Cloud processing for 5G systems v6simeone/Cloud processing for 5G systems v6.pdf · Cloud Processing for 5G Systems Cloud Radio Access Network (C-RAN): Baseband processing offloading

Cloud Processing for 5G Systems

O. SimeoneNew Jersey Institute of Technology (NJIT)

Joint work with S.-H. Park1, O. Sahin2, S. Shamai3, J. Kang4 and J. Kang4

1 2 3 4


[Barbarossa et al ‘14]

• What will 5G be? Highly integrative with a flexible and intelligent core network [Andrews et al ‘14]

• Enabling technologies: mmwave, massive MIMO, extreme densification, cloud processing, software defined networking,…

2


Cloud Radio Access Network (C-RAN): Baseband processing offloading from the base stations to the cloud

Interference management (CoMP), simplified base station deployment

Cloud Mobile Computing: Code offloading from mobile users to the cloud

Increase in battery lifetime, enabling computation-heavy mobile applications (e.g., video processing, object recognition, gaming, …)

• Examples:

3


Cloud Radio Access Network (C-RAN): Baseband processing offloading from the base stations to the cloud

Interference management (CoMP), simplified base station deployment

Cloud Mobile Computing: Code offloading from mobile users to the cloud

Increase in battery lifetime, enabling computation-heavy mobile applications (e.g., video processing, object recognition, gaming, …)

• Examples:

4

Overviewo Introduction and Motivationo Uplink with Single-Hop Fronthaul Topology

– Point-to-Point Fronthaul Compression– Distributed Fronthaul Compression– Joint Decompression and Decoding– Compute-and-Forward– Time-Varying Channels

o Downlink with Single-Hop Fronthaul Topology – Point-to-Point Fronthaul Compression– Multivariate Fronthaul Compression– Compute-and-Forward– Time-Varying Channels– Inter-Cluster Multivariate Fronthaul Compression

o Uplink with Multi-Hop Fronthaul Topologyo Conclusions

5




o Uplink with Multi-Hop Fronthaul Topologyo Conclusions 6

Cloud Radio Access Networks

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

takes place in the “cloud”

7


• Base stations act as radio units (RUs), or remote radio heads (RRHs): up/down-converters withno need for codebook information

• Fronthaul links carry complex (IQ) baseband signals

8

Advantages:• Low-cost “green” BSs (RUs)• Dense deployment with enhanced indoor coverage• Flexible radio and computing resource allocation• Effective interference mitigation via joint baseband processing (e.g., eICIC and CoMP in LTE-A)

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


9

10


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

• Fronthaul/backhaul links

• Mmwave backhauling for 5G systems [Ghosh ‘13]• Convergence of wireless access and backhaul [Dahlman et al

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

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

… Rate higher than supported by standard optical fiber channels (10GE)… 11

[IDT, Inc]

Scope of the talk• How to design C-RANs? An information-theoretic view• Implications on system design• Topics:

o Advanced fronthaul compression inspired by network information theory

o Structured coding (compute-and-forward)o Joint design of fronthaul and wireless communication o Optimal functional split RU-CU: channel estimation, precoding o Single-hop vs. multi-hop fronthaul network

12





System Model

MS

MS 1

RU1

RU

RU

MN

i

BN

,1Bn antennas

,B in antennas

, BB Nn antennas

iH

1H

BNH

CU

1C

iC

BNC

x

1y

iy

BNy

,1Mn antennas

, MM Nn antennas

Single-cluster single-hop fronthaul topology14

• Point-to-point fronthaul compression:– Algorithms [Segel and Weldon] [Samardzija et al ‘12] [Nieman

and Evans ’13]– Testbed results [Irmer et al ’11] [Vosoughi et al ‘12]

Point-to-Point Fronthaul Compression

15

RU 1

Decompressor

Decoder

RU NR

ul1y

ul2y

ulRNy

Fronthaul

RNC

1C ul1y

ul2y

ulˆRNy

Control Unit

Decompressor

Decompressor


Compressor

RU 2

Fronthaul

2CCompressor

FronthaulCompressor

16


uly000

001

010

100

……

101 uly

17

• Ex.: Scalar quantizer

niy

ˆ niy

110010011


18

• Ex.: Vector quantizer

• Information-theoretic analysis (rate-distortion theory)– Compression at RU described by a test channeli ˆ( | )i ip y y

ˆ( | )i ip y yiy ˆ iy

iy ˆ iy

Quantizationnoise


19

- The test channel determines the shape of the quantization regions

- Fronthaul rate = ˆ( ; )i i iI Cy y

ˆ( | )i ip y y

niy

ˆ niy

110010011


20

- MSE bound closely approximated by sufficiently large vector quantizers [Gray and Neuhoff ‘98] or scalar quantizers with entropy encoding [Ziv ‘85]

- Gaussian noise model asymptotically correct with dithered lattice quantizers such as TCQ, o at high resolution [Zamir and Feder‘96]

-Graphical codes [Nagpal et al 09]

niy

ˆ niy

110010011


21





Decoder

ul(1)y

ul(2)y

ul( )RNy

ul(1)ˆ y

ul(2)ˆ y

ul( )ˆ

RNy

Control Unit

Decompressor

WZDecompressor

WZDecompressor

Distributed Fronthaul Compression

RU 1

RU NR

Fronthaul

RNC

1CCompressor

RU 2

Fronthaul

2CWZ

Compressor

Fronthaul

WZCompressor

[Sanderovich et al ’09] [del Coso and Simoens ’09] [Zhou and Yu ’11]

23


uly000

001

010

100

……

101 uly

24

• Ex.: Scalar quantizer


uly

uly

000

001

010

100

101

……

25

• Ex.: Scalar WZ compression

10010

10010

10010

10010

10010

10010

10010

ˆ niy

niy


26

• Ex.: Vector WZ compression

10010

10010

10010

10010

10010

10010

10010

- Fronthaul rate =

ˆ niy

niy

ˆ ˆ( ; | )i i S iI Cy y y


27

• Information-theoretic analysis (rate-distortion theory):

• Practical implementations:o Quantization code that can be partitioned into cosets that are good

channel codes: trellis codes [Pradhan and Ramchandran ’03], polar codes [Korada and Urbanke '10], compound LDGM-LDPC code [Martinian and Wainwright '06]

o Separate quantization and channel coding: scalar quantization and vector binning [Liu et al '06], scalar quantization and turbo codes [Aaron et al ‘02]


QuantizernX Binning M

28


• Max-Rate compression problem at BS

– is the set of BSs previously selected by BS ordering

ˆ( | )ˆ ˆmaximize ( ; | )

ˆ ˆs.t. ( ; | )i i

ip

i i i

I

I Cy y

x y y

y y y

S

S

S

ˆ( | )i ip y yiyˆ iy

BS i

cloud

yS

i

29

• Proposition [del Coso and Simoens ’09]. The optimal test channel for (P1) is Gaussian, i.e.,

– Linear transformation given as

ˆ( | )i ip y y

ˆ i i i i y A y q with ~ ( , ).iq 0 ICN

,

†ˆ| 1diag , ,

i B in y y U US

1 11 ,1ll

,

1/2 †1diag , ,

B ii n A U

where

iA


30

• Block diagram of Max-Rate compression

†Uiy

1y

,B iny

1

,B in

,1iq

,, B ii nq

,1îy

,,ˆB ii ny

ySuncorrelated

conditioned on

ConditionalKLT

1† †ˆ|

x y x x x t xH H H H

S SS S S S


31

• Simulation setting:– Three-cell heterogeneous network– One MBS, HBSs and MSs per cell

• MBS located at the cell center• HBSs and MSs are uniformly distributed within the cells to

which they belong.

– Single-antenna MSs, i.e.,

• Schemes:– Max-Rate, (direct/indirect) MMSE, DF– With side information and no side information

/ 3 1BN

, 1M in

MN

Example

32

[Park et al ’13]

9, 9, 0.5B MN N 10 bps/HzC

Example

33

[Park et al ’13]

,9, 9, 8B M B iN N n 15 bps/HzC tx 5 dBP

Example

34

[Park et al ’13]

• CSI requirements of max-rate compression

†Uiy

1y

,B iny

1

,B in

,1iq

,, B ii nq

,1îy

,,ˆB ii ny

ySuncorrelated

conditioned on

ConditionalKLT

1† †ˆ|

x y x x x t xH H H H

S SS S S S


35

[Park et al ’13]

BSiy

i

Original Max-Rate maximization

Robust Max-Rate maximization [Park et al ’13]

Backhaul iCˆ iy

Cloud

yS

ˆ iy

ˆ|x yS

BSiy

i Backhaul iCˆ iy

Cloudˆ iy

ˆ ˆ| |ˆ ( ) x y x yS S

yS


36

• Imperfect covariance feedback, i.e., .• Additive model

with uncertainty set , i.e., where

ˆ ˆ ˆ| | | x y x y x yS S S

U

ˆ ˆ| | x y x yS S

ˆ ˆmin | LB max | UB( ) , ( ) x y x y

S S

ˆ| x ySU

†ˆ ˆ| |i i x y x yH HS S


37

• Max-min robust formulation

ˆ|ˆ( | )

ˆ ˆmax | UB min | LB

ˆ ˆmaximize min ( ; | )

ˆ ˆ s.t. ( ; | ) ,

and .

nMi iip

i i i

I

I C

x yy y

x y x y

x y y

y y y

S

S S

SH

S

( 2)P


38

• Theorem [Park et al ’13].A local optimum for problem (P2) is given as

ˆ i i i i y A y q with ~ ( , ).iq 0 ICN

,

†ˆ| 1

ˆ diag , ,i B in y y U US

,1, , : solution to the following mixed integer problemB in

,

1/2†

1diag , ,B ii n A U

where,

† †

1 1B in

UU U U I

,

1 ,

,

LB, , , 1

,

UB1

max log(1 ( )) log(1 )

s.t. 0 1,( ), 1, , ,

log(1 ( )) .

B i

nB i

B i

n

l l ll

l l B i

n

l l il

l n

C

Pwhere ( ) is a discrete set dependent on .l P


39

• Block diagram:

†Uiy

1y

,B iny

1

,B in

,1iq

,, B ii nq

,1îy

,,ˆB ii ny

ConditionalKLT

ˆ|x yS

Solution of optimization problem


40

• Simulation setting:– Single-cell heterogeneous network– One MBS and HBSs

• MBS located at the cell center• HBSs and MSs uniformly distributed within whole cell

– Single-antenna MSs, i.e.,

• Schemes:– Max-Rate, NSI– Max-Rate, imperfect SI– Max-Rate, imperfect SI, robust– Max-Rate perfect SI

1BN

, 1M in

Example

41

,4, 8, 2B M B iN N n 0.5 tx 10 dBP

Example

42

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

Simulation Set-upN K

43

• Frequency reuse pattern with reuse factor for 1-cell cluster [Wang and Yeh ’11]

1/ 3F

Simulation Set-up

44

Simulation Set-upParameters Assumptions

System bandwidth 10 MHz

Path-loss (macro-BS - MS)

Path-loss (pico-BS - MS)

Antenna pattern for sectorized macro-BS antennas

Lognormal shadowing (macro-BS - MS) 10 dB standard deviation

Lognormal shadowing (pico-BS - MS) 6 dB standard deviation

Antenna gain after cable loss (macro-BS) 15 dBi

Antenna gain after cable loss (pico-BS, MS) 0 dBi

Noise figure 5 dB (macro-BS), 6 dB (pico-BS), 9 dB (MS)

Transmit power 46 dBm (macro-BS), 24 dBm (pico-BS), 23 dBm (MS)

Small-scale fading model Rayleigh-fading

Synchronization Perfect synchronization

Inter-site distance (site: macro-BS) 750 m

Frequency reuse factor F=1/3

Number of antennas Single antenna at each macro/pico-BS and MS

Channel state information (CSI) Full CSI at control units about BSs in the cluster

10PL(dB) 128.1 37.6log ( in km)R R

10PL(dB) 38 30log ( in m)R R 2

3dB

3dB

( ) min[12( / ) , ]( 65 , 20 dB)

m

m

A AA

45

• 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

maxR

[3GPP-TR-136942, Annex A]

minattenuate

where

46

• 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.

47

• Cell-edge throughput versus average spectral efficiency– macro pico maxUplink, 1-cell cluster, 3 pico-BS, 5 MSs, ( , ) (9,3) bps/Hz, 10, 0.5, 1/ 3N K C C T F

0.85 0.9 0.95 1 1.05 1.11800

2000

2200

2400

2600

2800

3000

3200

3400

3600

spectral efficiency [bps/Hz]

5%-il

e ra

te (c

ell-e

dge

thro

ughp

ut) [

kbps

]

Point-to-point compressionMultiterminal compression

=2.0

=1.0

=0.5

=0.25

1.6x

Numerical Results

48





• An achievable rate with JDD was derived in [Sanderovich et al ’09]

sum ˆ ˆmin ( ; | ) ( ; ) ,j j jj

R C I I

y y x x y

BSS N

S

Joint Decompression and Decoding

50

• The problem of maximizing the sum-rate is a Difference of Convex (DC) problem [Park et al ’14].

-30 -25 -20 -15 -10 -5 07.5

8

8.5

9

9.5

inter-cell channel gain [dB]

aver

age

per-c

ell s

um-ra

te [b

it/c.

u.]

cutset upper boundJDD w/ MM algorithmSDD w/ exhaustive orderingSDD w/ greedy ordering

separate decompressionand decodinng

joint decompressionand decoding

Joint Decompression and Decoding

51





Compute-and-Forward[Nazer et al ’09] [Hong and Caire ’11]

• The MSs use nested lattice codes:

[B. Nazer]

53

Decoder

Control Unit

Compute-and-Forward[Nazer et al ’09] [Hong and Caire ’11]

RU 1

RU NR

ul1y

ul2y

ulRNy

Fronthaul

RNC

1CInteger Decoder

RU 2

Fronthaul

2C

Fronthaul

Integer Decoder

Integer Decoder

54

• Three-cell SISO circular Wyner model

Numerical Results

CU

CCC

55

Numerical Results3 bit/s/Hz and =0.4C

0 5 10 15 20 25 30

1

1.5

2

2.5

3

MS transmit power [dB]

per-c

ell s

um-ra

te [b

its/s

/Hz]

Cut-set upper bound

Point-to-point compression

Single-cell processing

56

Numerical Results3 bit/s/Hz and =0.4C

0 5 10 15 20 25 30

1

1.5

2

2.5

3

MS transmit power [dB]

per-c

ell s

um-ra

te [b

its/s

/Hz]

Cut-set upper bound

Distributed compression



Compute-and-forward

57





Time-Varying Channels

• So far, static channels • With static channels, the fronthaul overhead due to CSI transfer is

negligible

MS

MS 1

RU1

RU

RU

MN

i

BN

,1Bn antennas

,B in antennas

, BB Nn antennas

iH

1H

BNH

CU

1C

iC

BNC

x

1y

iy

BNy

1y

ˆ iy

ˆBNy

,1Mn antennas

, MM Nn antennas

59


• Time-varying ergodic channel with coherence blocks of length T channel uses [Hassibi et al ’03]

• RU-CU functional split: where to perform channel estimation?

60

… p d

T T T

p d p d …

Compress-Forward-Estimate (CFE)[Hoydis et al ‘11]

H CU

C1

MS NM

...

MS 1RU 1

RU NB CNB

,1 ,1p dY Y

, , B Bp N d NY Y

ˆ ˆp dY Y

• The RUs compress both training and data fields• The CU estimates the channel and performs coherent decoding

61

Estimate-Compress-Forward (ECF)

H CU

C1

MS NM

...

MS 1RU 1

RU NB CNB

[Kang et al ‘14]

• Motivated by the information-theoretic argument of separate estimation and compression [Witsenhausen ‘80]

• The RUs estimate the channel and compress both CSI and data field• The CU performs coherent decoding

62

Semi-Coherent Processing[Kang et al ‘14]

• For low fronthaul capacities…• BS estimates the CSI and performs local equalization • BS compresses and forwards the equalized data signals to CU• CU jointly decodes the data signal by mismatched decoding metric

[Weingarten et al ’04]

63

H

CU

C1MS 1

RU 1

,1dX

ˆdX

Semi-Coherent Processing[Kang et al ‘14]

H

CU

C1MS 1

RU 1

,1dX

ˆdX

is a weight factor for each coherence block

• Mismatched decoding:

• With one fronthaul bit per coherent block

weigh less blocks with poor CSI64

Non-Coherent Capacity

H CU

C1

MS NM

...

MS 1RU 1

RU NB CNB

• Do not impose training + data structure [Marzetta and Hochwald ’99]• The RUs quantize the received signal

[Kang et al ‘14]

65

66

NB=NM=2, Nt=Nr=4, C=6, T=10, K=0

Example

67

ExampleNt = Nr = 2, P = 10dB, T = 4, and K = 0

68

Nt = Nr = 2, P = 5dB, C=5 bits/s/Hz, T = 4, and K = 0Example





System Model

CU1, ,

MNM M

RU1

,1Bn antennas

1x1C

RU

BN

, BB Nn antennas

BNxBNC

MS 1M1

,1Mn antennas

1y

MSˆ

MNMMN

, MM Nn antennas

MNy

1H

MNH

Single-cluster single-hop fronthaul topology70


1C1M Channel

encoder 1

Precoding

RU 1

Central Unit

MNM Channelencoder NM

1s

MNs

Compressor1

1x

BNxBNC

1x

RU BNx

BNBN

Compressor

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

71


2x

1x

Ex.: Scalar quantization

72

2x

1x

… uncorrelated quantization noise


73






Multivariate Fronthaul Compression

1C1M Channel

encoder 1

Precoding

RU 1

Central Unit


1s

MNs

1x

BNxBNC

1x

RU BNx

BN

Multivariatecompression

[Park et al ’14]

75

• Multivariate compression produces compressed signals with correlated quantization noises: new design degree of freedom


can be reduced by controlling 1,2 2,1

HΩ Ω

RU 1

RU 2

MS

1 1 1H x E As q

2 2 2H x E As q

1,1H

1,2H

1z1

11 1 12

qH

qy H As z

1,1 1,21 1

2,1 2,2

, H

Ω Ω0 H H

Ω ΩCN

CU

1C

2C

76


2x

1x

77



2x

1x

78


Multivariate Compression Lemma

| , for all {1, , }i ii i

h X h X X R M

SS S

S

1, , MC C

1 1( , , , ) ( ) ( , , | )M Mp x x x p x p x x x

i.i.d.joint typicality wrt

79

• Multivariate compression lemma– Ex.: With , , we have

while with separate compression, we have

1 1 1

2 2 2

1

1 1

2 2

2 1 2 1 21 2 1 2

| , ( 1)

| , (

;

;

; ,

2)

, | . ( 3; )

Ih h C A

h h C A

h h h C

I

I CI A

x x x

x x x

x x x x

x x

x x

x x x x xx

1 1 1

2 2 2

; , ( 1)

; . ( 2)

I C B

I C B

x x

x x

2BN


80


(contrapolymatroid)81

• Linear precoding (DPC treated in a similar way)

• Gaussian test channel:

• The compressed signal is given as

with and


,, ~ ( , ),i i i i i i i x x q q 0 Ω BCN N

, x As q1 , ,

B

HH HN x x x

1 , , ~ ( , )B

HH HN q q q 0 Ω CN

1,1 1,2 1,

2,1 2,2 2,

,1 ,2 ,

B

B

B B B B

N

N

N N N N

Ω Ω Ω

Ω Ω ΩΩ

Ω Ω Ω

82

• Joint optimization of precoding and compression:

where

• DC problem: Local optimum via MM algorithm

, 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


83

Step :

• The implementation of joint compression is complex• A successive architecture with a given permutation .

– Compression rate at step :

i

: B BN N

Step 1:

( 1)i

(1)x

(1)q

(1)x

(1) (1), (1)~ , q 0 ΩCN

compression

(1)

( )( 1)

( )

ii

i

x

uxx

( ) ( ) ( )

1,i i i

x u u

( )ˆ iq

( )ix

MMSE estimationof given( )ix ( )iu

compression

(1) (1)( ) |ˆ ~ ,i x uq 0CN

( )ˆ ix

( ) ( )ˆ ;i iI x x( 1)i i


84

• Successive estimation-compression architecture


85

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

Simulation Set-upN K

86

• 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

87





• Reverse compute-and-forward (RCoF) [Hong and Caire ‘12]

Compute-and-Forward

1C1M Channel

encoder 1

Integerprecoding

RU 1

Central encoder


1s

MNs

1x

BNx BNC

1x

RU BNx

BN

89

• Three-cell SISO circular Wyner model

Numerical Results

CU

CCC

90

0 2 4 6 8 10 12 140

1

2

3

4

5

6

C [bits/s/Hz]

per-c

ell s

um-ra

te [b

its/s

/Hz]

Cut-set upper bound

Multivariate compression


Linear precoding


• Three-cell SISO circular Wyner model ( and )

Numerical Results20 dBP 0.5

91



0 2 4 6 8 10 12 140

1

2

3

4

5

6

C [bits/s/Hz]

per-c

ell s

um-ra

te [b

its/s

/Hz]

Cut-set upper bound



DPC precoding

Linear precoding


92



0 2 4 6 8 10 12 140

1

2

3

4

5

6

C [bits/s/Hz]

per-c

ell s

um-ra

te [b

its/s

/Hz]

Cut-set upper bound



DPC precoding

Compute-and-forward

Linear precoding


93





95

• Ergodic channel with coherence blocks of length T channel uses [Hassibi et al ’03]


T T T

… …

96

• RU-CU functional split: where to perform precoding?

• Instantaneous CSI: Design different precoders for each coherence period

• Stochastic CSI: o CU only knows the spatial correlation (e.g., [Adhikari et al ‘14])

o Design a single precoder across all coherence periods


97

Compress-After-Precoding

98

Compress-Before-Precoding[Park et al ‘13] [Kang et al ‘14] [Patil and Wu ‘14]

• Clustering: Send each user’s message to a subset of RUs• Precoding performed at the RUs

Precodingdesign

CSIMUX

...

CU

MUX

Q

Q

...

De-MUX

De-MUX

Q-1Pre-

coding

Pre-coding

RU1

RUNR

data streams

Cluster-ing

Fronthaul

Fronthaul

Channelcoding

Q-1

Channelcoding

99

Compress-Before-Precoding[Park et al ‘13] [Kang et al ‘14] [Patil and Wu ‘14]

• Perfect CSI: Precoding overhead gets amortized over T• Stochastic CSI: Since the precoding matrix is the same across all the

coherence periods, negligible overhead

Precodingdesign

CSIMUX

...

CU

MUX

Q

Q

...

De-MUX

De-MUX

Q-1Pre-

coding

Pre-coding

RU1

RUNR

data streams

Cluster-ing

Fronthaul

Fronthaul

Channelcoding

Q-1

Channelcoding

100

Example

101

ExampleNR = 4, Nt,i = 2, Nr,j = 1, P = 10 dB, C=4 bits/s/Hz, T = 10

102

ExampleNR = NM = 4, Nt,i = 2, Nr,j = 1, P = 20 dB, C= 2 bits/s/Hz,

instantaneous CSI

103

ExampleNR = NM = 4, Nt,i = 2, P = 10 dB, C= 3 bits/s/Hz, T=10





Inter-Cluster Multivariate Compression Design

[Park et al ‘14]

CU 1

1,1C

CU 2

RU(1,1)

RU(1,2)1,2C

2,1C RU(2,1)

RU(2,2)2,2C

MS(1,1)

MS(1,2)

MS(2,1)

MS(2,2)

1,1 1,2,M M

2,1 2,2,M M

Inter-cluster interference

1,1M

1,2M

2,1M

2,2M

105

Inter-Cluster Multivariate Compression Design

106





Multi-Hop Fronthaul Topology [Park et al ‘14]

108

• Multiplex-and-Forward (MF)• Decompress-Process-and-Recompress (DPR): In-network

processing• Optimization of the uplink sum-rate

Multi-Hop Fronthaul Topology

109

• [Ni et al ’13] Simulation-based study of in-network processing on the LLRs

• [Goela and Gastpar ’12] Optimal linear in-network processing strategies in a multihop network for estimation

• [Avestimehr et al ‘08] Deterministic and Gaussian relay networks• [Cuff et al ‘09] Cascade source coding

Related Works

110

MS 1

MS K

x

RU 1

RU 3

RU 5

RU 7

RU 4

RU 2

CU

RU 6

1y

2y

3y

4y

5y 6y

7y

i i i y H x z

1,2C

1,3C 2,8C

3,8C

4,3C4,8C

6,8C5,6C

5,7C7,8C

MS: mobile stationRU: radio unit

Uplinkchannels

System Model

Capacitated directed acyclic graph (DAG)111

RU i

?1

| ( ) |

i

iiI

i

e

i

e

yu

r

u

1

| ( ) |

i

iiO

e

e

u

u

RU Operation

• Multiplex-and-Forward (MF)• Decompress-Process-and-Recompress (DPR): In-network

processing

112

Compressioniy ˆ iy

RU i

MU

X / DEM

UX

( )I i ( )O i

From RUs inprevious layers

To RUs and CUin next layers

Multiplex-and-Forward

113


~ ( , ).i iq 0 ΩCNˆ ,i i i y y q• Gaussian test channel

Compressioniy ˆ iy

114

• Define as the capacity used to convey the signal compressed at node on edge for act ,e i E M

0ief

iv e

act,ie e

if C e

M

E


Edge capacity constraint

Flow conservation at each RU

115

• Sum-rate maximization:

• Difference-of-convex (DC) problem: Majorization Minimization (MM) approach to find a locally optimal solution.

,act

{ } ,{ 0}

( 1)

maximize log det log det

log det log det ,

s.t.

i iie e i

i

I

f

ii i e

e M

f i

xΩ 0

y

HΣ H I Ω I Ω

Ω Σ Ω

M

E M

M


act,ie e

if C e

M

E

116

( )O i

Compression

( )Ie e i

u

iy

i

Compression

Decom

pression

LinearProcessing

( )I i

RU

From RUs inprevious layers

To RUs and CUin next layers

Decompress-Process-and-Recompress

( )Oe e iu

ir

117

• Process and decompress:

with

,e e i e u L r q


~ ( , )e eq 0 ΩCN

• Characterization of the relation between input and output vector [Goela and Gastpar ’12]

118

• Sum-rate maximization

act

,

{ , }

act

rxact

log detmaximize

log det

s.t. log det log det , tail(e), ,

, , .

e e e

i

i e iee R i l l

H H H H

H H

He e e e e

d dd n ee e

C i e

e

x

L Ω 0

r

T HΣ H T TT TΩT

TT TΩT

Ω L Σ L Ω

L L

E

E

E


119

• Lemma. We can set for without loss of optimality.

• The optimization over is a DC problem: MM algorithm

e L I

act{ }e eΩ E

acteE


120

RU1

RUN

1y NyRUN+1

RU2N

1Ny 2Ny

RU2N+1

RU2N+2

RU2N+32 1Ny 2 2Ny 2 3Ny

CU

1,2 1NC

1,2 2NC

,2 1N NC ,2 2N NC 1,2 2N NC 2 ,2 2N NC

2 ,2 3N NC

1,2 3N NC

2 1,2 4N NC

2 2,2 4N NC

2 3,2 4N NC

1 {1, ,2 }N V

2 {2 1, ,2 3}N N V

3 {2 4}N V

Layer 1

Layer 2

Layer 32 3M N

• Each MS and RU have a single antenna• All RUs have the same average SNR• Rayleigh fading channels

Numerical Results

121

K=4, SNR = 20 dB

Numerical Results

2 4 6 8 10 12 14 16 18 20 222.5

3

3.5

4

4.5

5

5.5

6

6.5

7

Number N of RUs in layer 1

Aver

age

sum

-rate

[bits

/s/H

z]DPRMF

=4 bits/s/Hz

=3 bits/s/Hz

=2 bits/s/Hz

eC

eC

eC

122

-

K=5, N=2, SNR = 0 dB

0 5 10 15 200

2

4

6

8

10

12

14

16

18

Fronthaul capacity of each edge [bps/Hz]

Aver

age

sum

-rate

[bps

/Hz]

cutset boundDPRMF

Numerical Results

123





Conclusionso Information-theoretic view on C-RAN

o Novel approaches inspired by theory: distributed/multivariate 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

125

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