C-RAN Challenges and Solutions v8simeone/CRANF15.pdf · M1 Channel encoder 1 Precoding RU 1 Central...
Transcript of 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
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
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Cloud Radio Access Networks
• 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
Cloud Radio Access Networks
• 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
Cloud Radio Access Networks
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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
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
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
MNM Channelencoder NM
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
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
Is Point-to-Point Quantization Optimal?
1C1M Channel
encoder 1
Precoding
RU 1
MNM Channelencoder NM
1s
MNs
Quantization1x
BNxBNC
1x
RU BNx
BN
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Quantization
CSI
Central Unit
fronthaul
1C1M Channel
encoder 1
Precoding
RU 1
MNM Channelencoder NM
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
Point-to-Point Quantization
2x
1x
21
Point-to-Point Quantization
2x
1x
22
Point-to-Point Quantization
2x
1x
23
2x
1x
quantization noise = interference
Point-to-Point Quantization
24
h
h
Point-to-Point Quantization
signal space
• The impact of interference depends on CSI.
25
h
h
affects MS
does not affect MS
Point-to-Point Quantization
signal space
1C1M Channel
encoder 1
Precoding
RU 1
Central Unit
MNM Channelencoder NM
1s
MNs
1x
BNxBNC
1x
RU BNx
BN
Multivariatequantization
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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
Multivariate Fronthaul Quantization
signal space
28
h
h
Multivariate Fronthaul Quantization
signal space
1C1M Channel
encoder 1
Precoding
RU 1
Central Unit
MNM Channelencoder NM
1s
MNs
1x
BNxBNC
1x
RU BNx
BN
Multivariatequantization
29
Multivariate Fronthaul Quantization
CSI CSI
Signal space
• Dual role of precoding and multivariate quantization
Quantization noise space
1C1M Channel
encoder 1
Precoding
RU 1
Central Unit
MNM Channelencoder NM
1s
MNs
1x
BNxBNC
1x
RU BNx
BN
Multivariatequantization/ compression
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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
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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
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
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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
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
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
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[Dötsch et al ’13][Wübben et al ’14]
Alternative Functional Splits
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
[Dötsch et al ’13][Wübben et al ’14]
Alternative Functional Splits
Downconv Upconv
Sync + FFT
Resource demapping
Receiveprocessing
Transmitprocessing
Resourcemapping
IFFT
PHY
MAC
RF
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-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
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
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
Uplink (H)ARQ in D/C-RAN
CU
44
RU 1
RU 2
BBU(s)
MS 1
MS 2
Uplink (H)ARQ in D/C-RAN
CU decoding3
CU
45
RU 1
RU 2
BBU(s)
MS 1
MS 24
Uplink (H)ARQ in D/C-RAN
ACK/NAKfronthaul transfer
CU
46
RU 1
RU 2
BBU(s)
MS 1
MS 2
ACK/NAKfeedback
5
Uplink (H)ARQ in D/C-RAN
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
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
Low-Latency Local Feedback
• Low-latency local feedback based on alternative functional split for D-RAN [Dotsch et al ‘13]
BBUMS
CU
RU
Uplink1
Low-Latency Local Feedback
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
• Low-latency local feedback based on alternative functional split for D-RAN [Dotsch et al ‘13]
2
Low-Latency Local Feedback
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
• Low-latency local feedback based on alternative functional split for D-RAN [Dotsch et al ‘13]
Low-Latency Local Feedback
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
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
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
Point-to-point compression
Single-cell processing
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
MNM Channelencoder NM
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
Point-to-point compression
Linear precoding
Single-cell processing
• Three-cell SISO circular Wyner model ( and )
Numerical Results20 dBP 0.5
70
• Three-cell SISO circular Wyner model ( and )
Numerical Results20 dBP 0.5
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
Point-to-point compression
DPC precoding
Linear precoding
Single-cell processing
71
• Three-cell SISO circular Wyner model ( and )
Numerical Results20 dBP 0.5
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
Point-to-point compression
DPC precoding
Compute-and-forward
Linear precoding
Single-cell processing
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
3 dB, 5, 2 bits/symbol, 50, 1 and 1max t rs n r k m m
Numerical Results
CU
1st2nd3rdnthmaxn
HARQ-TI
CU
1st2nd3rdnthmaxn
HARQ-TI
CU
1st2nd3rdnthmaxn
HARQ-TI
CU
1st2nd3rdnthmaxn
HARQ-TI
CU
1st2nd3rdmaxn nth
HARQ-CC
CU
1st2nd3rd
maxn nth
HARQ-CC
CU
1st2nd3rd
maxn 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