C-RAN Challenges and Solutions v8simeone/CRANF15.pdf · M1 Channel encoder 1 Precoding RU 1 Central...

Cloud Radio Access Network: Challenges and (Some) Solutions

Osvaldo SimeoneNew Jersey Institute of Technology

Seok-Hwan ParkChonbuk University

Onur SahinInterDigital Shlomo Shamai

TechnionJoonhyuk Kang

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

• 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

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

• Digital transmission:digitized complex (IQ) baseband signals

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

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:

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

System Model

BNxBNC

MS 11y

MS MNMNy

fronthaul

Data streams

wireless downlink channel

CPRI-Based Implementation

1C1M Channel

encoder 1

Precoding

Central Unit

MNM Channelencoder NM

Quantization1x

BNxBNC

RU BNx

Quantization

CSIfronthaul

Compressed CPRI

1C1M Channel

encoder 1

Precoding

Central Unit

Quantization/Compression

BNxBNC

RU BNx

Quantization/Compression

CSIfronthaul

• 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

o Conclusions

Is Point-to-Point Quantization Optimal?

1C1M Channel

encoder 1

Precoding

Quantization1x

BNxBNC

RU BNx

Quantization

Central Unit

fronthaul

1C1M Channel

encoder 1

Precoding

BNxBNC

RU BNx

Multivariatequantization

Multivariate Fronthaul Quantization?

CSI CSI

Central Unit

fronthaul

Point-to-Point Quantization

quantization noise = interference

signal space

• The impact of interference depends on CSI.

affects MS

does not affect MS

signal space

1C1M Channel

encoder 1

Precoding

Central Unit

BNxBNC

RU BNx

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

signal space

1C1M Channel

encoder 1

Precoding

Central Unit

BNxBNC

RU BNx

CSI CSI

Signal space

• Dual role of precoding and multivariate quantization

Quantization noise space

1C1M Channel

encoder 1

Precoding

Central Unit

BNxBNC

RU BNx

Multivariatequantization/ compression

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

• 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

• 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

spectral efficiency [bps/Hz]

Point-to-point compressionMultivariate compression

Numerical Results• Quantization vs compression

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

11Ideal fronthaul

P = 20 dB

P = 0 dB

Multivariate compressionMultivariate quantization

o Conclusions

[Dötsch et al ’13][Wübben et al ’14]

Alternative Functional Splits

Downconv Upconv

Sync + FFT

Resource demapping

Receiveprocessing

Transmitprocessing

Resourcemapping

Conventional C-RAN split

Downconv Upconv

Sync + FFT

Resource demapping

Receiveprocessing

Transmitprocessing

Resourcemapping

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

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

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

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

o Conclusions

Fronthaul Latency Constraints

• 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

BBU(s)

Uplink

Uplink (H)ARQ in D/C-RAN

BBU(s)

Baseband fronthaul transfer

BBU(s)

CU decoding3

BBU(s)

ACK/NAKfronthaul transfer

BBU(s)

ACK/NAKfeedback

Fronthaul Latency and ARQ

1 2 3 4 5 6 7 8 9 100.55

Fronthaul Delay (slots)

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

RTT timer

subframeindex

mobile RU BU

FH latencyAir int.

processing+ frame building

Air int.FH latency

UL dataUL data

Latencybreakdown

ACK/NACK

ACK/NA

CK expected in SF n+

ACK/NACK

• Ex.: FD LTE

(Andreas Mader)CU

Low-Latency Local Feedback

Downconv Upconv

Sync + FFT

Resource demapping

Receiveprocessing

Transmitprocessing

Resourcemapping

• With alternative functionalsplits, low-latency local control

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

Downconv Upconv

Sync + FFT

Resource demapping

Receiveprocessing

Transmitprocessing

Resourcemapping

• 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]

Uplink1

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

ACK/NAKlocal feedback

Local feedback by RU

Decoding outcome at CU

Action Consequence

ACK/NAK Correct/Incorrect None None

ACK Incorrect ARQ retransmission (higher layer)

NAK Correct None Delay

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

Number of antennas

Fronthaul links

D-RAN vs. C-RAN

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

Fronthaul links

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,…

Extra Slides

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

• The MSs use nested lattice codes:

[B. Nazer]

Decoder

Control Unit

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

Fronthaul

1CInteger Decoder

Fronthaul

Integer Decoder

• Three-cell SISO circular Wyner model

Numerical Results

0 5 10 15 20 25 30

MS transmit power [dB]

Cut-set upper bound

Point-to-point compression

Single-cell processing

Numerical Results

0 5 10 15 20 25 30

MS transmit power [dB]

Cut-set upper bound

Distributed compression

Compute-and-forward

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

Compute-and-Forward

1C1M Channel

encoder 1

Integerprecoding

Central encoder

BNx BNC

RU BNx

• Three-cell SISO circular Wyner model

Numerical Results

0 2 4 6 8 10 12 140

C [bits/s/Hz]

Cut-set upper bound

Multivariate compression

Linear precoding

• Three-cell SISO circular Wyner model ( and )

Numerical Results20 dBP 0.5

0 2 4 6 8 10 12 140

C [bits/s/Hz]

Cut-set upper bound

DPC precoding

Linear precoding

0 2 4 6 8 10 12 140

C [bits/s/Hz]

Cut-set upper bound

DPC precoding

Compute-and-forward

Linear precoding

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

0 5 10 15 200

Fronthaul capacity of each edge [bps/Hz]

cutset boundDPRMF

Numerical Results

• Block implementation: Successive estimation-compression architecture

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

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

• LTE rate model [3GPP-TR-136942]

Simulation Set-up

attenuate min max

max max

0, if( ) ( ), if

k k k k

12 max max attenuate

attenuate

: 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

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

[3GPP-TR-136942, Annex A]

minattenuate

• Proportional fairness metric

– At each time , the rate is updated as

Simulation Set-up

sum-PF1

( )( )K

R tR tR

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

R t k tR k t

(1 ) ( )k k kR R R t

where [0,1]: the forgetting factor.

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

Numerical Results

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

Numerical Results

1st2nd3rdnthmaxn

HARQ-TI

1st2nd3rdnthmaxn

HARQ-TI

1st2nd3rdnthmaxn

HARQ-TI

1st2nd3rdnthmaxn

HARQ-TI

1st2nd3rdmaxn nth

HARQ-CC

1st2nd3rd

maxn nth

HARQ-CC

1st2nd3rd

maxn nth

HARQ-CC

1st2nd3rd

maxn nth

HARQ-CC

1st2nd3rdmaxn nth

HARQ-IR

1st2nd3rdnthmaxn

HARQ-IR

1st2nd3rdmaxn nth

HARQ-IR

1st2nd3rdmaxn nth

HARQ-IR

• 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

Design Constraint

Point-to-point quantization Multivariate quantization

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

Design Constraint

Multivariate quantization

quantization (transmission) levels

Point-to-point quantization

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

C-RAN Challenges and Solutions v8simeone/CRANF15.pdf · M1 Channel encoder 1 Precoding RU 1 Central...

Documents

Transcript of C-RAN Challenges and Solutions v8simeone/CRANF15.pdf · M1 Channel encoder 1 Precoding RU 1 Central...

Assignment Date Period - math-worksheet.org · ID: 1 Name_____ Assignment Date_____ Period____ Solve each equation. 1) x x 2) v v 3) n n 4) b b 5) a 6) x x 7) k k 8) p 9) x x 10)

n k n x n k n x x n x

N EW ! A gricultural P roducts Automotive, Commercial ... · Product Guide 2018 LU B R IC AN TS N EW ! A gricultural P roducts G uide *' $ J 1 MF B TF WJTJU X X X X X X N Z T UJL

Linear Programming, 1 Max c 1 *X 1 +…+ c n *X n = z s.t. a 11 *X 1 +…+ a 1n *X n b 1 … a m1 *X 1 +…+ a mn *X n b m X 1, X n 0 Standard form.

ScalingLimits of InteractingParticleSystemspi.math.cornell.edu/files/Probability/Cornell-Lec2.pdf’: B k(G n) !B k(G) withmax v2B k (Gn)d X(x n v;x ’) < ,where B k(G) (respy,B k(G

, N. Derzsy , X. Lin , B. K. Szymanski , G. Korniss June ...

ECE 6123 Advanced Signal Processing Introduction · n (x[n M+ 1] x[n M+ 2] x[n M+ 3] :::x[n])T (19) Then R B EfxB n(x B n m) g= R = RT (20) Please note that the text is for some reason

n : u 5, ¦ b& 5 b X 8 Z · n : u 5, ¦ b& 5 b _ X 8 Z ¹ B º>1 v ¥ S 5>@'¨ q) 5 G ¹ B º>3 v ¥ 3° ú'¨ 3° ; 6×

Basic Mathematics for Portfolio Management. Statistics Variables x, y, z Constants a, b Observations {x n, y n |n=1,…N} Mean.

MC-Smartケーススタディ（帳票）...è $ µ µ » Ã ¾ ® ~ ¯ Ê . $ ô4 N B SU X X X N P SJT B X B D P KQ ¾ ® s r w ó v s ¯ Ê x z ® p X M ^ p V \ q x j z . Y w 0 ^

...(a) Explain with example bitwise operators in C language. (b) Write a program using recursive function 'power' to compute xn (d) power(x,n) — if n 0 power(x,n) = x power(x,n)

B i n a n c e E x c h a n g e

Variational data assimilationdarc/training/ecmwf... · epsilon2 e =x x b x ˘N(x b;B ) P(x )= 1 p (2 p)n det (B ) exp 1 2 x x b T B 1 x x b Lecturer: Ross Bannister, thanks: Amos

Kinematics, – 2 groups Dynamics & • Haptic Simulation – 3 ... · mx bx kx 0 x b m x k m x 0 b m 2 n n 2 Natural damping ratio Critically damped when b/m=2wn n n b m 2 b km 2

Binomial test (a) p = 0.07 n = 17 X ~ B(17, 0.07) (i) P(X = 2) = 0.22437 = 0.224 using Bpd on calculator Or 17 C 2 x 0.07 2 x 0.93 15 (ii) n = 50 X ~ B(50,

X C E L B R A N D S , I N C

N K p th i phát hi n, x! lý v n b n cài c m li íchstatic.baophapluat.vn/Uploaded/Edition/pdf_082_2019_GRTV.pdf · (Trang 4) N N b b K p th i phát hi n, x! lý v n b n cài c

Measurement of the Fermi surface by quantum oscillations · surface de Fermi k x k y surface de Fermi n=0 n=1 n=2 n=3 B k x k z n=0 n=1F n=2 B k y Without B With B tubes de Landau

Proportion Practice #1. Question A n x 6 Question B n x 5.

PowerPoint Presentation - University of Minnesotahspark/csci5561_S2020/Lec...TT b b bx b b X Xb D · ¨¸¨¸ ©¹ X x x ªº¬¼1 n xi b i bxi b D Di 2 1 2 1 maximize maximize maximize

Assignment Date Period - math-worksheet.org · ID: 1 Name___ Assignment Date_ Period__ Solve each equation. 1) x x 2) v v 3) n n 4) b b 5) a 6) x x 7) k k 8) p 9) x x 10)

Linear Programming, 1 Max c 1 X 1 +…+ c n X n = z s.t. a 11 X 1 +…+ a 1n X n b 1 … a m1 X 1 +…+ a mn X n b m X 1, X n 0 Standard form.