bats: network coding in action - istc2018.org · bats: network coding in action raymond w. yeung...

BATS: Network Coding in Action

Raymond W. Yeung

Institute of Network CodingThe Chinese University of Hong Kong

December 1, 2018

Joint work with Shenghao Yang, Hoover Yin, Tsz-Ching Ng, et al.

R. W. Yeung (CUHK) BATS December 1, 2018 1 / 31

Outline

1 File Transmission in Networks with Packet Loss

2 Random Linear Network Coding

3 BATS Codes

4 BATS Protocol and Implementations

5 Smart Cities Applications


Networks with Packet Loss (Erasure Networks)

One 20MB file ⇡ 20,000 packets

A practical solution

low computational and storagecosts

high transmission rate

small protocol overhead

x1 x2 · · · xK

s

t1 t2


Smart City

http://www.monitormymeter.com/lighting-automation.html


http://www.monitormymeter.com/lighting-automation.html

Vehicular ad-hoc Network


Balloon-powered Internet

http://www.theneweconomy.com/insight/googles-project-loon-explained


http://www.theneweconomy.com/insight/googles-project-loon-explained

Low-orbit Satellite Internet

http://www.cs.ucsb.edu/~almeroth/classes/S99.290I/Satellite.html


http://www.cs.ucsb.edu/~almeroth/classes/S99.290I/Satellite.html

Underwater Acoustic Network


Power-line Communication Network


Why Loss and Relay are Inevitable

WLAN has more and more interference in both 2.4GHz and 5GHzHigher loss due to interference

Higher frequency in millimeter wave spectrum e.g. 60 GHz to beadopted in 802.11AD and 5G

Higher loss due to obstaclesRelay for non-line-of-sight transmission

Low power transmission in IoTHigher loss due to low S/N ratioRelay for long distance transmission

Underwater acoustic communicationHigh loss due to path loss, noise, multi-path, Doppler spreadRelay for long distance transmission


Line Networks of n Hops

s r1 r2 · · ·

rn�1

t

All links have a packet loss rate 0.2.

Intermediate Operation Maximum Rate

forwarding 0.8n ! 0network coding 0.8

In practice, it is very di�cult to build a line network with morethan 5 or 6 hops – the “multi-hop curse”


Network Coding is a Necessity

Link-by-link retransmissionCache O(

pK) packets at intermediate nodes.

Feedbacks may not be reliable or available.Far from optimal for multicast.

Link-by-link erasure coding (fountain coding)Cache O(K) packets at intermediate nodes.High computation costs at intermediate nodes and/or destinationnodes.Far from optimal for multicast.


Outline



3 BATS Codes




Multicast Capacity of Erasure Networks

Random linear network codes achieve the capacity of a large range ofmulticast erasure networks.

However, the complexity issues prevent the real-world implementationof the baseline RLNC scheme.

[Wu06] Y. Wu, “A trellis connectivity analysis of random linear network coding with bu↵ering,” in Proc. IEEE ISIT 06, Seattle,USA, Jul. 2006.

LMKE08] D. S. Lun, M. Medard, R. Koetter, and M. E↵ros, “On coding for reliable communication over packet networks,”Physical Communication, vol. 1, no. 1, pp. 320, 2008.


Complexity of Linear Network Coding

CV overhead: K/T .

Encoding: O(TK) per packet.

Decoding: O(K2 + TK) per packet.

Network coding: O(TK) per packet. Bu↵er K packets.

encoding

network coding

K: number of packets. T : packet length.R. W. Yeung (CUHK) BATS December 1, 2018 15 / 31

Outline



3 BATS Codes




BATched Sparse (BATS) Codes

outer code

inner code(network code)

[YY11] S. Yang and R. W. Yeung. Coding for a network coded fountain. ISIT 2011, Saint Petersburg, Russia, 2011.

[YY14] S. Yang and R. W. Yeung. Batched sparse codes. Information Theory, IEEE Transactions on, vol. 60, no. 9, pp.5322-5346, Sep. 2014.

[YY17] S. Yang and R. W. Yeung. BATS Codes: Theory and Practice, in Synthesis Lectures on Communication Networks,Series Editor: R. Srikant. Morgan & Claypool Publishers, 2017.


BATS Codes Theory and Practice

Shenghao YangRaymond W. Yeung

Series ISSN: 1939-4608

Synthesis Lectures onCommunication Networks

Series Editor: R. Srikant, University of Illinois at Urbana-Champaign

Analytical Methods for Network Congestion ControlShenghao Yang, The Chinese University of Hong Kong, ShenzhenRaymond W. Yeung, The Chinese University of Hong KongThis book discusses an efficient random linear network coding scheme, called BATched Sparse code, or BATS code, which is proposed for communication through multi-hop networks with packet loss. Multi-hop wireless networks have applications in the Internet of Things (IoT), space, and underwater network communications, where the packet loss rate per network link is high, and feedbacks have long delays and are unreliable. Traditional schemes like retransmission and fountain codes are not sufficient to resolve the packet loss so that the existing communication solutions for multi-hop wireless networks have either long delay or low throughput when the network length is longer than a few hops. These issues can be resolved by employing network coding in the network, but the high computational and storage costs of such schemes prohibit their implementation in many devices, in particular, IoT devices that typically have low computational power and very limited storage. A BATS code consists of an outer code and an inner code. As a matrix generalization of a fountain code, the outer code generates a potentially unlimited number of batches, each of which consists of a certain number (called the batch size) of coded packets. The inner code comprises (random) linear network coding at the intermediate network nodes, which is applied on packets belonging to the same batch. When the batch size is 1, the outer code reduces to an LT code (or Raptor code if precode is applied), and network coding of the batches reduces to packet forwarding. BATS codes preserve the salient features of fountain codes, in particular, their rateless property and low encoding/decoding complexity. BATS codes also achieve the throughput gain of random linear network coding. This book focuses on the fundamental features and performance analysis of BATS codes, and includes some guidelines and examples on how to design a network protocol using BATS codes.

Synthesis Lectures onCommunication Networks

store.morganclaypool.comR. Srikant, Series Editor

YANG • YEUNG

BATS CODES: THEORY AND PRACTICE

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CLAY

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About SYNTHESIS

This volume is a printed version of a work that appears in the Synthesis Digital Library of Engineering and Computer Science. Synthesis books provide concise, original presentations of important research and development topics, published quickly, in digital and print formats.


Encoding of BATS Code: Outer Code

Apply a “matrix fountain code” at the source node:1 Obtain a degree d by sampling a degree distribution .2 Pick d distinct input packets randomly.3 Generate a batch of M coded packets using the d packets.

Transmit the batches sequentially.

b1 b2 b3 b4 b5 b6

· · · · · ·

X1 X2 X3 X4

Xi =⇥bi1 bi2 · · · bidi

⇤Gi = BiGi.


Encoding of BATS Code: Inner Code

The batches traverse the network.

Encoding at the intermediate nodes forms the inner code.

Linear network coding is applied in a causal manner within a batch.

snetwork with linearnetwork coding

t

· · · , X3, X2, X1 · · · , Y3, Y2, Y1

Yi = XiHi, i = 1, 2, . . ..


Belief Propagation Decoding

1 Find a check node i with degreei = rank(GiHi).

2 Decode the ith batch.

3 Update the decoding graph. Repeat 1).

b1 b2 b3 b4 b5 b6

G1H1 G2H2 G3H3 G4H4 G5H5

The linear equation associated with a check node: Yi = BiGiHi.


Precoding

Precoding by a fixed-rate erasure correction code.

The BATS code recovers (1� ⌘) of its input packets.

Precode

BATS code

[Shokr06] A. Shokrollahi, Raptor codes, IEEE Trans. Inform. Theory, vol. 52, no. 6, pp. 25512567, Jun. 2006.


Complexity

Coe�cient vector overhead M/TSource node encoding O(MT ) per packet

Destination node decoding O(M2 +MT ) per packet

Intermediate Nodebu↵er O(MT )

network coding O(MT ) per packet

T : length of a packet

K: number of packets

M : batch size, M ⌧ K,T .


Achievable Rates for Line Networks: Up to 50 Hops

0 10 20 30 40 500

0.2

0.4

0.6

0.8

network length (l)

R⇤forsystem

atic

recoding

qm = 28, M = 64

qm = 28, M = 32

qm = 28, M = 16

qm = 28, M = 8

qm = 28, M = 4

qm = 28, M = 2

qm = 28, M = 1



0 10 20 30 40 500

0.2

0.4

0.6

0.8

network length (l)

R⇤forsystem

atic

recoding

qm = 28, M = 64

qm = 28, M = 32

qm = 28, M = 16

qm = 28, M = 8

qm = 28, M = 4

qm = 28, M = 2

qm = 28, M = 1

qm = 2, M = 64

qm = 2, M = 32

qm = 2, M = 16

qm = 2, M = 8

qm = 2, M = 4

qm = 2, M = 2

qm = 2, M = 1



0 200 400 600 800 1,0000

0.2

0.4

0.6

0.8

network length (l)

R⇤forsystem

atic

recoding

qm = 28, M = 64

qm = 28, M = 32

qm = 28, M = 16

qm = 28, M = 8

qm = 28, M = 4

qm = 28, M = 2

qm = 28, M = 1



0 200 400 600 800 1,0000

0.2

0.4

0.6

0.8

network length (l)

R⇤forsystem

atic

recoding

qm = 28, M = 64

qm = 28, M = 32

qm = 28, M = 16

qm = 28, M = 8

qm = 28, M = 4

qm = 28, M = 2

qm = 28, M = 1

qm = 2, M = 64

qm = 2, M = 32

qm = 2, M = 16

qm = 2, M = 8

qm = 2, M = 4

qm = 2, M = 2

qm = 2, M = 1


Outline



3 BATS Codes




A Demo for Video Transmission

Video streaming between 2 PC’s through 10 Raspberry Pi 3

11 wireless hops with significant packet loss due to interference


Outline



3 BATS Codes




Smart Lampposts• Key infrastructure of smart cities• Equipped with networking interfaces, cameras and

sensors• Promote smart city innovations on a city scale– intelligent transportation– autonomous driving– real-time surveillance– high-speed WiFi coverage

• Estimated over 70 million smart lampposts will be installed worldwide by 2027

• Creating a global market of USD 8.3 billion

2

Smart Lamppost Connectivity

• Smart lampposts must be connected to the Internet backbone

• Possible technologies– optical fiber– 4G– BATS

3

Optical Fiber• Pros– very high data rate– highly reliable

• Cons– high installation cost– very long setup time– very disrupting process– sometimes not possible

• Realistically only a small number of lampposts can be connected by optical fiber

• The rest still need to be connected to the Internet

4

How about 4G?

• A 4G card is installed at each lamppost• Pros– easy to deploy– relatively inexpensive

• Cons– high recurrent cost– bandwidth drops drastically during rush hours

5

opticalfiber

The Multi-hop Solution

Advantages of BATS

high throughput

low latency

low coding complexity

low storage requirement

þ

þ

þ

þ

10

Comparison with 4G

12

Low installation cost

Easy to deploy

Low recurrent cost

Guaranteed bandwidth

Reach rural areas

4G ✓ ✓BATS ✓ ✓ ✓ ✓ ✓

Summary

BATS breaks the multi-hop curse in wireless networks

Essentially converts a multi-hop wireless network into a single-hopwireless network

An enabling communication technology for wireless mesh networks,V2X, IoT, smart cities, and beyond


Reference

[ACLY00] R. Ahlswede, N. Cai, S.-Y. R. Li, and R. W. Yeung. Network information flow. IEEE Trans. Inform. Theory,46(4):1204-1216, July 2000.

[CHKS09] M.-L. Champel, K. Huguenin, A.-M. Kermarrec, and N. L. Scouarnec. LT network codes. Research Report RR-7035,INRIA, 2009.

[CWJ03] P. A. Chou, Y. Wu, and K. Jain. Practical network coding. In Proc. Allerton Conf. Comm., Control, and Computing,Oct. 2003.

[Dana06] A. F. Dana, R. Gowaikar, R. Palanki, B. Hassibi, and M. E↵ros, Capacity of wireless erasure networks, IEEE Trans.Inform. Theory, vol. 52, no. 3, pp. 789804, 2006.

[GS08] R. Gummadi and R. Sreenivas. Relaying a fountain code across multiple nodes. In Proc. IEEE ITW 08, May 2008.

[HB10] A. Heidarzadeh and A. H. Banihashemi. Overlapped chunked network coding. In Proc. ITW 10, pages 1–5, 2010.

[Ho03] T. Ho, B. Leong, M. Medard, R. Koetter, Y. Chang, and M. E↵ros, The benefits of coding over routing in a randomizedsetting, in Proc. IEEE ISIT 03, Jun. 2003.

[LMKE08] D. S. Lun, M. Medard, R. Koetter, and M. E↵ros, “On coding for reliable communication over packet networks,”Physical Communication, vol. 1, no. 1, pp. 320, 2008.

LSS11] Y. Li, E. Soljanin, and P. Spasojevic, E↵ects of the generation size and overlap on throughput and complexity inrandomized linear network coding, IEEE Trans. Inform. Theory, vol. 57, no. 2, pp. 1111–1123, Feb. 2011.

[LYC03] S.-Y. R. Li, R. W. Yeung, and N. Cai. Linear network coding. IEEE Trans. Inform. Theory, 49(2):371381, February2003.

[MAB12] K. Mahdaviani, M. Ardakani, H. Bagheri and C. Tellambura, “Gamma codes: a low-overhead linear-complexity networkcoding solution,” NetCod 2012.

[MHL06] P. Maymounkov, N. J. A. Harvey, and D. S. Lun. Methods for e�cient network coding. In Proc. Allerton Conf. Comm.,Control, and Computing, Sept. 2006.

[PFS05] P. Pakzad, C. Fragouli, and A. Shokrollahi. Coding schemes for line networks. In Proc. IEEE ISIT 05, 2005.

[SZK09] D. Silva, W. Zeng, and F. R. Kschischang. Sparse network coding with overlapping classes. In Proc. NetCod 09, pages74–79, 2009.

[TF11] N. Thomos and P. Frossard, Degree distribution optimization in Raptor network coding, in Proc. IEEE ISIT 11, Aug.2011.

[Wu06] Y. Wu, “A trellis connectivity analysis of random linear network coding with bu↵ering,” in Proc. IEEE ISIT 06, Seattle,USA, Jul. 2006.


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