C-RAN Challenges and Solutions v8 · Cloud Radio Access Networks • Fronthaullinks carry...

Cloud Radio Access Network: Challenges and (Some) Solutions

Osvaldo SimeoneNew Jersey Institute of Technology

2

Seok-Hwan ParkChonbuk University

Onur SahinInterDigital Shlomo Shamai

TechnionJoonhyuk Kang

KAIST

Shahrouz KhaliliNJIT

Wonju LeeKAIST

Jinkyu KangKAIST

Eunhye HeoKAIST

Hyuncheol ParkKAIST Marco Levorato

UC Irvine

Cloud Radio Access Networks

• Heterogeneous dense cellular network• Macro, femto, pico-BSs• C-RAN: Joint baseband processing

takes place in the “cloud”• Instance of network

function virtualization

3


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

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

• Cloud resources shared by all connected RUs unlike D-RAN

4


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

• Digital transmission:digitized complex (IQ) baseband signals

5

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: Capacity and latency bottleneck imposed by fronthaul network limitations


6

7

Fronthauling

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

• Fiber-optic communication is the preferred choice.

• Mmwave front/backhauling [Ghosh ‘13] [Checko et al ‘15][Fujitsu ’15]

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

Fronthaul Capacity Limitations• Common Public Radio Interface (CPRI) specifies the fronthaul

interface between CU and RUs • Sampling and scalar quantization

[Checko et al ‘15]

Fronthaul Latency Limitations

Air interface

Fronthaul transport (∝ fronthaul length; single vs. multi-hop; <250 μs [NGMN ‘15])

CU processing (~ms)

• Fronthaul transport and CU processing depends on transport technology (wireless vs. wired).

• Latency associated with two-way mobile-CU transmission:

10

Overviewo Fronthaul capacity limitations

– Point-to-point fronthaul compression (compressed CPRI)– Multivariate fronthaul compression– Alternative functional splits

o Fronthaul latency limitations

– HARQ with local feedback

o Conclusions

11





o Conclusions

System Model

CU

RU1

1x1C

RU

BN

BNxBNC

MS 11y

MS MNMNy

fronthaul

12

Data streams

CSI

wireless downlink channel

CPRI-Based Implementation

1C1M Channel

encoder 1

Precoding

RU 1

Central Unit

MNM Channelencoder NM

1s

MNs

Quantization1x

BNxBNC

1x

RU BNx

BN

13

Quantization

CSIfronthaul

Compressed CPRI

1C1M Channel

encoder 1

Precoding

RU 1

Central Unit


1s

MNs

Quantization/Compression

1x

BNxBNC

1x

RU BNx

BN

14

Quantization/Compression

CSIfronthaul

15

• Filtering [Samardzija et al ‘12] [Guo et al ‘12]: Remove redundancies due to oversampling

• Per-block scaling [Samardzija et al ‘12] [Guo et al ‘12]: Mitigate large peak-to-peak variations

• Optimized non-uniform quantization [Samardzija et al ‘12] [Guo et al ‘12]: Lloyd-Max or companding

• Noise shaping [Nieman and Evans ’13]: predictive quantization

• Lossless compression [Vosoughi et al ’12]: entropy coding

• Compressed CPRI reduces the fronthaul rate by a factors around 3 [Dotsch et al ’13]

Compressed CPRI

16





o Conclusions

Is Point-to-Point Quantization Optimal?

1C1M Channel

encoder 1

Precoding

RU 1


1s

MNs

Quantization1x

BNxBNC

1x

RU BNx

BN

17

Quantization

CSI

Central Unit

fronthaul

1C1M Channel

encoder 1

Precoding

RU 1


1s

MNs

1x

BNxBNC

1x

RU BNx

BN

Multivariatequantization

18

Multivariate Fronthaul Quantization?

CSI CSI

Central Unit

fronthaul

19

Point-to-Point Quantization

2x

1x

20


2x

1x

21


2x

1x

22


2x

1x

23

2x

1x

quantization noise = interference


24

h

h


signal space

• The impact of interference depends on CSI.

25

h

h

affects MS

does not affect MS


signal space

1C1M Channel

encoder 1

Precoding

RU 1

Central Unit


1s

MNs

1x

BNxBNC

1x

RU BNx

BN


26

Multivariate Fronthaul Quantization

W. Lee, O. Simeone, J. Kang and S. Shamai (Shitz), “Multivariate fronthaul quantization for downlink C-RAN: Algorithm design”, arXiv:1510.08301.

CSI CSI

27

h

h


signal space

28

h

h


signal space

1C1M Channel

encoder 1

Precoding

RU 1

Central Unit


1s

MNs

1x

BNxBNC

1x

RU BNx

BN


29


CSI CSI

Signal space

• Dual role of precoding and multivariate quantization

Quantization noise space

1C1M Channel

encoder 1

Precoding

RU 1

Central Unit


1s

MNs

1x

BNxBNC

1x

RU BNx

BN

Multivariatequantization/ compression

30

Multivariate Fronthaul Compression

• Multivariate quantization can be combined with (block) compression.

S.-H. Park, O. Simeone, O. Sahin and S. Shamai (Shitz), “Joint Precoding and Multivariate Backhaul Compression for the Downlink of Cloud Radio Access Networks,” IEEE Trans. Signal Processing., vol. 61, no. 22, pp.5646-5658, Nov. 2013.

CSI CSI

Multivariate Fronthaul Compression

31

• Multivariate compression can be analyzed using information-theoretic methods [Park et al ‘13].

• An application of multivariate compression from network information theory (used, e.g., for multi-description coding)

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

• LTE-recommended simulation set-up [3GPP-TR-136942]• Proportional fairness scheduler• Information-theoretic performance metrics (with clipping)

Simulation Set-up

32

• 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

330.8 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 compressionMultivariate compression

=1.5

=0.5

=0.25

2x

34

Numerical Results• Quantization vs compression

Fronthaul capacity B [bit/symbol]1 1.5 2 2.5 3 3.5 4

1

2

3

4

5

7

8

9

10

11Ideal fronthaul

P = 20 dB

P = 0 dB

6

Rat

e [b

it/sy

mbo

l]

Multivariate compressionMultivariate quantization

35





o Conclusions

36

[Dötsch et al ’13][Wübben et al ’14]

Alternative Functional Splits

Downconv Upconv

Sync + FFT

Resource demapping

Receiveprocessing

Transmitprocessing

Resourcemapping

IFFT

Conventional C-RAN split

MAC

PHY

RF

37



Downconv Upconv

Sync + FFT

Resource demapping

Receiveprocessing

Transmitprocessing

Resourcemapping

IFFT

Compression in the frequency domain [Dotsch et al ’13]: lower PAPR but minor gains

PHY

MAC

RF

38

Compression of only used subcarriers and adaptive quantization [Dotsch et al ’13] [Lorca and Cucala ’13] [Grieger et al ’12]: significant gains in lightly loaded systems



Downconv Upconv

Sync + FFT

Resource demapping

Receiveprocessing

Transmitprocessing

Resourcemapping

IFFT

PHY

MAC

RF

39

-8 -6 -4 -2 0 2 4 60

2

4

6

8

10

12

k

1/S

n Q[k

]

SNRp = 0dB

SNRp = 4dB

SNRp = 8dB

SNRp = 12dB

E. Heo, O. Simeone, and H. Park, “Optimal Fronthaul Compression for Synchronization in the Uplink of Cloud Radio Access Networks,” arXiv:1510.01545.J. Kang, O. Simeone, J. Kang and S. Shamai (Shitz), “Joint Signal and Channel State Information Compression for the Backhaul of Uplink Network MIMO Systems,” IEEE Trans. Wireless Commun., vol. 13, no. 3, pp. 1555,1567, Mar. 2014.

Example• Alternative functional split with resource demapping at RU:

differentiated pilot-data fronthaul compression

40





o Conclusions

Fronthaul Latency Constraints

41

• Fronthaul latency (transport and processing) affects higher layer protocols, most notably ARQ and HARQ

Air interface

Fronthaul transport (∝ fronthaul length; single vs. multi-hop; <250 μs [NGMN ‘15])

CU processing (~ms)

Air interfaceFronthaul transport

42

RU 1

RU 2

BBU(s)

Uplink

MS 1

MS 2

1

Uplink (H)ARQ in D/C-RAN

CU

43

RU 1

RU 2

BBU(s)

MS 1

MS 2

Baseband fronthaul transfer

2


CU

44

RU 1

RU 2

BBU(s)

MS 1

MS 2


CU decoding3

CU

45

RU 1

RU 2

BBU(s)

MS 1

MS 24


ACK/NAKfronthaul transfer

CU

46

RU 1

RU 2

BBU(s)

MS 1

MS 2

ACK/NAKfeedback

5


CU

Fronthaul Latency and ARQ

1 2 3 4 5 6 7 8 9 100.55

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

Fronthaul Delay (slots)

Thro

ughp

ut

Q. Han, C. Wangy, M. Levorato and O. Simeone, “On the Effect of Fronthaul Latency on ARQ in C-RAN Systems,” arXiv:1510.07176.

Conventional(local processing)

C-RAN (Selective Repeat ARQ)

C-RAN (Go-Back-N ARQ)

Fronthaul Latency and HARQ

48

HARQ

RTT timer

subframeindex

mobile RU BU

FH latencyAir int.

processing+ frame building

Air int.FH latency

UL DL

UL dataUL data

Latencybreakdown

0

1

2

3

4

5

6

7

8

9

0

1

2

3

4

5

6

7

8

0

1

2

3

4

5

6

7

8

0

1

2

3

4

5

6

7

8

ULDL9

0

1

2

3

4

5

6

7

1

2

3

4

5

6

7

8

9

ULDL

ACK/NACK

ACK/NA

CK expected in SF n+

4

ACK/NACK

• Ex.: FD LTE

(Andreas Mader)CU

49

Low-Latency Local Feedback

Downconv Upconv

Sync + FFT

Resource demapping

Receiveprocessing

Transmitprocessing

Resourcemapping

IFFT

PHY

MAC

RF

• With alternative functionalsplits, low-latency local control

• Low-latency local control vs.high-latency global controlat the cloud

50


Downconv Upconv

Sync + FFT

Resource demapping

Receiveprocessing

Transmitprocessing

Resourcemapping

IFFT

PHY

MAC

RF

• With alternative functionalsplits, low-latency local control

• Low-latency local control vs.high-latency global controlat the cloud


• Low-latency local feedback based on alternative functional split for D-RAN [Dotsch et al ‘13]

BBUMS

CU

RU

Uplink1


NACK

ACK

estimate based on look-up table or error exponents [Rost and Prasad ‘14] or finite-blocklength bounds [Khalili and Simeone ‘15]

BBUMS

CU

RU


2


NACK

ACK

estimate based on look-up table or error exponents [Rost and Prasad ‘14] or finite-blocklength bounds [Khalili and Simeone ‘15]

ACK/NAKlocal feedback

3

BBUMS

CU

RU



Local feedback by RU

Decoding outcome at CU

Action Consequence

ACK/NAK Correct/Incorrect None None

ACK Incorrect ARQ retransmission (higher layer)

Delay

NAK Correct None Delay

55

Performance Evaluation

S. Khalili and O. Simeone, “Uplink HARQ for C-RAN via Low-Latency Local Feedback over MIMO Finite-Blocklength Links,” arXiv:1508.06570.

• Throughput: number of correctly delivered bits/ total transmission time

• Probability of success: probability of ACK transmission and successful CU decoding by the maximum number of allowed transmission attempts

• Analysis based on finite-blocklength bounds [Polyanskiy et al ‘10]

Numerical Results

Blocklength

r = 3 bit/s/Hz

r = 1 bit/s/Hz

Numerical ResultsNmax = 5, s = 3 dB, k = 50 and r = 3 bit/symbol

1 2 3 4 5 6 7 80

1

2

3

4

5

6

7

8

9

Number of antennas

Thro

ughp

ut lo

ss [%

]

SIMO

MIMO

D-RAN

RU 1

RU 2

BBU 1

Fronthaul links

BBU 2

CU

D-RAN vs. C-RAN

• Multi-bit local feedback from RUs to user + user-centric decision

RU 1

RU 2

BBU 1

Fronthaul links

BBU 2

RU 1

RU 2

Fronthaul links

CUCU

BBU

60





o Conclusions

Conclusions• Key advantages of C-RAN derive from the virtualization of base

station functionalities.

• Main challenges: Capacity and latency limitations of fronthaul network

• Some solutions:– Quantization and compression inspired by network information

theoretic principles (see also uplink, multi-hop,…)– Alternative functional splits– Local low-latency feedback

• Open issues: random access, interplay with caching,…

61

Extra Slides

62

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

• The MSs use nested lattice codes:

[B. Nazer]

63

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

64

• Three-cell SISO circular Wyner model

Numerical Results

CU

CCC

65

Numerical Results

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

66

Numerical Results

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

67

• 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

68

• Three-cell SISO circular Wyner model

Numerical Results

CU

CCC

69

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

70



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


71



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


72

-

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

73

• Block implementation: Successive estimation-compression architecture

74

Network-Aware Fronthaul Compression

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

75

• 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

76

• 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

(1 ) ( )k k kR R R t

t kR

where [0,1]: the forgetting factor.

77

3 dB, 5, 2 bits/symbol, 50, 1 and 1max t rs n r k m m

Numerical Results

CU

1st2nd3rdnthmaxn

HARQ-TI

CU

1st2nd3rdmaxn nth

HARQ-CC

CU

1st2nd3rd

maxn nth

HARQ-CC

CU

1st2nd3rdmaxn nth

HARQ-IR

CU

1st2nd3rdnthmaxn

HARQ-IR

CU

1st2nd3rdmaxn nth

HARQ-IR

1st2nd3rdmaxn nth

HARQ-IR

CU

• Information-theoretic analysis assumes long encoding blocks• Optimization via successive convex approximation• Symbol-by-symbol quantization: multivariate quantization as vector

quantization with product codebook -- Lloyd-Max-like design [Gersho and Gray ‘92]

Multivariate Quantization: Design

92

. . .

. . .

. . .

Design Constraint

93

C.E.

P

RU

C.E.

Q

RUQ

CSI

C.E.

P

RU

C.E.

RU

CSI

M.Q.

CSI

Point-to-point quantization Multivariate quantization

• Add complexity only at the cloud and not at the RUs

Design Constraint

94

C.E.

P

RU

C.E.

Q

RUQ

CSI

C.E.

P

RU

C.E.

RU

CSI

Multivariate quantization

quantization (transmission) levels

M.Q.

CSI

Point-to-point quantization

• Add complexity only at the cloud and not at the RUs

C-RAN Challenges and Solutions v8 · Cloud Radio Access Networks • Fronthaullinks carry...

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Transcript of C-RAN Challenges and Solutions v8 · Cloud Radio Access Networks • Fronthaullinks carry...