Background ! Power control is essential to CDMA wireless networks - - PDF document

SLIDE 1

Minimizing Power Consumption of Source Encoding and Radio Transmission in CDMA Systems

Xiaoan Lu

Department of Electrical and Computer Engineering Polytechnic University, Brooklyn, NY

2

Background

! Power control is essential to CDMA wireless networks

" Relax the near far problem. " Improve the quality of service. " Increase the channel capacity. " Increase the battery life of the mobile terminal.

! Power control as an optimization problem

" Minimize the total transmission power " maintain a required SINR threshold

SINR: the signal to interference and noise ratio, depends on transmission power of all users.

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3

" Constraint: PSNR or distortion (MSE), not SINR

! Lossy compression Ds ! Erroneous transmission Dt (Ds+ Dt= D0) ! PSNR: peak signal-to-noise ratio, MSE: mean squared error

Motivation (2/ 1)

! Previous power control focused on voice signal ! Video signal is integrated into the new generation wireless communication system

Ds Bit rate Rs1Rs2 Ds1 Ds2 (a) Distortion(compression) (b) Distortion(transmission) (c) (d) Power bit rate x SI NR

∝

4

" Minimize: transmission power + signal processing power " Parameters: { bit rate, compression complexity} , { transmission power} " Constraint: PSNR or distortion (MSE), not SINR

Motivation (2/ 1)

Power bit rate x SI NR

∝

! Previous power control focused on voice signal ! Video signal is integrated into the new generation wireless communication system

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Motivation (2/ 2)

! Proposed solution: Dynamically Reconfigurable Energy Aware Multimedia I nformation Terminal (DREAM-IT)

" Adapting operating parameters of all components simultaneously and dynamically to minimize total power consumption

! This subproject focuses on power allocation between video source coding, and radio transmission

" Parameter:

i t i i s

P R

, , ,

power

• n

transmissi complexity n compressio , rate bit β

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transmitter baseband signal processing terminal 1 base station transmitter baseband signal processing terminal N ......

Adapt to minimize ,subject to

∑

=

+ =

N i i t i s tot

P P P

1 , ,

) (

, , ,

) , , (

i i i i s i tot

D R D = γ β

{ }

i t i i s i

P R c

, , ,

, β = ! the uplink of a CDMA cell ! video transmission

System description

1 t

P

tN

P SINR : voice γ

P

D0

∑

= N i i t

P

1 ,

Minimize subject to γ γ ≥

i

∑

=

+

N i i s i t

P P

1 , ,

) (

Minimize subject to

, , i i tot

D D ≤

power

• n

transmissi complexity n compressio : rate, bit :

: , , , i t i i s

P R β

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7

One terminal

pL(γ) : packet error rate Channel encoder Pt Dt(β,pL): (1) transmission error (2) Error propagation Transmission power Pt Transmitter Ps Ds(Rs, β): lossy compression Bit rate Rs Complexity β Video compressor Power Distortion Parameters Component

Minimize subject to

∑

=

+

N i i s i t

P P

1 , ,

) (

, , , , i i t i s i tot

D D D D ≤ + =

Decoded

γ: signal to interference and noise ratio, SINR, depends on transmission power of all users.

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power bit rate signal processing

Conceptual illustration

total

! Special case example: , fixed individually, one user

" Signal compression Bit rate , power " Transmission Bit rate , power " Total Bit rate , power ?

s

D

t

D

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! Separate optimization

" Operating parameters for terminal , is decided by base station + user

Why optimize jointly?

c1 c2 c3

          =

i t i i s i

P R c

, ,

power

• n

transmissi complexity n compressio rate bit β

i

i

i

c

! One user’s signal is other users’ interference " All users interact with others " Local minima may not be global optimum ! Optimize jointly: Base station + all terminals " Full search: good for a small number of users " Iterative algorithm: converge? " Our approach:

! Simplified models + Lagarangian method ! Two-step fast algorithm c1 c2 c3

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Simplified models

! Power consumption

" Distortion= f(compression, transmission)

! From compression ! Total distortion

" Source compression power

! Increase linearly with complexity

" Transform coding: transform block size " H.263 encoder(periodic INTRA update, full ME search): INTER rate

! Independent of bit rate

i s

P ,

i

β

i s

R ,

) ( ) (

, , , , i s i s R s i s

D R D D β

β

=

[ ]

2 , , , , , , ,

) , ( 1 ) , , (

i s i L i s i i s i L i i i s i tot

p R D p R D σ β γ β + − =

Adapt to minimize subject to

∑

=

+ =

N i i t i s tot

P P P

1 , ,

) (

,

D D

i tot =

{ }

i t i i s i

P R c

, , ,

, β =

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Method

! Lagarangian Multiplier method ! Equations: , , , ! Unknowns:

{ }

∑

=      

− + =

N i i i i s i tot i i i s i tot i s

D R D R P c J

1 , , , , , ,

) , , ( ) , , (

t t

P P β λ β

i i t i i s

P R λ β , , ,

, ,

,

= ∂ ∂

i s

R J = ∂ ∂

i

J β

,

= ∂ ∂

i t

P J

, , ,

) , , (

i i i i s i tot

D R D = γ β

! is used to re-parameterize the equations

) ,..., (

* * 1 * N

γ γ = Γ

,

D D

i tot = i i

γ λ ~

i i s

R γ ~

, i i

γ β ~

i i s

R γ ~

,

N equations, with solutions

i i s

R γ ~

, i i

γ β ~ ) ,..., (

* * 1 N

γ γ ) ,..., (

1 , N i t

P γ γ

,

= ∂ ∂

i s

R J = ∂ ∂

i

J β = ∂ ∂

i

J λ

,

= ∂ ∂

i t

P J

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0.2 0.4 0.6 0.8 1 0.4 0.6 0.8 1 1.2 1.4 Source rate (bits/sample) distance (km) first user second user

Simulation

! As distance increases, more compression is needed (lower source rate) ! The “better” users (with a small distance) needs to compress less

! Transform coding ! Gauss-Markov source ! Two users

" 1st user moves around " 2nd user stands still

1st user closer 2nd user closer

2nd user 1st user

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0.2 0.4 0.6 0.8 1 0.2 0.4 0.6 0.8 1 distance (km) Power

adaptive compression fixed compression (complex) fixed compression (simple)

Comparison

Total power (user 1 + user 2)

! our adaptive algorithm vs. fixed schemes (both users have same parameters)

" Fixed simple compression (good for small distance) " Fixed complex compression (good for large distance)

! significant power saving

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Two-step approach

) (

max , i i

q β ) (

, i i s

R β ) (

i i β

γ

base station Choose the optimum complexity set to minimize the total power consumption, corresponding and are together taken as the optimum

• perating parameters.

} ,..., , {

* * 2 * 1 N

β β β ) (

* * , i i s

R β ) (

* * i i β

γ

! Computation

" Dimensions

! Bit rate: ! complexity: ! SINR:

" Full search

! { Rs, Β, Γ Β, Γ Β, Γ Β, Γ}

" Two-step (can be further reduced)

! { Β Β Β Β}

N R

M M M ) (

γ β

× ×

N R

M M M N ) (

β

γ + × ×

R

M

β

M

γ

M

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Conclusions

! Minimize total power consumption while maintaining the video quality at the receiver

" mobile users sending video to a base station in one CDMA cell " Video compression power + radio transmission power are considered " An analytical solution based on simplified models " A two-step fast algorithm

! Results

" Operating parameters depend on the distance " “better” users compress less. " Adaptive solution leads to significant power savings