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Bernd Haberland + Team
28.9.2012 ITG Fachtagung Wien
Base Stations in the Cloud
Content
•Objectives, Use Cases
•Traffic Scenario Example
•Overall VRAN/Mobile Cloud Architecture
•Baseband Pool (MSS-BBU) Functional Architecture
•Virtualization Concept
•MSS-BBU HW Architecture
• Virtualization Concept/Architecture options (for GP P configurations)
•Control Architecture + Resource Pooling management
•Multi-cell System Simulation to verify pooling gain s
•Summary, Next steps
• Reduce processing resources in Baseband Units (BBUs) with loadbalancing (CAPEX)
• Load profiles of different cells (Indoor/Outdoor) are different over time• Processing capacity of BBUs not anymore dimensioned according to Peak Load• Gains can be used:
• to reduce the equipped processing resources• or to create processing headroom for LTE advanced
features
• Reduce number of sites for BBUs (OPEX Reduction)
• „Low cost“ Remote Radio Head (RRH) sites
• Reduce Blocking probability of a cell
Objectives, Use Cases for Mobile Cloud
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• Reduce Blocking probability of a cell
• in case of extreme traffic situation within a cell
• causing peak processing load in distributed solution
• Can be used to support „All-in-One“ Metros with a pool
• BBU Processing Resources allocated from low to high traffic RRH sites• During week-end no traffic at the enterprise area• Processing resources reused for week-end sport events
• Operator Sharing• BBUs of different operators can share the RRH configuration
TCO today with conventional solution
Equipment
Civil work
Support equipmentSite Plan
CAPEX (40%) OPEX (60%)
TransmissionSite Rent
O&M
Power
Objectives, Use Cases for Mobile Cloud (cont.)
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Further Objectives• Easy BBU Processing Capacity upgrade without access to all sites
• Enabling Configuration for LTEadvanced technologies
• NetMiMO (CoMP), HetNet with UL CoMP, Intercell Interference Coordination
• Semistatic Multistandard Mix (LTE/UMTS) in context of standard dedicated BBUs
• M2M Communication Network of Networks included
• Hot zones of sensors, distributed sensor Networks
Dynamic Cell and Traffic Scenarios:2004 Traffic Distribution 8:00-13:00 Milano Scenario
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Click for animation
MSS-MSS-
RRH
RRH
RRH
RRH
RRH
RRH
RRHRRH
DCC
Multi-standardCloud Base Station
RRHRRH
RRH
RRH
eX2
…
……
• Remote Radio Head (RRH) Cluster: Macro, HetNet, Indoor or any mix
• RRH to MSS-BBU connectivity: 50 -100
Mobile Cloud Overall Architecture
Abis MSS-
BBUBBUDCC
DCC
MSS-
BBU
MSS-
BBU
MSS-
BBUDCC
DCC
DCC
eX2
In the Cloud (inter MSS-BBU)• on User level• on Cell level
MSS-BBU: Multi-site/standard BBUDCC: Decentralized Cloud ControlerRRH: Remote Radio Head
S1Iub
Abis
S1Iub
Abis
S1Iub
Abis
S1Iub
Abis
S1
IubA
bis
eX2: Enhanced X2 interface
Interface MSS-BBU to RRHand MSS-BBU to MSS-BBU:����Low latency, High bandwidth
Base Band Interfacing options – naming
Cell processingUser processing
signal flow (DL)
I II III
IV
FEC
QAM +multi-antennamapping
Resourcemapping
IFFT+CPin
P/S
BBtoRF
encodeto CPRI
III III
IV
FEC-1
QAM-1 +multi-antennaProcessing(e.g. MRC)
Resourcedemapping
CPout+FFT
S/P
RFtoBB
encode
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• (I): „soft-bit fronthauling“ (softbits + control info)
• (II): „subframe data fronthauling“ (frequency domain I/Q + control info)
• (III): „subframe symbol fronthauling“ (frequency domain I/Q)
• (IV): „CPRI fronthauling“ (time domain I/Q)
• (IV‘): „compressed CPRI fronthauling“ (time domain I/Q)
signal flow (UL)
IV encodeto CPRI
Links from MSS-BBU to Macro site (Downlink)
Transmission bandwidth of Macro site as given below
Process splitting at several potential interfaces
Each Macro site holds 3 sectors:
- LTE, 4 antennas, 20 or 40 MHz
Bandwidth Requirements MSS-BBU to RRH site
[Gb/s] per site "CPRI" IV' III II/100% II/30% I/100% I/30% Data*/100%
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100%
Data rate (40 MHz BW, Gb/s)
29,5 10,9 5,6 5,4 2,5 0,53 0,19 0,24
sites/10 Gb/s link
0 0 1 1 4 18 53 41
Data rate (20 MHz BW, Gb/s)
14,7 5,5 2,8 2,7 1,25 0,27 0,09 0,12
sites/10Gb/s link
0 1 3 3 8 37 111 83
• Link level protocol not yet included on I,II and III.*average Spectral efficiency:2,0 b/s/hzOverhead included
MSS-BBU Functional Architecture
RRHm
RRH1
RRH2
RRH3
RRH4
RRH5
MUX/DEMUX
MUX/DEMUX
MUX/DEMUX
S1 -MME
Spreader/Despr
Spreader/Despr
Spreader/DesprSpreader/Despr
RB 1UP 1
UP 2
eNBControlFunctions
Spreader/Despread
NBControlFunctions
Schedulerantenna
cell
PHYcell
Iub
S1 - U
RB 1UP 2
RB 1UP 3
RB 1UP 4
RB 1UP 5
MUX/*DEMUX
antenna
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RRHm
To other MSS-BBUs
Cell Signals
UP: User Processing stack eX2: Enhanced X2 *: Framer/Deframer/IFFT/FFT are assumed to be in the RRH
MAC-(e)hs/eScheduler
MAC-(e)hs/eScheduler
UP j-1
UP j
Functions
BTSControlFunctions
DCCLTE UMTS
MAC-(e)hs/eScheduler
PHYcell
eX2Abis
UP i-1
UP i
cell
Interface II
MSS-BBU Functional Architecture (2)Some definitions
UP: Dedicated User Processing (DL/UL) for LTE and U MTS to be virtualized
For LTE:
S1-MME Termination stack: ETH/IP/SCTP/S1AP ded.
S1-U Termination stack: ETH/IP/UDP/GTP-U
Uu Control Plane stack: RRC/PDCP/RLC/MAC/PHYuser
Uu User Plane stack: PDCP/RLC/MAC/PHYuser
For UMTS:For UMTS:
Iub Termination User Plane stack for HSPA:
ETH/IP/UDP/FP(HS-DSCH/e-DCH)
Uu Termination stack for HSPA: MAC (e)hs/e/PHYuser
A set of UPs of one standard can be allocated to one CEM up to its maximum bandwidth
UPi: overal number of LTE users in a Cloud Base Station
UPj: overal number of UMTS users in a Cloud Base Station
i,j: not fixed depends on bandwidth for the singular user
MSS-BBU Functional Architecture (3)
PHYcell (LTE): MUX/DEMUX function with statistica l MUX gains
---� IFFT/FFT/ CPI/CPR can be shifted to RRH (save bandwidth)
PHYcell /UMTS): Spreader/Despreader
Any UP of one standard can be allocated to any PHYcell of the same standardAny UP of one standard can be allocated to any PHYcell of the same standard
Scheduler for LTE and MAC (e)hs/e Scheduler for UM TS/HSPA
RRHm: overal number of sites/RRHs connected
Virtualization Principle of a UP (example LTE)
UP1…UPx
BBU2
UPx+1… UPn
Cell Control Functions
DCCS1-MME S1-U
MSS-BBU internal connection
Inter MSS-BBU via eX2 IF
DCC
MME/SGW
Router
Scheduler
BBU1
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Some definitions:- For the users 1..x the BBU1 is the Home and the Serving BBU- For the users x+1..n the BBU1 is the Home BBU and the BBU2 the Remote BBU - For the users n+1…n+y the BBU1 is the Home BBU and the BBU3 the Remote BBU
PHY cell BBU3UPn+1… UPn+yInter MSS-BBU
via eX2 IF
DCCScheduler
MSS-BBU1
MSS-BBU2
XFPXFPXFPXFP
Poosible MSS-BBU HW Architecture (for Light Radio)
BBU2 BBU3
RadioSite k-1,k
BBUk
Radio Sitek+1
RadioSite 5,6
RadioSite 3,4
MUXarray BBU1
Cell/User Signal (from other MSS-
XFPXFP XFPXFP XFP
Radio Sitek+2
Radio Site m
XFP
RadioSite 1,2
XFP
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Abis Iub S1-U
Router/Address Dispatcher
DCC
S1-MME
eX2 IF
eX2to other
MSS-BBUs
LTE+GSM
WCDMA + GSM
MSS-BBU)
User Level
Ethernet Switch
Highspeed Interconn.
Basic Control Architecture and S1/X2 flows
DCC DCCeX2eX2
ControlControlDCC
RouterS1 RouterS1
S1 data
S1 datato/from X2
S1 data to/from X2
S1 data
RouterS1
S1 data to/from X2
S1 data
M SoC
C+M SoC LRMM SoC
C+M SoC LRMCEM
C+M SoC LRMBBU
BBU LRM
MSS-BBU1
M SoC
C+M SoC LRMM SoC
C+M SoC LRMCEM
C+M SoC LRMBBU
BBU LRM
MSS-BBU2
eX2eX2
Data Data M SoC
C+M SoC LRMM SoC
C+M SoC LRMCEM
C+M SoC LRMBBU
BBU LRM
MSS-BBUn
S1 datafrom/to X2
S1 datafrom/to X2
S1 datafrom/to X2
LRM: Local Resource Manager
Control Flow Architecture
RBC: Radio Bearer Control
Multi-cell System SimulationQuantification of Possible Processing Resource Savings
100 %
? %
Controller
Mapping of processing effort
KPIs
Mapping of transport format
to processing effort
Controller
Non full buffer
traffic generator
Modem
SchedulerControllerto transport
format
MSS-BBU Processing Resources
Scenario and Basic Simulation Parameters
Parameter Value
Scenario LTE, 10 MHz uplink, 10 MHz downlink FDD
Playground 4.1 km2, 19 sites, 500 m distance, 3 sectors per site
Mobiles Unlimited number, not moving
Request messages Created on application layer according to a Poisson inter-arrival process; constant message size of 1 kB
Load distribution Randomly according to a mix of Uniform & Gaussian, mean in central site
Response messages One per request; varying object sizes according to a mix of 3 log-normals [Hernandez-Campos]
Channel model Pathloss and static shadowing
Handover Based on SINR
Outage Requests from mobiles dropped if SINR below -6 dB
Scheduling Frequency-diverse allocation cell reuse one scheme, Resource-fair round-robin bandwidth assignment
Link adaptation Capacity calculated according to Shannon formula, no losses, no HARQ/ARQ
Processing resource model
Processing resources scale linearly with the share of allocated radio resources
[Hernandez-Campos] F. Hernandez-Campos et al., Variable heavy tails in Internet traffic, Performance Evaluation, Vol. 58.2–3, page 261-284, Nov. 2004
Geographic User Load Distributions
• After request generation, the position of the associated user is chosen according to a mix of Uniform and Gaussian parameters
• Single hotspot in the center to model non-uniform load distribution
• Varying share of hotspot users varies the corresponding spatially non-uniform load distributions and their spatial variance (hotspot size (standard deviation) not varied)
100 % uniform 80 % uniform 50 % uniform
deviation) not varied)
• 100% hotspot users: less than 20% resource utilization for 90% of the time and less than 38% for 99.9% of the time
• 100% uniformly placed users: less
Pooling Gains Resulting from User Load Distribution and Traffic Model
• Quantiles for 90%, 95%, 99% and 99.9% of the system time at its capacity limit.
60%
70%
80%
90%
100%
use
d r
eso
urc
es
99,9%tile
99%tile
95%tile
90%tile
than 65% resource utilization for 90% of the time and less than 91% resource utilization for 99.9% of the time
� Expected improvement up to a factor of 3 of the resource utilizations by the cloud approach between the uniform to non-uniform user distribution cases
0%
10%
20%
30%
40%
50%
60%
0 0,2 0,4 0,6 0,8 1fraction of uniformly placed users
use
d r
eso
urc
es
(contributed from Dipl.-Ing. Thomas Werthmann from the Institute of Communication Networks and Computer Engineering in Stuttgart, Germany)
Resource Usage Trace for 100 % Uniformly Placed Users – Sum Over All Cells
(contributed from Dipl.-Ing. Thomas Werthmann from the Institute of Communication Networks and Computer Engineering in Stuttgart, Germany)
Resource Usage Trace for 50 % Uniformly Placed Users – Sum Over All Cells
(contributed from Dipl.-Ing. Thomas Werthmann from the Institute of Communication Networks and Computer Engineering in Stuttgart, Germany)
Summary, Conclusion
•Objectives and Use Cases of Mobile Cloud are discussed and illustrated
with a Traffic Scenario Example
•A VRAN/Mobile Cloud Architecture is discussed
• High bandwidth, low latency backhaul needed
•Discussion of a Baseband Pool (MSS-BBU) Functional Architecture
• A possible Virtualization Concept is presented with an
approach for a MSS-BBU HW Architecture
•Readout of a Control Architecture + Resource Pooling management
• First Multi-cell System Simulation to verify pooling gains are shown
• significant pooling gains can be shown (20-80%) depending on spatial
distribution of users (uniform/non-uniform)