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Capacity Optimization for Selforganizing Networks: Analysis and
Algorithms
Philipp Hasselbach
Philipp Hasselbach
Motivation
 Inhomogeneous capacity
demand
 Rush hour traffic
 Concerts, sports tournaments
 Change in user behaviour
t  t1
 Capacity Optimization
t  t2
Philipp Hasselbach
 As much capacity as
required
 At all times and all places
 Achieved by allocation of
cell bandwidth and transmit
power to the cells
2
Capacity in Cellular Networks
Ptx , B
PI
 Downlink considered
 1 , PN
 Link capacity influencing factors




User position
Attenuation
Shadowing
Inter-cell interference
 Cell capacity influencing factors
 User distribution
 Service type
 Scheduling
Philipp Hasselbach
PI
 2 , PN
Ptx
B
PI
k
PN
Transmit power
Cell bandwidth
Inter-cell interference power
SINR of user k
Noise power
3
Self-organizing Networks (SONs)
 Drivers
 High complexity of mobile radio
technology
 Operation of several networks of
different technologies
 Need to reduce CAPEX and OPEX
 Autonomous operation
 In configuration, optimization, healing
 Circumventing classical planning and
optimization processes
SONS: Shift of paradigm
Philipp Hasselbach
Source:
FP7 SOCRATES
4
Automatic Capacity Optimization for SONs
SON requirements
Source:
FP7 SOCRATES




Real-time capabilities
Treatment of large networks
Accurate results
Reliable operation
Capacity optimization
 Depends on
 user distribution
 environment
 Inter-cell interference (ICI)
 Interdependencies among cells and
users
High complexity, excessive signaling
Philipp Hasselbach
5
Outline
 Cell-centric Network Model
 Requirements and Derivation
 PBR- and PBN-Characteristic
 Automatic Capacity Optimization for SONs
 Self-Organizing Approach
 Network State evaluation
 Network Capacity Optimization
 Simulation and Analysis
 Functional Analysis
 Real-World Analysis
 Summary
Philipp Hasselbach
6
Outline
 Cell-centric Network Model
 Requirements and Derivation
 PBR- and PBN-Characteristic
 Automatic Capacity Optimization for SONs
 Self-Organizing Approach
 Network State evaluation
 Network Capacity Optimization
 Simulation and Analysis
 Functional Analysis
 Real-World Analysis
 Summary
Philipp Hasselbach
7
Cell-centric Network Model: Requirements
 Application for allocation of resources cell bandwidth and transmit powers
to the cells
 Modeling of the relation between cell bandwidth, transmit power and cell
performance
 Low complexity
 Consideration of
 User QoS requirements
 User distribution
 Environment
 Inter-cell interference
 Interdependencies among cells
Philipp Hasselbach
8
Cell-centric Network Model
User bit rate
Cell throughput
PBR-Characteristic
•SINR measurements
•User distribution,
environment model
Philipp Hasselbach
9
Cell-centric Network Model
User bit rate
Cell throughput
PBR-Characteristic
•User bit rate pdf
• empiric
• theoretic
•SINR measurements
•User distribution,
environment model
Philipp Hasselbach
•Number of users
•User QoS
requirements
10
User bit rate
•SINR measurements
•User distribution,
environment model
p
Philipp Hasselbach
Cell throughput
PBR-Characteristic
•User bit rate pdf
• empiric
• theoretic
•Cell throughput cdf
• empiric
• theoretic
•Number of users
•User QoS
requirements
•Outage probability p
•Cell bandwidth B
•Transmit power P
Cell throughput in Mbit/s
Cell-centric Network Model
11
Number of users
Cell throughput in Mbit/s
PBR- and PBN-Characteristic
Philipp Hasselbach
 PBR-Characteristic
 Relates transmit power, cell bandwidth,
cell throughput of cell i
Ri  f R Bi , i 
 PBN-Characteristic
 Relates transmit power, cell bandwidth,
number of users of cell i
Ni  f N Bi , i 
12
Cell throughput in Mbit/s
PBR- and PBN-Characteristic
 PBR-Characteristic
 Relates transmit power, cell bandwidth,
cell throughput of cell i
Ri  f R Bi , i 
Number of users
Power ratio: relates transmit power
to average
inter-cell interference power
 PBN-Characteristic
Philipp Hasselbach
 Relates transmit power, cell bandwidth,
number of users of cell i
Ni  f N Bi , i 
13
Number of users
Cell throughput in Mbit/s
PBR- and PBN-Characteristic
 PBR-Characteristic
 Relates transmit power, cell bandwidth,
cell throughput of cell i
Ri  f R Bi , i 
Available for different schedulers
 PBN-Characteristic
 Relates transmit power, cell bandwidth,
number of users of cell i
Ni  f N Bi , i 
Available for different schedulers
Philipp Hasselbach
14
Outline
 Cell-centric Network Model
 Requirements and Derivation
 PBR- and PBN-Characteristic
 Automatic Capacity Optimization for SONs
 Self-Organizing Approach
 Network State evaluation
 Network Capacity Optimization
 Simulation and Analysis
 Functional Analysis
 Real-World Analysis
 Summary
Philipp Hasselbach
15
Self-organizing Approach
 Self-organizing control loop:
 Network state optimization




Collection
of measurements
Network
capacity
optimisation
Cellular
radio
network
Philipp Hasselbach
Cell throughput in Mbit/s
Network
state
evaluation
Application of PBR-/PBN-Characteristic
Determination of possible performance
Comparison with required performance
Decision to take action
 Network capacity optimization
 Definition of optimization problems
 Application of PBR-/PBN-Characteristic
in objective function and constraints
 Solution of optimization problems to
obtain resource allocation to cells
 Constant cell sizes
16
Network State Evaluation
~
 Number of users in cell i: N i
 Cell bandwidth: Bi
 Power ratio: i
Number of users
 Current network state
Bi
 Number of users that can be
supported by the cell (obtained from
PBN-Characteristic): N i
~
 N i  N i   N : no action
~
 N i  N i   N : network optimization
Philipp Hasselbach
i
Ni  f N Bi , i 
17
Network Capacity Optimization
Optimization problems
max
Capacity P, B 
s.t.
min. user QoS
Optimization approaches
Network
throughput R
P ,B
max. transmit power
max. cell bandwidth
feasibilit y
Total
number of
users N
Transmit
power P
Cell bandwidth B
Joint P,B
R   Ri Bi , i 
i
N   N i Bi , i 
i
Philipp Hasselbach
18
Network Capacity Optimization
Optimization problems
max
Capacity P, B 
s.t.
min. user QoS
Optimization approaches
Network
throughput R
P ,B
max. transmit power
max. cell bandwidth
feasibilit y
R   Ri Bi , i 
i
N   N i Bi , i 
Total
number of
users N
Transmit
power P
Cell bandwidth B
Joint P,B
Central and distributed solving algorithms
for analysis and implementation
i
Philipp Hasselbach
19
Outline
 Cell-centric Network Model
 Requirements and Derivation
 PBR- and PBN-Characteristic
 Automatic Capacity Optimization for SONs
 Self-Organizing Approach
 Network State evaluation
 Network Capacity Optimization
 Simulation and Analysis
 Functional Analysis
 Real-World Analysis
 Summary
Philipp Hasselbach
20
Simulation Approach for Functional Analysis
Single hotspot scenario
Cluster hotspot scenario
 Inhomogeneous capacity demand:
hotspot scenarios

N hs users in hotspot cell, N 0
users in
non-hotspot cell
 Hotspot factor
 hs 
N hs
N0
 Wrap-around technique to avoid border
effects
Philipp Hasselbach
Multi hotspot scenario
 Evaluation of capacity optimization
approaches w.r.t. hotspot distribution
 Evaluation for different hotspot
strengths
 w/o coordination of bandwidth
allocations of neighbored cells
 Mitigation of inter-cell interference
 LTE-typical simulation parameters
21
Simulation Parameters for Functional Analysis
Cell radius R
250 m
Number of cells
39
User distribution
uniform
Propagation model
3GPP SCM Urban Macro
Shadow fading variance
8 dB
Max. transmit power
33 dBm
Total system bandwidth
10 MHz
Scheduling
PF, FT
Data rate per user
100 kbit/s
Philipp Hasselbach
22
Network Throughput Optimization, Single
Hotspot Scenario
PF scheduling
Philipp Hasselbach
FT scheduling
23
Network Throughput Optimization,
Coordinated Bandwidth Allocations
Cluster HS Scenario
Philipp Hasselbach
Multi HS Scenario
24
Functional Analysis: Summary
 Adaptation of the network to inhomogeneous capacity demands achieved
 For strong inhomogeneous capacity demand coordination of bandwidth
allocations required
 For FT scheduling coordination of bandwidth allocations required
 Transmit power allocation favorable with clustered hotspot cells
 Cell bandwidth allocation and joint allocation favorable with distributed
hotspot cells
Philipp Hasselbach
25
Simulation Approach for Real-World Analysis
 Scenario based on real network
 Network footprint from existing network
 Downtown area, 50 km², 46 sites, 126 sectors
 Pilot power receive strength prediction for each sector
 Determination of cell borders
 Inhomogeneous capacity demand
 According to user distribution estimation
 Based on DL throughput measurements
 229 snapshots over 5 days
 Performance analysis
 Consideration of snapshots 10-50
 Evaluation of performance in strongest hotspots
Philipp Hasselbach
26
Real-World-Analysis: Hotspot Strength and
Strongest Hotspots
Maximum hotspot strength
Philipp Hasselbach
Strongest hotspot
27
Real-World-Analysis: Hotspot Strength and
Strongest Hotspots
Maximum hotspot strength
Philipp Hasselbach
Strongest hotspot
28
Real-World-Analysis: Hotspot Strength and
Strongest Hotspots
Network throughput,
FT scheduling
Philipp Hasselbach
Strongest hotspot
29
Outline
 Cell-centric Network Model
 Requirements and Derivation
 PBR- and PBN-Characteristic
 Automatic Capacity Optimization for SONs
 Self-Organizing Approach
 Network State evaluation
 Network Capacity Optimization
 Simulation and Analysis
 Functional Analysis
 Real-World Analysis
 Summary
Philipp Hasselbach
30
Summary
 Cell-centric network modeling proposed
 PBR- and PBN-Characteristic
 Provides accurate modeling for automatic capacity optimization for SONs
 Avoids high complexity and high signaling effort
 Self-Organizing Approach proposed
 Application of cell-centric network model
 Central and distributed implementations for analysis and practical
implementation
 Simulative verification
 In artificial scenarios and real-world scenario
 Adaptation of the network to inhomogeneous capacity demands shown
Philipp Hasselbach
31
Backup
Philipp Hasselbach
32
Power-Bandwidth Characteristics
f r , r ,  
User distribution
f r , r ,  
f   
K independent users
~
Bk 
Rbit,k
log 2 1   k 
K
~
~
Bcell   Bk
k 1
Philipp Hasselbach
Bandwidth required
by user k
Bandwidth required
by the whole cell

PDF of the bandwidth
required by user k
~
f B~k Bk , Ptx , PI

Central Limit Theorem
PDF of the bandwidth
required by the cell
~
2
f B~cell ( Bcell , Ptx , PI ) ~ Ν ( cell ,  cell
)
F ( Bcell , Ptx , PI )
33
Cell Outage Probability
 CDF of the bandwidth required by the
1  pcell
cell
 Probability that sufficient bandwidth is
allocated


~
Prob Bcell  Bcell  F ( Bcell , Ptx , PI )
 Cell outage probability
 Probability that allocated bandwidth is
not sufficient
pcell  1  F ( Bcell , Ptx , PI )
Philipp Hasselbach
~
Bandwidth Bcell required by the cell
Bcell
34
Philipp Hasselbach
35
Motivation
 Fluctuating capacity demand
 Rush hour traffic
 Concerts, sports tournaments
 Change in user behaviour
 Change in environment
t  t1
 Capacity Optimization
 As much capacity as
required
 At all times and all places
t  t2
Philipp Hasselbach
36
Automatic Capacity Optimization for SONs
SONs
Source:
FP7 SOCRATES
 Real-time capabilities
 Accurate results
 Reliable operation
Philipp Hasselbach
Capacity optimization
 Complex modeling




Large number of users and BSs
Effects of the user distribution
Effects of the environment
Interdependencies among cells and
users
37
Automatic Capacity Optimization for SONs
SONs
Source:
FP7 SOCRATES
 Real-time capabilities
 Accurate results
 Reliable operation
Philipp Hasselbach
Capacity optimization
 Complex modeling




Effects of the user distribution
Effects of the environment
Inter-cell interference (ICI)
Interdependencies among cells and
users
38
Philipp Hasselbach
39
Cell-centric Network Model
•User distribution,
environment model
•SINR measurements
Cell throughput in Mbit/s
User QoS requirements
Philipp Hasselbach
•Outage probability
•Cell bandwidth
•Transmit power
40
Cell-centric Network Model
•User bit rate pdf
• empiric
• theoretic
•Number of users
•User QoS
requirements
•User distribution,
environment model
•SINR measurements
•Outage probability
•Cell bandwidth B
•Transmit power P
Cell Performance for (B,P)
Philipp Hasselbach
41
PBR-Characteristic
Cell Performance for (B,P)
Cell throughput in Mbit/s
•For different
• Cell bandwidth B
• Transmit power P
Philipp Hasselbach
 Reduced complexity due to focus on
cells
 User QoS requirements considered
 Relation between cell bandwidth,
transmit power and cell performance
42
Philipp Hasselbach
43
Cell-centric Network Model
Theoretic Approach
 Model the interdependence of
transmit power and cell
bandwidth
 Contain information on user
distribution, environment,
inter-cell interference
 Analytic derivation available
 Measurement based derivation
available, determined from
standard system measurements
(attenuation, SINR)
Random
Variable
transformation
Practical Approach
Measurement
data
transformation
Modeling equations
Philipp Hasselbach
44
Cell-centric Network Model
•User distribution,
environment model
•SINR measurements
•Outage definition
•Cell bandwidth
•Transmit power
Number of
users
Philipp Hasselbach
45
Philipp Hasselbach
46
Automatic Capacity Optimization Approaches
Uncoordinated/scheduling based
(State of the art):
Can I take
SC 1?
I take SC
1.
SC1
Local
Scheduling
I take SC
1.
SC1
SC2
OK, I
take SC
2
SC1
Local
Scheduling
+ : easy implementation
- : Collisions, QoS?
Philipp Hasselbach
Coordinated (new):
Inter-BS
communication
+ : Collisions can be avoided
QoS
- : Complexity?
Implementation?
47
Two Alternative SO Approaches
Uncoordinated:
Coordinated:
Can I take
SC 1?
I take SC
1.
SC1
Local
Scheduling
I take SC
1.
SC1
SC1
Local
Scheduling
Power-Bandwidth Characteristic
for performance analysis
Philipp Hasselbach
SC1
OK, I
take SC
2
Inter-BS
communication
Power-Bandwidth Characteristic
for approach realization and performance analysis
48
General System Concept
Hierarchical approach
Self-organising functionality/
Self-organising control loop
Network
parameter
optimisation
Resource allocation to cells
Sched. Sched.
cell 1
cell 2
Sched.
cell N
Resource allocation to users,
no inter-cell scheduling
Philipp Hasselbach
Network
state
evaluation
Network
parameter
adjustment
Source: 3GPP
49