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

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Traditional Radio Access NetworkNetwork Trend• Rapidly growing mobile traffic• Dense antenna deployment• Cell size reductionLimitations• Interference• High operation and capital

expenditures

[Ericsson, Akamai, 2013]

BS

BS

BS

BS

RRH BS BBU UE

RRH Remote radio headBBU Baseband processing unitUE User equipment

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Cloud Radio Access Network

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

RRH BS BBU UE

BS

BS B

SBS

cloudfronthaul

RRH Remote radio headBBU Baseband processing unitUE User equipment

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Challenge: Fronthaul Capacity Constraints

fronthaul links

• Very expensive links• Poor scalability to LTE-A (100 MHz)

Compress baseband IQ samples before sending

over fronthaul links

Number of Antennas

LTE Bandwidth

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

Data Rates Per Sector

5

[Nieman & Evans, 2013]

MMSE quantization for Gaussian signals

Lloyd-Max quantization

5th-order IIR Chebychev Type II filter that pushes noise power to the guard band

Noise shaping filterW antennas

*Operations in uplink are reciprocal

Noise Shaping Effect

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

Solution 1: Time-Domain Compression

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[Nieman & Evans, 2013]Validation: Time-Domain Compression

Contributions• Achieves 3x compression• Keeps an error vector magnitude (EVM) < 2%

Limitations• Each antenna baseband IQ stream is separately compressed

Channel Quality Index (CQI) = 15Bandwidth = 5 MHzPed. A Channel

Channel Quality Index (CQI) = 15Bandwidth = 1.4 MHzPed. A Channel

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CPRI

Spatial domain compres

s

Spatial domain compres

s

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

Solution II: Spatial Domain Compression

Split Point

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LTE Uplink: Single Carrier FDMA• Localized Frequency Domain Multiple Access

A B C D DFT(M)

IDFT(N)

• Frequency Domain • 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

System Model

Single-Antenna UEs Mr Antennas/RRH

= x

Solution II: Spatial Domain Compression

(a)

AFE

PCADimensi

on Reductio

n

Link

Remote Radio Equipment

PHY Proce

ssJoint

SymbolDetecti

on

Dequantization+

PCA Decompressio

n

PHY Proce

ss

Base Station Processor

Adaptive Quantizati

on

Compression Block(a) (b)

• Forms received signal matrix of OFDM samples

• V is an eigenvector matrix• T is a de-correlated matrix

• Achieves low-rank approximation by keeping only major principal components

Principal Component Analysis (PCA)

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• Original received signal matrix

• Low-rank approximation for data matrix

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Solution II: Spatial Domain Compression

Compression Rate (CR)

Q Q-1Link Baseband

Processing

(b)

: Quantization Information: Quantization Bits

Adaptive Quantization-Bit Allocation• Adaptively allocate quantization bits - Based on quantization noise power

• T is a de-correlated matrix

- ti will have lower amplitude as i

increases

• R for eigenvector is fixed as -1 to 1

- Unitary vector, Qv is adaptively

selected

AFE

PCADimensi

on Reductio

n

Link

Remote Radio Equipment

PHY Proce

ssJoint

SymbolDetecti

on

Dequantization+

PCA Decompressio

n

PHY Proce

ss

Base Station Processor

Adaptive Quantizati

on

Compression Block(a) (b)

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• Modulation - 64 QAM

• # of Antennas - 8 /16 / 32 / 64 cases

• # of Users - 4 users

• Resource blocks per User -12 blocks each (total 48/50)

• Compression Block Length

- Nr = 1096 (1024+CP)

• Channel - Ped. A channel model

Validation – Link Level Simulation

Simulation SettingParameters 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

# of occupied subcarriers

72 180 300 600 900 1200

# of resource blocks

6 15 25 50 75 100

# of CP samples (normal)

9 x 610 x 1

18 x 620 x 1

36 x 640 x 1

72 x 680 x 1

108 x 6

120 x 1

144 x 6

160 x 1

# of CP samples (extended)

32 64 128 256 384 512[Fundamentals of LTE, Arunabha Ghosh, Jun Zhang, Jeffery G. Andrews, Rias Muhamed, 2010]

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Validation – 32 Antennas

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

Noise Reduction

Info. loss

<Comment>Matrix Rank = 16 (w.o. noise)

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Validation – 64 Antennas

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

Noise Reduction

Info. loss

<Comment>Matrix Rank = 16 (w.o. noise)

Compression Ratio vs. Estimated Complexity

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* Arithmetic complexity before quantization

UPLINK

PCA 64-Rx

PCA 32-Rx

PCA 16-Rx PCA 8-Rx

Guo, et al, 12Nieman & Evans, 13

Samardzija, et al, 12

Nanba & Agata, 13Vosoughi, Wu & Cavallaro, 12Ren, et al, 14

Compression Ratio vs. Estimated Complexity

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* Arithmetic complexity before quantization

Guo, et al, 12Nieman & Evans, 13

Samardzija, et al, 12Vosoughi, Wu & Cavallaro, 12Ren, et al, 14

DOWNLINK

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• 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

Spatial Domain Compression

Contribution & Limitation

• 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

Future Work

Block diagram for Slepian-Wolf coding: independent encoding of two correlated data streams.H: entropy

Thank you

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Cloud RAN - future

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

RRH BS BBU UE

BS

BS B

SBS

Mr

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Validation – 8 Antennas

Analysis• Matrix Degree of Freedom = 8 (4 channel taps, 4 users) - Low Rank Approximation is very poor - Adaptive Quantization-Bit Allocation is still possible• Achieves 1.9x compression with 0.3% EVM loss

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Validation – 16 Antennas

Analysis• Matrix Degree of Freedom = 16 (4 channel taps, 4 users) - Low Rank Approximation is poor - Adaptive Quantization-Bit Allocation is possible• Achieves 2.5x compression with 0.5% EVM loss

<Comment>Matrix Rank = 16 (w.o. noise)

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References1. Nieman & Evans, 13

- Lloyd-Max Quantization- Noise Shaping

2. Guo, et al, 12- Resampling- Block Scaling

3. Samardzija, et al, 12- Resampling- Block Scaling

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

- 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

- 3.0x Compression (UL & DL)

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References (Cont’d)4. Nanba & Agata, 13

- I/Q Sample Width Reduction- Free Lossless Audio Codec

5. Vosoughi, Wu & Cavallaro, 12- Lossless Compression- Sample Quantizing

6. 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

- 2.0x Compression (UL)

- 2.0x ~ 3.5x Compression (UL)- 2.3x ~ 4.0x Compression (DL)

- 3.3x Compression (UL & DL)