paper presentation channel coding and transmission aspects for wireless multimedia

ECE738 Advanced Image Processing

Paper Presentation

Channel Coding and Transmission Aspects for Wireless Multimedia

Authors: Joachim Hagenauer, Thomas Stochhammer

Source: Proceedings of the IEEE , Volume: 87 Issue: 10 , Oct 1999, pp. 1764 -1777

Originally Presented by Hong Hong Chang, Feb 17, 2003

(C) 2005 by Yu Hen Hu 2ECE738 Advanced Image Processing

Overview

• Introduction• System Architecture• The Links between Source and Channel

Coding– RCPC, UEP– PCM Transmission example

• Transmission


Wireless Channel

• Multipath fading• Doppler spreading• Effect of distance• Quite noisy• High BER

– average error rates up to 10%

• Channel coding is necessary

http://www.wireless.per.nl:202/multimed/cdrom97/indoor.htm


Source Coding & Channel Coding (I)

• Shannon’s separation theorem – source coding - long blocks of source symbols– channel coding -a sequence of random block

codes with infinite length– Infinite delay

Source Coding Channel Coding Modulation

transmission

data


Source Coding & Channel Coding (II)

• Shannon’s separation theorem is no longer applicable – short blocks, small delays

• Combined and joint source and channel coding– MPEG II audio layer

• Source-controlled channel decoding– Uses the residual redundancy of the

uncompressed or partly compressed source data to improve channel decoding


Transmissions - Two Kinds of Data Channels

• Mode 1– Error free delivery– Using ARQ– Delay and bit throughput rate (BTR) vary

according to the channel conditions

• Mode 2– Guarantees constant bit rate and delay– Errors occur


System for Transmission of Multimedia Applications over Mobile Channels

S

C

M

A

C

M

A

M

A


Application Properties

• Delay-sensitive applications– Speech, video telephony– Use frequent resynchronization, reduced predictive coding– No ARQ, deep interleaving or long block codes

• BTR-sensitive applications– Audio, video– Use bidirectional predictive coding, long term rate control

algorithms– Might use error protection interleaving, serial or parallel

concatenated coding or ARQ to exploit the provided bandwidth as optimally as possible


Application Properties (Cont)

• BER-sensitive applications– Data– Error-free delivery– Use ARQ, FEC


Multimedia Transmission

• Each application may request different QoS• All application are combined into one single

transmission stream• New layer necessary for multimedia transmission

Adaptation Layer

Multiplex Layer


Adaptation Layer and Multiplex Layer

• Adaptation layer– Adapt the requesting upper application to

transmission condition according to the required QoS

– Have tools for error detection, error correction, bit reordering, retransmission protocols

• Multiplex layer– Multiplex the adaptation layer bit streams or

packets into one single bit steam– Optimizing the throughput, minimize misdeliveries


Transmission Scheme over a Mobile Channel


Links between Source Coding and Channel Coding

• Channel State Information (CSI)– Connected by soft decision of demodulator/detector– Soft decision gains 2-3dB

• Source Significant Information (SSI)– For unequal error protection (UEP)– Rate-compatible punctured convolutional code (RCPC)

• Decision Reliability Information (DRI)– Soft output from channel decoder

• Source a priori/a posteriori information (SAI)– probability of next bit, correlation– Reduce channel decoder error rate


Rate-Compatible Punctured Convolutional Code for Unequal Error Protection

• Start with a rate 1/n0 linear convolutional code• Encode k input bits to produce n0k output bits• Delete n0k−n bits from the output bits• The code rate is

• The corresponding n0k perforation matrix has n ones and n0k−n zeros

0 0

k kn k n k n n

http://www.ee.byu.edu/ee/class/ee685/lectures/lecture37.pdf


Punctured Convolutional Code Example



Puncture Pattern and Perforation Matrix



Rate Compatible Convolutional Code

2/3 2/3



Rate Compatible Punctured Convolutional Code

• A family of punctured codes are rate compatible if the codeword bits from the higher-rate code are embedded in the lower rate codes.

• The zeros in perforation matrices of the lower rate codes are also the zeros in the perforation matrices of the higher rate

• The ones in in perforation matrices of the higher rate codes are also ones in in perforation matrices of the lower rate codes.



RCPC Example



Recursive Systematic Encoder Structure

• Memory M=4 , Mother code rate = ½, Puncturing rate = 8/12 • Nonsystematic vs Systematic

G(D) = (1+D3+D4, 1+D+D2+D4, 1+D2+D3+D4)

Gs(D) =

43

432

43

42

1

1,

1

1,1

DD

DDD

DD

DDD


Error Probability Upper Bound

• df – free distance, the minimum distance of any path from the correct path

• cd – the sum of all information weights on all wrong path of distance d starting inside one puncturing period

• Pd – the pairwise error probability of two code sequences at distance d

ddd db PcP

Pf

1


Puncturing TableRate Table df d df df +1 df +2

8/10 11111111

10010000

00000000

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14

5

138

41

1114

276

8/12 11111111

11010010

00000000

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10

3

81

22

307

74

8/14 11111111

11011110

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1

82

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126

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8/16 11111111

11111111

00000000

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128

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Comparison of systematic recursive convolutional code with nonsystematic

codes


Encoder & Decoder

• Encoder– Puncture – Repeat – replacing “1” by “2” or any higher integer in the

puncturing tables

• Decoder– Punctured bits are stuffed with zeros– Repeated bits are combined by adding soft values

• Header of frame contains the coding rate information of payload

• Easily adapted to multimedia and channel requirements via puncturing control


BER Performance of Systematic Recursive PCPC code


Soft-In/Soft-Out Decoding• Decoding algorithm

– Viterbi (VA) – Maximum-a-posteriori-probability-symbol-by-symbol (MAP)

• VA and MAP can accept soft values– Source a priori information– Channel state information

• VA and MAP can deliver soft outputs


PCM Transmission example - EEP• Analog source• Source coding: m-bit linear quantization (m=20)

– Quantized sample

– smaller k -> more important.

• Transmission distortion

• equal Pb for all k=1,2,…,m

]1;1[ v

1,1,21

k

m

k

kkQ xxv

mmbQe

sPCM P

SNR2222

2

2214

1

m

k bkkm

k kke kPxxE1

22

1

2 )(242)ˆ(


PCM Transmission Example – Applying Soft Bits

• CSI is transformed to a DRI and directly passed to the source decoder. Thus, λ(x) (soft value) is obtained

• Reconstructed PCM value

• Gain of about 1.6dB in SNRPCM

1,1)(,2)(ˆ1

k

m

k

kk xxv


PCM Transmission Example – Apply Channel Coding

• m=10– m is smaller, quantization noise increases

• Channel coding rate = ½– RCPSRC 8/16– Improves total performance


PCM Transmission Example – UEP

• Let all bits contribute the same transmission distortion. Then,

– Small k, small Pb

– Use this information for unequal error-protection design

• Require that transmission distortion of each bit is smaller than quantization distortion. We have

kb kP22

1)(

)(2212

1)( km

b kP


PCM Transmission Example: RCPSRC code for UEP

• Employ – the upper bound for the bit error probability– Distance spectra of puncture table

• Obtain a certain rate R(k) for each bit class at different channel SNR

• Rate distribution for PCM Transmission


PCM Transmission Example- Simulation Results


Approaches to Improve the Transmission of Multimedia

I. Error Resilient Source Coding

• Fixed length coding– more stable against channel error

• MPEG-4 error resilient mode– Space the Resync markers evenly throughout the

bit stream– All predictively encoded information is confined

within one video packet to prevent the propagation of errors


II. Improved Receiver Algorithms

• European Digital Satellite TV-Broadcasting standard– MPEG-2 based source coding– Concatenated coding scheme – Error-concealment techniques based on temporal, spatial,

frequency

• Joint-source channel coding– Instead of remove residual redundancy by using VLC, keep it

and use it at the receiver side to achieve more reliable decoding

• Soft source decoding


III. Source Adapted UEP

• RCPC• Application to GSM speech

– Turbo Code– Channel coding is applied according to the bit

sensitivity

• Application to hierarchical video broadcast– Base layer and enhancement layer


IV. Channel Adapted Combined Source-Channel Coding Methods

• Goal– Allocate bit rates in an optimal way between

source and channel encoders as the source and channel vary

– Minimize end-to-end distortion

• Feed back the CSI from the decoder to the encoder on a reverse channel

paper presentation channel coding and transmission aspects for wireless multimedia

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