Packet Error Rate

7/31/2019 Packet Error Rate

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Video Communication over Wired

and Wireless CDMA Networkswith Optimal Bandwidth Allocation

Yushi Shen

Advisors: Prof. Pamela C. Cosman

Prof. Laurence B. Milstein

2005 June 20th University Qualifying Exam 1



Introduction• Area: Video communications

• Channel model:Tandem wired and wireless CDMA networks– Wired component: Packet erasures

– Wireless component: Burst errors• Direct-sequence (DS) CDMA technology

Three components:

– Source Coding– Channel Coding– Spread Spectrum




Introduction




Introduction• Problem:

Bandwidth allocation among source coding, channelcoding and spreading, with a target frame rate, under thetotal bandwidth (chip rate) constraint:

R s: source bit rate (bit/s); r

c: channel coding rate;

M : spreading gain; W : total bandwidth (chip rate, chip/s).

• Adaptive allocation: – Changing channel characteristics

– Different source content




Talk Outline Introduction

2. Computation of Packet Error Rate3. Tradeoff between Channel Coding and

Spreading

4. Source Encoding with Optimal Mode

Selection

5. Optimal Bandwidth Allocation6. Simulation Results

7. Future Work




Computation of Packet Error RatePacket Error Rate (P):

• The key factor for the performance

• Determined by:

– Packet erasure rate introduced by wired link: Pe

– Packet drop rate due to uncorrectable bit errors by

wireless link: P p

• Total packet error rate:

P = Pe + P p - Pe x P p




Computation of Packet Error RatePacket Drop Rate due to Wireless Link (P p):

– y : fixed packet length

– Ad : weight distribution of block codes

– : received SNIR (signal-to-noise-plus-interference-ratio)

r γ




Computation of Packet Error RateComputation of Weight Distribution ( Ad ):

• Computed based on the transition matrix sequence

• Computed

– For each RCPC code (characterized by rate r c)

– Given fixed packet length ( y)




Computation of Packet Error Rate

Computation of received SNIR ( ):

=

– : channel gain;

K : # of users; L: # of resolvable paths;

E b: energy per source bit; N 0: energy of Gaussian noise;

: exponential multipath intensity profile

– Result for sufficiently large number of users

• Multi-access-interference is asymptotically Gaussian

• Self-interference is negligible


(forward)

α

λ



Computation of Packet Error RatePacket Drop Rate due to Wireless Link (P p):

– Weight distribution ( Ad ), which is determined by

• Structure of RCPC code (characterized by r c)

• Fixed packet length ( y)

– Received SNIR ( ), which is determined by

• System parameters (K , L, E b, N 0)

• Current channel conditions ( )

• Channel coding rate (r c ) and spreading gain ( M )

α




Computation of Packet Error RateConclusion: Given system parameters, and conditioned on

channel characteristics, packet error rate is fully determinedby channel coding rate (r c ) and spreading gain ( M ). (forward)

X axis: Received SNIR

(dB)

Y axis: Probability of packet error

Rate 1/3 RCPC code

Rate 2/3 RCPC codeRate 8/9 RCPC code

No FEC used






3. Tradeoff between Channel Coding and

Spreading


Selection


7. Future Work




Tradeoff between Channel Coding and Spreading,

under a Target Packet Error Rate

• r c and M the packet error rate, given the system

parameters and conditioned on the channelcharacteristics.

• r c and M the source bit rate ( Rs), under the

bandwidth constraint.

• Basic idea:

– Choose the (r c , M ) for each packet that maximizes Rs, among all the (r c , M ) combinations that achievethe target packet error rate.






Algorithm:

• Pre-calculate the relationship between packet error andreceived SNIR, and solve the SNIR threshold under the

target packet error rate, for each RCPC code. (example)

• During transmission

– Estimate the current channel characteristics;

– For each r c , calculate the smallest M that achieves the

SNIR threshold for the target, and get a (r c , M ) pair;

– Among all the (r c , M ) pairs, choose the one that

minimizes the ( M / r c) ratio to maximize Rs.






It can be shown the encoder will actually select the

same r c , and only vary M , most of the time.• For each r c , denote the SNIR threshold for thetarget packet error rate

• Solve M in the SNIR equation and plug in ( M / r c): (see)


• To minimize ( M/ r c ) is equivalent to maximize

• RCPC code with lowest rate has the highest ratio

≈cr

M





Conclusion:

• The tradeoff between channel coding andspreading:

– Achieve the target packet error rate

– Maximize the source bit rate

– Satisfy the bandwidth constraint

• Under the scenario of larger number of users(self-interference becomes negligible), FEC ismore important than spreading






Tradeoff between Channel Coding and

Spreading


Selection


7. Future Work






Source Encoding: Intra-Coding Mode

CurrentFrame

PreviousFrame

INTRA Coding

Macro

Block




Source Encoding: Inter vs. Intra Coding


• Inter-Mode coding:

– High compression efficiency – Propagates past errors

– More suitable for good channel conditions

• Intra-Mode coding: – Stops error propagation

– Costly in bits in general

– More suitable for bad channel conditions

• It is desired to switch between two modes according

to channel conditions and video content



Fixed-length Packet• Fixed-length packet

• Re-sync per packet• A MB is reconstructable at the decoder if:

– All packets containing this MB are received

• Illustration:






Rate-Distortion Optimization• Every MB is tentatively encoded using both

intra/inter mode and 31 quantization steps• For each possibility, distortion ( D MB) and rate

usage ( R MB), thus the chip used for the MB (W MB),

are calculated

• To minimize D MB under the constrained of frame

rate target. This is given by the Lagrangian




Source Encoding: Algorithm Summary• Computation of expected distortion at the encoder

is recursive on a per-pixel basis

• Uses the estimated distortion within a rate-

distortion framework for optimal mode-switching

• Optimal mode selection algorithm

– Given the packet error rate – Given the target frame rate







Spreading

Source Encoding with Optimal ModeSelection


7. Future Work




Optimal Bandwidth Allocation• Problem:

Allocation bandwidth among Rs, r c and M , tooptimize the overall performance, under:

– Bandwidth constraint

– Frame rate target (transmission time)

• Effects:

– Changing channel characteristics

– Different source content




Optimal Bandwidth AllocationAlgorithm:

• Predetermine a set of target packet error rates

• For each packet: 2-step tradeoff (1) For each target packet error rate

Trade off channel coding and spreading, to maximize

the source bit rate ( Rs) and achieve the target packet errorrate (P p).

If no (r c, M ) pair can achieve a certain P p, skip this P p.

If none of the packet error rates can be achieved, reportdeep fading and temporarily send nothing (except pilotbits for channel estimation).

Result: a set of (P p , Rs , r c , M ) 4-tuples




Optimal Bandwidth AllocationAlgorithm (cont.):

• For each packet: 2-step tradeoff

(2) Trade off source coding rate and packet error rate

For each (P p , Rs , r c , M ) 4-tuple, using the video

encoder encodes the current content ( MB by MB),

until the encoded bits fill the fixed length packet.

Choose the tuple that optimizes the performance:

minimizing the expected distortion per time unit.




Optimal Bandwidth AllocationAlgorithm (cont.):

• Illustration of a typical packetization after source encoding

• Select the 4-tuple that minimizes the distortion per time

unit, which is approximately determined by




Optimal Bandwidth Allocation• Computation complexity:

Let N 1 denote the number of candidate packet error rate, N 2 the number of MBs per packet, the encoder needs to

tentatively encode ( N 1x N 2 ) MBs for each packet

• Choices of P p candidates:

– Too large (P p >5%): error rate goes up quickly to unity

– Too small (P p

<0.1%): under the same Rs

, performance

gain diminishes dramatically

– Our Choices of P p : [0.2%, 0.6%, 1%, 1.5%, 3%]




Optimal Bandwidth AllocationSummary:

Optimal bandwidth allocation among source coding,channel coding and spreading for video

communications over tandem channels

– Adaptively at the packet level

– Incorporates the effect of changing channel

conditions– Incorporates the effect of current video content







Spreading


Optimal Bandwidth Allocation

6. Simulation Results

7. Future Work







Simulation Result: PSNR vs. Chip RatePSNR performance versus target transmission chip rate,

Carphone QCIF, and E b /N 0 =4dB.

X axis: Chip Rate W

(Mcps)

Y axis: PSNR (dB)Optimal bandwidth

allocation

Allocation with fixed

target P p=1%

Allocation with

P p=1% and a fixed M =15




Simulation Result: PSNR vs. Eb /N0PSNR performance versus Eb /N0, Carphone QCIF, chip rate

15Mcps.

X axis: Eb /N0 (dB)

Y axis: PSNR (dB)

Optimal bandwidth

allocation

Allocation with fixed

target P p=1%

Allocation with

P p=1% and a fixed M =15




Simulation Result: Packet Error Rate UsagePercentage of the total packets that employ the corresponding

target drop rate as optimal bandwidth allocation, chip rate 15

Mcps, and E b /N 0 =4dB.

X axis: Target Packet

Error Rate Pp (%)Y axis: Percentage (%)

Carphone QCIF

(high motion)

Mother and

daughter QCIF (low

motion)






Spreading


Optimal Bandwidth Allocation

Simulation Results

7. Future Work




Future Work • Channel Estimation, and System Sensitivity to

Estimation Error• Video Communications over Multi-Carrier (MC)

CDMA

– Spreading data stream in the freq domain over multiple

subcarriers

– Subcarriers convey the same information– Better freq diversity to combat selective fading

comparing to signal carrier




Acknowledgements




Appendix: Weight Distribution• Punctured Convolutional Codes:

– Mother convolutional code

– Puncturing table

• Rate-Compatible Punctured Convolutional (RCPC) Codes:

– The higher rate code is embedded in the lower rate codes




Appendix: Weight Distribution (Cont.)• Transition Matrix of Convolutional Codes (C. C.)

Example:

• Transition Matrix Sequence of Punctured C. C.Example: for puncture table a(1):

where




Appendix: Weight Distribution (Cont.)• Product Matrix

Φ=– y is the fixed packet length

• Weight Distribution for Block Codes

– Coming from the corresponding elements of Φ

– Example: Zero-tail method, T(x) is given byΦ1,1

∏=

y

ii A

1




Appendix: Distortion per Time UnitDenominator: transmission

time of the packet (exactly)

Numerator: distortion of the

packet (approximate due to

the tails)– DCT transform

– Negligible, because tails are

much smaller than y

s

c

R

y

W

M r

y

=




Appendix: System with Fixed Spreading GainSpreading gain ( M ) is fixed for all packets

Trade off between source coding and channel codingAlgorithm:

• Pre-calculate the relationship between packet error and

received SNIR, and solve the SNIR threshold under the

target packet error rate, for each RCPC code

• Under a target packet error rate (say 1%), use the RCPC

code with highest coding rate (thus maximize Rs) among

those achieve the target error rate.


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Packet Error Rate