Fronthaul Compression for Cloud Radio Access Networks O. Simeone - PowerPoint PPT Presentation

Fronthaul Compression for Cloud Radio Access Networks O. Simeone New Jersey Institute of Technology (NJIT) Joint work with S.-H. Park 1 , O. Sahin 2 and S. Shamai 3 3 1 2

Cloud Radio Access Networks Base stations operate as radio units • Baseband processing takes place in the “cloud” • Fronthaul links carry complex (IQ) • baseband signals

Cloud Radio Access Networks Advantages: • Low-cost BSs • Effective interference mitigation via joint baseband processing Key challenge: Effective transfer of the IQ signals on the fronthaul links

Cloud Radio Access Networks • CPRI standard based on ADC/DAC … Need for fronthaul compression

State of the Art • Point-to-point fronthaul compression: – Algorithms [Segel and Weldon] [Samardzija et al ‘12] [Nieman and Evans ’13] – Testbed results [Irmer et al ’11] [Vosoughi et al ‘12]

State of the Art • Multiterminal fronthaul compression: – Uplink: Distributed source coding coding [Sanderovich et al ’09] [del Coso and Simoens ’09] [Zhou and Yu ’11] [Marsch and Fettweis ’11] – Downlink: Multivariate compression [Park et al ’13] • Compute-and-forward: – Uplink [Nazer et al ’09] [Hong and Caire ’11] – Downlink [Hong and Caire ‘12]

Overview • Uplink – Multiterminal compression – Compute-and-forward • Downlink – Multiterminal compression – Compute-and-forward • Performance evaluation • Extensions and conclusions

Overview • Uplink – Multiterminal compression – Compute-and-forward • Downlink – Multiterminal compression – Compute-and-forward • Performance evaluation • Extensions and conclusions

System Model H 1 y ˆ 1 RU1 C y 1 MS 1 1 H i ˆ i C y CU i RU i y i MS N M C N B RU N H B ˆ y N B N B y N B Single-cluster single-hop fronthaul topology

Point-to-Point Fronthaul Compression Control Unit RU 1 C ˆ ul y 1 1 Decompressor Compressor ul y 1 Fronthaul RU 2 ˆ ul y C 2 2 Decompressor Compressor ul y Decoder 2 Fronthaul RU N R ul ˆ y C N N R R ul y Decompressor Compressor N R Fronthaul

Joint Fronthaul Decompression [Sanderovich et al ’09] [del Coso and Simoens ’09] [Zhou and Yu ’11] [Park et al ’13] Control Unit RU 1 C 1 ul y ˆ  ul y Compressor (1)  (1) Decompressor Fronthaul RU 2 C 2 ul y ˆ  WZ WZ ul y (2)  (2) Compressor Decoder Decompressor Fronthaul RU N R C N R WZ ul y WZ  ( ) N R Compressor ˆ ul y Fronthaul Decompressor  ( ) N R

Joint Fronthaul Decompression 010 001 … … 000 ul y 100 ul ˆ y 101 Point-to-point compression

Joint Fronthaul Decompression 010 001 … … 000 ul y 100 ul 101 ˆ y WZ compression … Coset coding at the RU and channel decoding at the CU [Pradhan and Ramchandran ’03]

Compute-and-Forward [Nazer et al ’09] [Hong and Caire ’11] • The MSs use (nested) lattice codes: [B. Nazer]

Compute-and-Forward [Nazer et al ’09] [Hong and Caire ’11] Control Unit RU 1 C 1 Integer ul y Decoder 1 Fronthaul RU 2 C 2 Integer ul y 2 Decoder Decoder Fronthaul RU N R C N R Integer ul y N R Decoder Fronthaul

Numerical Results • Three-cell SISO circular Wyner model CU C C C     

Numerical Results   3 bit/s/Hz and =0.4 C 3 Cut-set upper bound 2.5 per-cell sum-rate [bits/s/Hz] Point-to-point compression 2 Single-cell processing 1.5 1 0 5 10 15 20 25 30 MS transmit power [dB]

Numerical Results   3 bit/s/Hz and =0.4 C 3 Cut-set upper bound Joint decompression 2.5 per-cell sum-rate [bits/s/Hz] Point-to-point compression 2 Single-cell processing 1.5 1 0 5 10 15 20 25 30 MS transmit power [dB]

Numerical Results   3 bit/s/Hz and =0.4 C 3 Cut-set upper bound Joint decompression 2.5 per-cell sum-rate [bits/s/Hz] Point-to-point compression 2 Single-cell processing 1.5 Compute-and-forward 1 0 5 10 15 20 25 30 MS transmit power [dB]

Overview • Uplink – Multiterminal compression – Compute-and-forward • Downlink – Multiterminal compression – Compute-and-forward • Performance evaluation • Extensions and conclusions

System Model RU1 H 1 C 1 MS 1 1 ,..., M M C CU N M i RU i C N B MS N M H RU N N M B Single-cluster single-hop fronthaul topology

Point-to-Point Fronthaul Compression Control Unit C s M  x Compressor 1 x 1 Channel 1  1 RU 1  1 1  encoder 1 Precoding s  x M C Channel N N Compressor N N M x B B  M RU  N N encoder N M  B N B B

Joint Fronthaul Compression [Park et al ’13] Control Unit C s M  x 1 x 1 Channel 1  1 RU 1  1  encoder 1 Joint Precoding compression s  x M C Channel N N N N M x B B  M RU  N N encoder N M  B B

Joint Fronthaul Compression • Multivariate compression produces compressed signals with correlated quantization noises   q   x E As q H   1  y H As H z 1 1 1   z 1 1 1 1 q   1 C 2 1 RU 1 H 1,1 MS CU RU 2 H C 1,2 2   x E As q H 2 2 2    Ω Ω  1,1 1,2 CN  0 H , H  H     1 1 Ω Ω     2,1 2,2 can be reduced by controlling  Ω Ω H 1,2 2,1

Joint Fronthaul Compression x 2 x 1 Point-to-point compression

Joint Fronthaul Compression x 2 x 1 Point-to-point compression

Joint Fronthaul Compression x 2 x 1 Multivariate compression

Joint Fronthaul Compression Successive estimation-compression implementation [Park et • al ’13]:  x x    (1) (1)  RU π(1) Compressor   x ˆ  x x  (2)   (2) MMSE (2)  RU π(2)  Compressor estimation  x ˆ x x  ( )  N ( ) MMSE N  B  B ( ) RU π(N B )  N Compressor B  estimation

Compute-and-Forward • Reverse compute-and-forward (RCoF) [Hong and Caire ‘12] Control Unit C s M  x 1 x 1 Channel  1 1 RU 1  1  encoder 1 Integer precoding s  x C M N Channel N N B N M x B  M RU  N encoder N M N  B B

Numerical Results • Three-cell SISO circular Wyner model CU C C C     

Numerical Results • Three-cell SISO circular Wyner model ( and )   P  0.5 20 dB Cut-set upper bound 6 Joint compression 5 Linear precoding per-cell sum-rate [bits/s/Hz] 4 Point-to-point compression 3 2 Single-cell processing 1 0 0 2 4 6 8 10 12 14 C [bits/s/Hz]

Numerical Results • Three-cell SISO circular Wyner model ( and )   P  20 dB 0.5 Cut-set upper bound 6 DPC precoding Joint compression 5 Linear precoding per-cell sum-rate [bits/s/Hz] 4 Point-to-point compression 3 2 Single-cell processing 1 0 0 2 4 6 8 10 12 14 C [bits/s/Hz]

Numerical Results • Three-cell SISO circular Wyner model ( and )   P  20 dB 0.5 Cut-set upper bound 6 DPC precoding Joint compression 5 Linear precoding per-cell sum-rate [bits/s/Hz] 4 Compute-and-forward Point-to-point compression 3 2 Single-cell processing 1 0 0 2 4 6 8 10 12 14 C [bits/s/Hz]

Overview • Uplink – Multiterminal compression – Compute-and-forward • Downlink – Multiterminal compression – Compute-and-forward • Performance evaluation • Extensions and conclusions

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

Simulation Set-up F  Frequency reuse pattern with reuse factor for 1-cell cluster 1/ 3 • [Wang and Yeh ’11]

Numerical Results • Cell-edge throughput versus average spectral efficiency –        Uplink, 1-cell cluster, 3 pico-BS, 5 MSs, ( , ) (9,3)bps/Hz, 10, 0.5, 1/ 3 N K C C T F macro pico max 3600  =2.0 3400 5%-ile rate (cell-edge throughput) [kbps]  =1.0 3200 3000 1.6x 2800  =0.5 2600 2400  =0.25 2200 Point-to-point compression 2000 Multiterminal compression 1800 0.85 0.9 0.95 1 1.05 1.1 spectral efficiency [bps/Hz]

Numerical Results • Cell-edge throughput versus average spectral efficiency –        Downlink, 1-cell cluster, 1 pico-BS, 4 MSs, ( , ) (3,1)bps/Hz, 5, 0.5, 1/ 3 N K C C T F macro pico max 4000 3500  =0.5 5%-ile rate (cell-edge throughput) [kbps] 3000  =1.5 2x 2500 2000 1500 1000 Point-to-point compression  =0.25 500 Multiterminal compression 0 0.8 0.85 0.9 0.95 1 1.05 1.1 1.15 1.2 1.25 spectral efficiency [bps/Hz]

Overview • Uplink – Multiterminal compression – Compute-and-forward • Downlink – Multiterminal compression – Compute-and-forward • Performance evaluation • Extensions and conclusions

Multiterminal Compression with Imperfect CSI [Park et al ‘13]