Cloud Processing for 5G SystemsO. Simeone

New Jersey Institute of Technology (NJIT)

Joint work with S.-H. Park1, O. Sahin2 and S. Shamai3

1 2 3

What Will 5G Be?• Highly integrative system supporting a variety of applications

• Flexible and intelligent radio access network (RAN)

• Enabling technology: Cloud-RAN (C-RAN)

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[Demestikas ’13]

Overview

o Introduction and Motivationo System Model o Point-to-Point Fronthaul Compressiono Multivariate Fronthaul Compressiono Numerical Results o Conclusions

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Cloud Radio Access Networks

• Heterogeneous dense network• Macro, femto, pico-BSs, relays• C-RAN: Baseband processing

takes place in the “cloud”(virtualization)

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Cloud Radio Access Networks

• Fronthaul links carry “radio” signals to/from control unit (CU)

• Base stations act as radio units (RUs) or remote radio heads (RRHs)

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Cloud Radio Access Networks

• Analog (e.g., radio-over fiber) vs digital (e.g., CPRI) fronthaultransmission

• Digital transmission:digitized complex (IQ) baseband signals

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Advantages:• Dense deployment with low-cost “green” BSs (RUs)• Flexible radio and computing resource allocation (statistical multiplexing)• Effective interference mitigation via joint baseband processing (e.g., eICIC and CoMP in LTE-A)• Easier network upgrades and maintenance

Key challenge: Effective transfer of the IQ signals on the fronthaul links

Cloud Radio Access Networks

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Cloud Radio Access Networks• CPRI standard based on ADC/DAC

… Rate higher than supported by standard optical fiber channels (10GE)…

[IDT, Inc]

• Fronthaul links

• Mmwave front/backhauling for 5G systems [Ghosh ‘13] [Checkoet al ‘15]

• Copper (LAN cable) for indoor coverage [Lu et al ‘14]

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Cloud Radio Access Networks

The distribution of backhaul connections for macro BSs (green: fiber, orange: copper, blue: air) [Segel and Weldon].

System Model

CU1, ,

MNM M

RU1

1x1C

RU

BN

BNxBNC

MS 1M11y

MSˆ

MNMMNMNy

1H

MNH

11

S.-H. Park. O. Simeone, O. Sahin and S. Shamai (Shitz), “Fronthaul compression for Cloud Radio Access Networks,” IEEE Signal Processing Magazine, vol. 31, no. 6, pp. 69-79, Nov. 2014.

Point-to-Point Fronthaul Quantization/ Compression

1C1M Channel

encoder 1

Precoding

RU 1

Central Unit

MNM Channelencoder NM

1s

MNs

Quant/ Comp1

1x

BNxBNC

1x

RU BNxBN

BNQuant/ Comp

[Simeone et al ’09] [Patil and Yu ’14]

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Channel State Information

Point-to-Point Fronthaul Quantization/ Compression

2x

1x

Ex.: Scalar quantization

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2x

1x

Point-to-Point Fronthaul Quantization/ Compression

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Ex.: Scalar quantization

2x

1x

Point-to-Point Fronthaul Quantization/ Compression

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Ex.: Scalar quantization

2x

1x… uncorrelated quantization noise… uniform “interference”

Point-to-Point Fronthaul Quantization/ Compression

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Ex.: Scalar quantization

Multivariate Fronthaul Quantization/ Compression

[Park et al ’14]

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• Key idea: Controlling the impact of the interference on the signal space

• Akin to – quantization noise shaping techniques used in transform coding– interference control via linear precoding

Multivariate Fronthaul Quantization/ Compression

1C1M Channel

encoder 1

Precoding

RU 1

Central Unit

MNM Channelencoder NM

1s

MNs

1x

BNxBNC

1x

RU BNxBN

MultivariateQuant/ Comp

[Park et al ’14]

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Multivariate Fronthaul Quantization/ Compression

2x

1x

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Ex.: Scalar quantization

Multivariate Fronthaul Quantization/ Compression

2x

1x

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Ex.: Scalar quantization

• Joint optimization of precoding and compression:

where

, 1

,

maximize ,

s.t. , , for all ,

tr , for all .

MN

k kk

i Bi

Hi i i i i B

w f

g C

P i

A Ω 0

A Ω

A Ω

E AAE Ω

SS

S N

N

,

, ;

log det ( ) log det ,

, |

log det log det .

k k k

H H H Hk k k l l k

l k

ii

H H Hi i i i i

i i

f I

g h h

C

A Ω s y

I H AA Ω H I H A A Ω H

A Ω x x x

E AA E Ω E ΩE

S SS

S SS S

Multivariate Fronthaul Quantization/ Compression

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• Successive estimation-compression architecture

Multivariate Fronthaul Quantization/ Compression

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• In each macro-cell, pico-BSs and MSs are uniformly distributed.

Simulation Set-up

N K

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• LTE rate model [3GPP-TR-136942]Simulation Set-up

min

attenuate min max

max max

0, if( ) ( ), if

, if

k

k k k k

k

R SR

12 max max attenuate

attenuate

max

min

: SINR at MS ; ( ) log (1 ); ( / );: attenuation factor representing implementation losses;: Maximum and minimum throughput of the codeset, bps/Hz;: Minimum SINR of the codeset.

k k S S R

R

Parameter UL DL Notes

2.0 4.4 Based on 16-QAM 3/4 (UL) & 64-QAM 4/5 (DL)

-10 dB -10 dB Based on QPSK with 1/5 (UL) & 1/8 (DL)

0.4 0.6 Representing implementation losses

maxRmin

attenuate

where

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• Proportional fairness metric

– At each time , the rate is updated as

Simulation Set-up

sum-PF1

( )( )K

k

k k

R tR tR

: fairness constant;( ) : instantaneous rate for MS at time ;: historical data rate for MS until time 1.

k

k

R t k tR k t

where

(P1)

(1 ) ( )k k kR R R t

t kR

where [0,1]: the forgetting factor.

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• Cell-edge throughput versus average spectral efficiency–

Numerical Results

macro pico maxDownlink, 1-cell cluster, 1 pico-BS, 4 MSs, ( , ) (3,1) bps/Hz, 5, 0.5, 1/ 3N K C C T F

0.8 0.85 0.9 0.95 1 1.05 1.1 1.15 1.2 1.250

500

1000

1500

2000

2500

3000

3500

4000

spectral efficiency [bps/Hz]

5%-il

e ra

te (c

ell-e

dge

thro

ughp

ut) [

kbps

]

Point-to-point compressionMultiterminal compression

=1.5

=0.5

=0.25

2x

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Conclusions and Outlook

o C-RAN design inspired by network information theory

o Other examples: distributed fronthaul compression, compute-and-forward, joint decompression and decoding, estimate-compress-forward, semi-coherent processing, in-network processing,…

o Implementation: linear codes, scalar quantization, successive estimation and compression,…

o Performance of conventional techniques can be drastically improved by strategies inspired by information theory

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