysis of Linear Operator Channels Over Finite Fields
Author :
Shenghao Yang ; Siu-Wai Ho ; Jin Meng ; En-Hui Yang
Author_Institution :
Inst. for Theor. Comput. Sci., Tsinghua Univ., Beijing, China
Volume :
60
Issue :
8
fYear :
2014
fDate :
Aug. 2014
Firstpage :
4880
Lastpage :
4901
Abstract :
Motivated by communication through a network employing linear network coding, capacities of linear operator channels (LOCs) with arbitrarily distributed transfer matrices over finite fields are studied. Both the Shannon capacity C and the subspace coding capacity CSS are analyzed. By establishing and comparing lower bounds on C and upper bounds on CSS, various necessary conditions and sufficient conditions such that C = CSS are obtained. A new class of LOCs such that C = CSS is identified, which includes LOCs with uniform-given-rank transfer matrices as special cases. It is also demonstrated that CSS is strictly less than C for a broad class of LOCs. In general, an optimal subspace coding scheme is difficult to find because it requires to solve the maximization of a nonconcave function. However, for an LOC with a unique subspace degradation, CSS can be obtained by solving a convex optimization problem over rank distribution. Classes of LOCs with a unique subspace degradation are characterized. Since LOCs with uniform-given-rank transfer matrices have unique subspace degradations, some existing results on LOCs with uniform-given-rank transfer matrices are explained from a more general way.
Keywords :
channel capacity; linear codes; matrix algebra; network coding; optimisation; LOC; Shannon capacity; arbitrarily distributed transfer matrices; capacity analysis; convex optimization problem; finite fields; linear network coding; linear operator channels; maximization; nonconcave function; optimal subspace coding scheme; rank distribution; subspace coding capacity; uniform-given-rank transfer matrices; unique subspace degradation; Channel coding; Degradation; Network coding; Random variables; Symmetric matrices; Vectors; Linear operator channel; network coding; subspace coding;
fLanguage :
English
Journal_Title :
Information Theory, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9448
Type :
jour
DOI :
10.1109/TIT.2014.2326976
Filename :
6822565
Link To Document :
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