Batched Sparse Codes

TL;DR

BATS codes introduce a scalable, efficient network coding scheme combining fountain codes and random linear coding, reducing computational and storage costs while achieving near-capacity transmission in various network topologies.

Contribution

The paper presents BATS codes, a novel coding scheme that maintains low complexity and independent storage requirements at network nodes, enabling practical implementation in resource-constrained devices.

Findings

Almost capacity-achieving schemes for various network types

Reduced computational and storage costs at network nodes

Guaranteed decoding rates under different optimization criteria

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 an 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, two-way relay networks, tree networks, a class of three-layer networks, and the butterfly network. For general networks, under different optimization criteria, guaranteed decoding rates for the receiving nodes can be obtained.