Baseband LTE Compression
Jinseok Choi and Brian L. Evans
Wireless Networking & Communication Group
The University of Texas as Austin
Collaboration with Robert W. Heath, Jr. , and Jeonghun Park
1
Traditional Radio Access Network
Network Trend
B
S
RRH
BBU
•
•
•
UE
B
S
B
S
Rapidly growing mobile traffic
Dense antenna deployment
Cell size reduction
Limitations
•
•
Interference
High operation and capital expenditures
B
S
B
S
[Ericsson, Akamai, 2013]
RRH Remote radio head
BBU Baseband processing unit
UE User equipment
2
Cloud Radio Access Network
RRH
B
S
BBU
UE
Cloud Radio Access Networks (C-RANs)
•
•
•
•
Separate radio heads and baseband proc. units
Share processing resources in the cloud
Increase energy efficiency vs. traditional RANs
Support growing mobile traffic
Radio Interface
• Transports complex-baseband wireless samples
• Needs expensive link to support high data rates
cloud
B
S
B
S
B
S
B
S
RRH Remote radio head
BBU Baseband processing unit
UE User equipment
3
Challenge: Fronthaul Capacity Constraints
Data Rates Per Sector
fronthaul links
LTE Bandwidth
Number of
Antennas
10 MHz
20 MHz
2
1.2288 Gbps
2.4576 Gbps
4
2.4578 Gbps
4.9512 Gbps
8
4.9512 Gbps
9.8304 Gbps
• Very expensive links
• Poor scalability to LTE-A (100 MHz)
Compress baseband IQ samples
before sending over fronthaul links
4
Solution 1: Time-Domain Compression
[Nieman & Evans, 2013]
Lloyd-Max quantization
MMSE quantization for Gaussian signals
W antennas
Noise shaping filter
5th-order IIR Chebychev Type II filter that pushes noise power
to the guard band
*Operations in uplink are reciprocal
Lloyd-Max Quantization
• Minimizes MSE for a probability density function
• Derives quantization levels in closed-form
Noise Shaping
• Shapes quantization noise to guard band
• Increases SQNR
Noise Shaping Effect
5
Validation: Time-Domain Compression
Channel Quality Index (CQI) = 15
Bandwidth = 5 MHz
Ped. A Channel
Contributions
• Achieves 3x compression
• Keeps an error vector magnitude (EVM) < 2%
[Nieman & Evans, 2013]
Channel Quality Index (CQI) = 15
Bandwidth = 1.4 MHz
Ped. A Channel
Limitations
• Each antenna baseband IQ stream is separately compressed
6
Solution II: Spatial Domain Compression
 Main idea
- To exploit space-time correlation between antennas
- Can be applied to LTE uplink
 Split point
- Time-domain I/Q samples
- To reduce complexity
Split Point
Spatial
domain
compres
s
CPRI
Spatial
domain
compres
s
7
System Model
LTE Uplink: Single Carrier FDMA
• Localized Frequency Domain Multiple Access
A
B
C
D
DFT
(M)
IDFT
(N)
Single-Antenna UEs
•
Frequency Domain
yM r
sT
Mr Antennas/RRH
• Received Signals in Time-Domain
Small cell size and densely deployed RRHs with the large # of antennas each, result in correlated received signals.
Intuition: exploit space-time correlation to compress baseband LTE samples
8
Solution II: Spatial Domain Compression
Remote Radio Equipment
(a)
y
A
F
E
Base Station Processor
Compression Block
(b)
1
Y
(M r ´ Nb )
PCA
Dimension
Reduction
Vth Tth
Adaptive
Quantization
Link
Dequantization
+
PCA
Decompression
ŷ
Ŷ(M
r
´ Nb )
1
PHY
Proces
s
PHY
Process
Joint
Symbol
Detectio
n
(a)
Principal Component Analysis (PCA)
Ŷ(M r ´ Nb ) =
Vth
(M r ´ th)
x
Tth (th ´ Nb )
•
•
•
Forms received signal matrix of OFDM samples
V is an eigenvector matrix
• Original received signal matrix Y = [ym,n ] λ M ´N
Mr
T is a de-correlated matrix
•
T T = V TY ,
Achieves low-rank approximation by keeping
only major principal components
r
b
ym,n = å vm,i ×ti,n
i=1
• Low-rank approximation for data matrix
th
ŷm,n = å vm,i × t i,n
i=1
th < M r
9
Solution II: Spatial Domain Compression
Remote Radio Equipment
(a)
y
A
F
E
Base Station Processor
Compression Block
(b)
1
Y(M
r
´ Nb )
PCA
Dimension
Reduction
Vth Tth
Adaptive
Quantization
Link
ŷ
Dequantization
+
PCA
Ŷ(M
Decompression
r
´ Nb )
1
PHY
Proces
s
PHY
Process
Joint
Symbol
Detectio
n
(b)
Adaptive Quantization-Bit Allocation
•
Vth Tth
Q
Adaptively allocate quantization bits
Link
Q-1
Baseband
Processing
- Based on quantization noise power
D2
R
2
s Qnoise =
D = b , R = ti,max - ti,min
12
2
•
•
T is a de-correlated matrix
T T = V TY ,
- ti will have lower amplitude as i increases
R for eigenvector is fixed as -1 to 1
- Unitary vector, Qv is adaptively selected
Compression Rate (CR)
CR =
(M r × N b + a )Q
th
th
i=1
i=1
M r å QVi + N b å QTi + bQ
Q: Quantization Bits
a , b : Quantization Information
10
Validation – Link Level Simulation
Simulation Setting
• Modulation
- 64 QAM
Parameters for LTE Transmission
Transmission BW
[MHz]
1.4
3
5
10
15
20
Occupied BW [MHz]
1.08
2.7
4.5
9.0
13.5
18.0
Guardband [MHz]
0.32
0.3
0.5
1.0
1.5
2.0
Sampling
Frequency [MHz]
1.92
3.84
7.68
15.36
23.04
30.72
FFT size
128
256
512
1024
1536
2048
-12 blocks each (total 48/50)
# of occupied
subcarriers
72
180
300
600
900
1200
• Compression Block Length
# of resource blocks
6
15
25
50
75
100
# of CP samples
(normal)
9x6
10 x 1
18 x 6
20 x 1
36 x 6
40 x 1
72 x 6
80 x 1
108 x 6
120 x 1
144 x 6
160 x 1
# of CP samples
(extended)
32
64
128
256
384
512
• # of Antennas
- 8 /16 / 32 / 64 cases
• # of Users
- 4 users
• Resource blocks per User
- Nr = 1096 (1024+CP)
• Channel
- Ped. A channel model
[Fundamentals of LTE, Arunabha Ghosh, Jun Zhang, Jeffery G. Andrews, Rias Muhamed, 2010]
11
Validation – 32 Antennas
(M r = 32 Nb = 1096)
# of eigenvectors = 16
QV ,QT = [9 9 9 9 8 8 8 8 7 7 6 6 6 5 5]
Analysis
• Matrix Degree of Freedom = 16 (4 channel taps, 4 users)
- Low Rank Approximation is effective
Compression + Noise Reduction
- Adaptive Quantization-Bit Allocation is effective
• Achieves 4.0x compression with 0.3% EVM gain
12
Validation – 64 Antennas
(M r = 64 Nb = 1096)
# of eigenvectors = 16
QV ,QT = [9 9 9 9 8 8 8 7 7 6 6 6 6 5 5 5]
Noise Reduction
Analysis
• Matrix Degree of Freedom = 16 (4 channel taps, 4 users)
- Low Rank Approximation is very effective
Compression + effective Noise Reduction
- Adaptive Quantization-Bit Allocation is very effective
• Achieves 8.0x compression with 0.5% EVM gain
13
Compression Ratio vs. Estimated Complexity
Compression Rate
vs. Estimated Complexity
UPLINK
8
PCA 64-Rx
Compression Rate
7
PCA 32-Rx
PCA 16-Rx
PCA 8-Rx
6
Nieman & Evans, 13
Guo, et al, 12
Samardzija, et al, 12
Nanba & Agata, 13
Vosoughi, Wu & Cavallaro, 12
Ren, et al, 14
5
4
3
2
1
0
* Arithmetic complexity
before quantization
20
40
60
80
100
Computational Complexity / Sample
120
140
160
14
Compression Ratio vs. Estimated Complexity
DOWNLINK
4
Nieman & Evans, 13
Guo, et al, 12
Samardzija, et al, 12
Vosoughi, Wu & Cavallaro, 12
Ren, et al, 14
Compression Rate
3.5
3
2.5
2
1.5
1
0
* Arithmetic complexity
before quantization
20
40
60
80
100
Computational Complexity / Sample
120
140
15
Contribution & Limitation
Spatial Domain Compression
•
•
•
•
Achieves 1.9x / 2.5x / 4.0x / 8.0x compression for 8 / 16 / 32 / 64-antenna cases with 4 users
Draws noise reduction effect in some favorable network environment
Proposes possible solution for future communication network trend
Develops fast algorithm based on power method to find major principal components
Future Work
•
•
Determination of Optimal Block Size, Quantization-Bit Numbers
Development of Spatial Compression Algorithm with 2 to 8 antennas
- Slepian Wolf Coding: Separate encoding is as efficient as joint encoding
Block diagram for Slepian-Wolf coding: independent encoding of two correlated data streams.
H: entropy
16
Thank you
18
Cloud RAN - future
RRH
B
S
BBU
UE
Key Assumption
• CRAN in Massive MIMO Environment
-
5Generation (mmWave)
# of Antennas of RRH: Large Mr
Smaller Cell Size
# of Antennas >># of Users
Solution
• Spatial Domain Compression
- To achieve large compression rate with large #
of antennas
Mr
B
S
B
S
B
S
B
S
18
Validation – 8 Antennas
(M r = 8 Nb = 1096)
Analysis
• Matrix Degree of Freedom = 8 (4 channel taps, 4 users)
- Low Rank Approximation is very poor
- Adaptive Quantization-Bit Allocation is still possible
# of eigenvectors = 8
QV ,QT = [9 9 8 8 7 7 6 6 ]
• Achieves 1.9x compression with 0.3% EVM loss
19
Validation – 16 Antennas
(M r = 16 Nb = 1096)
Analysis
• Matrix Degree of Freedom = 16 (4 channel taps, 4 users) # of eigenvectors = 13
- Low Rank Approximation is poor
- Adaptive Quantization-Bit Allocation is possible
QV ,QT = [9 9 9 8 8 8 7 7 7 6 6 6 6 ]
• Achieves 2.5x compression with 0.5% EVM loss
20
References
1. Nieman & Evans, 13
-
Lloyd-Max Quantization
Noise Shaping
2. Guo, et al, 12
-
Resampling
Block Scaling
-
Resampling
3.0x Compression (5.3x in Theory) (UL & DL)
-
Non-Linear Quantization
3.3x Compression (UL & DL)
-
Non-Linear Quantization
Dithering Signals in Multi-link Case
1. Samardzija, et al, 12
-
Resampling
Block Scaling
-
3.0x Compression (UL & DL)
1. K. Nieman and B. Evans, “Time-domain compression of complex-baseband LTE signals for cloud radio access networks,” in Global
Conference on Signal and Information Processing (GlobalSIP), 2013 IEEE, Dec 2013, pp. 1198–1201.
2. Guo, Bin, et al. "CPRI compression transport for LTE and LTE-A signal in CAN."Communications and Networking in China
(CHINACOM), 2012 7th International ICST Conference on. 2012.
3. Samardzija, Dragan, et al. "Compressed transport of baseband signals in radio access” Wireless Communications, IEEE Transactions
on 11.9 (2012): 3216-3225
21
References (Cont’d)
4. Nanba & Agata, 13
-
I/Q Sample Width Reduction Free Lossless Audio Codec
2.0x Compression (UL)
5. Vosoughi, Wu & Cavallaro, 12
-
Lossless Compression
Sample Quantizing
-
2.0x ~ 3.5x Compression (UL)
2.3x ~ 4.0x Compression (DL)
-
3.3x Compression (UL & DL)
4. Ren, et al, 14
-
Down Sampling
Modified Block AGC
4. Nanba, Shinobu, and Akira Agata. "A new IQ data compression scheme for front-haul link in centralized RAN.” Personal, Indoor and
Mobile Radio Communications (PIMRC Workshops), 2013 IEEE 24th International Symposium on. IEEE, 2013.
5. Vosoughi, Aida, Michael Wu, and Joseph R. Cavallaro. "Baseband signal compression in wireless base stations." Global
Communications Conference (GLOBECOM), 2012 IEEE. IEEE, 2012.
6. Ren,Yuwei, et al. "A compression method for LTE-A signals transported in radio access networks." Telecommunications (ICT), 2014
21st International Conference on. IEEE, 2014
22