DocumentCode
77357
Title
Batched Sparse Codes
Author
Shenghao Yang ; Yeung, Raymond W.
Author_Institution
Inst. for Theor. Comput. Sci., Tsinghua Univ., Beijing, China
Volume
60
Issue
9
fYear
2014
fDate
Sept. 2014
Firstpage
5322
Lastpage
5346
Abstract
Network coding can significantly improve the transmission rate of communication networks with packet loss compared with routing. However, using network coding usually incurs high computational and storage costs in the network devices and terminals. For example, some network coding schemes require the computational and/or storage capacities of an intermediate network node to increase linearly with the number of packets for transmission, making such schemes difficult to be implemented in a router-like device that has only constant computational and storage capacities. In this paper, we introduce batched sparse code (BATS code), which enables a digital fountain approach to resolve the above issue. BATS code is a coding scheme that consists of an outer code and inner code. The outer code is a matrix generation of a fountain code. It works with the inner code that comprises random linear coding at the intermediate network nodes. BATS codes preserve such desirable properties of fountain codes as ratelessness and low encoding/decoding complexity. The computational and storage capacities of the intermediate network nodes required for applying BATS codes are independent of the number of packets for transmission. Almost capacity-achieving BATS code schemes are devised for unicast networks and certain multicast networks. For general multicast networks, under different optimization criteria, guaranteed decoding rates for the destination nodes can be obtained.
Keywords
linear codes; network coding; optimisation; random codes; BATS code; batched sparse codes; communication networks; decoding complexity; destination nodes; digital fountain code; encoding complexity; intermediate network nodes; multicast networks; network coding; optimization criteria; packet loss; random linear coding; unicast networks; Complexity theory; Decoding; Encoding; Generators; Network coding; Packet loss; Vectors; Network coding; erasure network; fountain codes; sparse graph codes;
fLanguage
English
Journal_Title
Information Theory, IEEE Transactions on
Publisher
ieee
ISSN
0018-9448
Type
jour
DOI
10.1109/TIT.2014.2334315
Filename
6847232
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