PhD Thesis Defense on
Optimization and Self-optimization for LTE Networks
Author: Abdoulaye TALL
Supervisors: Zwi ALTMANEitan ALTMANRichard COMBES
December 17th, 2015
Introduction
Context
More and morecomplex networks
Highly competitivemarket withsteadily decreasingprices/revenues
Example: Orange France
Sites: 19000+ 2G, 19000+ 3G, 7000+ 4G
Frequency bands: 700 MHz, 800 MHz, 900 MHz,1.8 GHz, 2.1 GHz and 2.6 GHz
Indoor femto-cells
Challenge:Optimize resource utilization and Control the OPEX.
Enabler:Automation with Self-Organizing Network (SON) for
self-configuration, self-optimization and self-healing.
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 2 / 56
Introduction
Thesis objectives
Design self-optimizing algorithms for use cases of interest: hetnets and activeantenna systems.
RequirementsSimple
Non-Heuristic
Fast
Robust
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 3 / 56
Introduction
SON design methodology
Network/KPI modeling
Queuing theory
Probability theory
Mobile technology
Problem Formulation
Algorithm Design
Game theory
Convex Optimization
Stochastic Approximation
Proof-of-concept Simulator/Demonstrator
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 4 / 56
Overview
1 Introduction
2 Background
3 Heterogeneous NetworksLoad balancingInterference coordinationNumerical results
4 Active Antenna SystemsVertical SectorizationVirtual SectorizationMultilevel Beamforming
5 SON CoordinationConceptProblem formulation and SolutionUse case
6 Conclusion & PerspectivesConclusionPerspectives
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 5 / 56
Overview
1 Introduction
2 Background
3 Heterogeneous Networks
4 Active Antenna Systems
5 SON Coordination
6 Conclusion & Perspectives
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 6 / 56
Background
Flow level network model
Base station modeled as M/G/1 PS queue
User’s data rate R(r) = min(Rmax, η log2(1 + SINR(r)))
Key performance indicators:
Load: ρ = traffic demandservice rate
Mean user throughput: µ = R(1− ρ)
Others by simulation.
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 7 / 56
Overview
1 Introduction
2 Background
3 Heterogeneous NetworksLoad balancingInterference coordinationNumerical results
4 Active Antenna Systems
5 SON Coordination
6 Conclusion & Perspectives
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 8 / 56
Heterogeneous Networks
Introduction
Nodes with different transmit powers
Nodes with different propagation conditions
Nodes with low processing capabilities
Dense network with more interference problems
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 9 / 56
Overview
1 Introduction
2 Background
3 Heterogeneous NetworksLoad balancingInterference coordinationNumerical results
4 Active Antenna Systems
5 SON Coordination
6 Conclusion & Perspectives
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 10 / 56
Heterogeneous Networks Load balancing
Literature review
H. Kim et al., IEEE INFOCOM 2010:
s∗u = argmaxsRu,s(1− ρs)α (1)
with Ru,s (data rate from BS s to user u), ρ (load) and α ∈ R+.
=⇒ Minimizes∑
s(1−ρs )1−α
α−1 .
R. Combes et al., IEEE INFOCOM 2012:
Ps(k + 1) = Ps(k)(1 + εk(ρ1(k)− ρs(k))) (2)
with P (pilot power).
=⇒ Minimizes maxs ρs
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 11 / 56
Heterogeneous Networks Load balancing
End-to-end load model
Classical BS load definition:
ρ = min
(1,
∫A
λ(r)E(σ)
R(r)dr
), (3)
End-to-end load definition
ρg = min
(1,
∫A
λ(r)E(σ)
min(CBH ,R(r))dr
). (4)
where
CBH : backhaul capacity,
λ(r): arrival rate at location r ,
E(σ): mean file size,
R(r): peak rate at location r .
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 12 / 56
Overview
1 Introduction
2 Background
3 Heterogeneous NetworksLoad balancingInterference coordinationNumerical results
4 Active Antenna Systems
5 SON Coordination
6 Conclusion & Perspectives
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 13 / 56
Heterogeneous Networks Interference coordination
Almost Blank Subframe (ABS) mechanism
Time-domain interference mitigation in heterogeneous networks.
Used in conjunction with Cell Range Extension (CRE).
Illustration of Almost Blank Sub-Frames in a HetNet
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 14 / 56
Heterogeneous Networks Interference coordination
ABS-based interference coordination (eICIC)
Opimization Problem: trade-off between small cells’ users SINR and macro cells’capacity.
θ: ABS ratio applied by the considered macro cells.
Ru = (1− θ)Ru,m: Average data rate of macro user.
Ru = (1− θ)Rno ABSu,p + θRABS
u,p : Average data rate of small cell user.
Performance criteria: α-fair utility of users’ throughput
Uα(θ) =
∑
all users
logRu if α = 1
∑all users
R1−αu
1−α otherwise
(5)
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 15 / 56
Heterogeneous Networks Interference coordination
Lower-bound PF utility for low complexity algorithm
Exact Proportional Fair (α = 1) utility:
UPF exact(θ) =M∑
m=1
∑u∈m
log((1− θ)Ru,m) +∑u∈p
log((1− θ)Rno ABSu,p + θRABS
u,p ) (6)
Lower bound utility:
UPF approx(θ) =M∑
m=1
∑u∈m
log((1− θ)Ru,m) +∑u∈p
1
2log(2(1− θ)Rno ABS
u,p )
+∑u∈p
1
2log(2θRABS
u,p )
(7)
Optimal ABS ratio in closed-form
θ =Np
2(Np +∑M
m=1 Nm)(8)
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 16 / 56
Heterogeneous Networks Interference coordination
Frequency splitting (orthogonal deployment)
Orthogonal frequency used between macro cells and small cells
Completely eliminates interference between macro cells and small cells at theprice of reduced frequency reuse.
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 17 / 56
Overview
1 Introduction
2 Background
3 Heterogeneous NetworksLoad balancingInterference coordinationNumerical results
4 Active Antenna Systems
5 SON Coordination
6 Conclusion & Perspectives
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 18 / 56
Heterogeneous Networks Numerical results
Numerical results - Scenario
Network parameters
Number of macro BSs 3
Number of small BSs 12
Number of interfering macros 6 × 3 sectors
Macro Cell layout hexagonal trisector
Small Cell layout omni
Intersite distance 500 m
Bandwidth 10MHz
Scheduling Round-Robin
Channel characteristics
Thermal noise -174 dBm/Hz
Macro Path loss (d in km) 128.1 + 37.6 log10(d) dB
Small cell Path loss (d in km) 140.7 + 36.7 log10(d) dB
Algorithms Parameters
SON update frequency every event
Step size of ABSrO 10−4
Step size of FSO 5.10−5
Network layout scenario
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Heterogeneous Networks Numerical results
Numerical results - Scenario cont’d.
Traffic spatial distribution uniform
λ 14 users/s/km2
λh 6 users/s/km2
Service type FTP
Average file size 6 Mbits
NoSON: baseline.LBonly: load balancing only.AFUAonly: alpha-fair user association (afua) only.LB-CCD-approx: load balancing with approximate ABS-based eICIC.AFUA-CCD-approx: afua with approximate ABS-based eICIC.LB-OD-approx: load balancing with approximate frequency-based eICIC.AFUA-OD-approx: afua with approximate frequency-based eICIC.
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 20 / 56
Heterogeneous Networks Numerical results
Numerical results - Performance
0
2
4
6
8
10
−46%
65%
424%
98%
411%
108%
Geo
met
ric M
ean
Thr
ough
put (
Mbp
s)
NoSO
N
LBon
lyAFU
Aonly
LB_C
CD_app
rox
AFUA_C
CD_app
rox
LB_O
D_app
rox
AFUA_O
D_app
rox
α-fair utility (α = 1)
0
20
40
60
80
100
Res
ourc
e U
tiliz
atio
n (%
)
NoSO
N
LBon
lyAFU
Aonly
LB_C
CD_app
rox
AFUA_C
CD_app
rox
LB_O
D_app
rox
AFUA_O
D_app
rox
At Small CellsAt macro Cells
Loads
0
5
10
15
60%31%
136%
65%
152%
27%
Mea
n U
ser
Thr
ough
put (
Mbp
s)
NoSO
N
LBon
lyAFU
Aonly
LB_C
CD_app
rox
AFUA_C
CD_app
rox
LB_O
D_app
rox
AFUA_O
D_app
rox
Mean User Throughput
0
1
2
3
−45%
60%
368%
118%
307%
74%
Cel
l Edg
e U
ser
Thr
ough
put (
Mbp
s)
NoSO
N
LBon
lyAFU
Aonly
LB_C
CD_app
rox
AFUA_C
CD_app
rox
LB_O
D_app
rox
AFUA_O
D_app
rox
Cell Edge throughput
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 21 / 56
Heterogeneous Networks Numerical results
Exact vs Approximate algorithms
0 10 20 30 40 500
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
User Throughput (Mbps)
CD
F (
%)
Exact utility ABSrO with LBLower Bound utility ABSrO with LBExact utility ABSrO with AFUALower Bound utility ABSrO with AFUA
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 22 / 56
Overview
1 Introduction
2 Background
3 Heterogeneous Networks
4 Active Antenna SystemsVertical SectorizationVirtual SectorizationMultilevel Beamforming
5 SON Coordination
6 Conclusion & Perspectives
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 23 / 56
Active Antenna Systems
Introduction
Base Station architecture evolution
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 24 / 56
Overview
1 Introduction
2 Background
3 Heterogeneous Networks
4 Active Antenna SystemsVertical SectorizationVirtual SectorizationMultilevel Beamforming
5 SON Coordination
6 Conclusion & Perspectives
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 25 / 56
Active Antenna Systems Vertical Sectorization
Vertical Sectorization (VeSn) description
Vertical separation of the two beams in the same sector
Sector divided in two cells: inner and outer with resp. vertical tilts θinner andθouter with θinner > θouter
I Transmit powers:Pi and Po for inner andouter cells resp. withPi + Po = Ptotal.
I Possible implementations:bandwidth sharing or fullreuse.
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 26 / 56
Active Antenna Systems Vertical Sectorization
VeSn with frequency reuse one
Description
Inner and outer sectors reuse the whole available bandwidth.
Power budget split equally between inner and outer sectors.
Advantages
Increased capacity.
Increased antenna gain for inner cell users.
Drawbacks
Reduced transmit power.
More interference.
Requirement: SON controller for VeSn feature activation.
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 27 / 56
Active Antenna Systems Vertical Sectorization
VeSn feature activation problem
Activation rule
Action =
{VeSn ON if µON(ρi , ρo) > µOFF(ρi , ρo)VeSn OFF otherwise
(9)
Decision Boundary
µON(ρi , ρo) = µOFF(ρi , ρo) (10)
(VSOFF) : a1ρ2i + b1ρiρo + c1ρ
2o + d1ρi + e1ρo = 0 (11)
(VSON) : a2ρ2i + b2ρiρo + c2ρ
2o + d2ρi + e2ρo = 0 (12)
with a1, a2, b1, b2, c1, c2, d1, d2, e1, e2: parameters depending on traffic and datarate distributions in the sector.
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 28 / 56
Active Antenna Systems Vertical Sectorization
VeSn activation controller calibration
Data from realistic network simulator
Activation
0 0.2 0.4 0.6 0.8 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
ρinner,off
ρ oute
r,of
f
Better activateStay deactivated
Deactivation
0 0.2 0.4 0.6 0.8 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
ρinner,on
ρ oute
r,on
Stay activatedBetter deactivate
Data used to estimate parameters of the decision boundaries: classificationproblem.
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 29 / 56
Active Antenna Systems Vertical Sectorization
VeSn activation controller performance
ρinner
ρ oute
r
0 0.2 0.4 0.6 0.8 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Decision boundary for VS OFFDecision boundary for VS ON
Activation controller
0 1000 2000 3000 4000 5000 6000 7000 80000
2
4
6
8
10
12
Tra
ffic
dem
and
(Mbp
s)
Time (s)
Inner cellOuter Cell
Traffic scenario
0 1000 2000 3000 4000 5000 6000 7000 80000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Act
ivat
ion
Dec
isio
n (0
=O
FF
,1=
ON
)
Time (s)
Always OFFAlways ONAAS SON
Activation decisions
0 1000 2000 3000 4000 5000 6000 7000 80000
5
10
15
20
25
30
Mea
n U
ser
Thr
ough
put (
Mbp
s)
Time (s)
Always OFFAlways ONAAS SON
User performance
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 30 / 56
Active Antenna Systems Vertical Sectorization
VeSn with bandwidth sharing
Description
Total frequency bandwidth split between inner and outer sectors.
Transmit power per Hz does not change.
Advantages
No Inter-cell interference between inner and outer cells.
Increased transmit power for each user compared to reuse one.
Increased antenna gain for inner cell users.
Drawbacks
Reduced capacity because there is no reuse.
Problem: Which sharing proportions for the frequency bandwidth?
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 31 / 56
Active Antenna Systems Vertical Sectorization
Optimal bandwidth sharing
Parameter: δ - proportion of bandwidth allocated to inner cell.
Criteria: Alpha-fair utility of all users throughputs
Uα(δ) =
∑u∈Ui
log(δRu
)+∑
u∈Uolog((1− δ)Ru
)α = 1∑
u∈Ui
(δRu)1−α
1−α +∑
u∈Uo
((1−δ)Ru)1−α
1−α α 6= 1
If α = 1, optimal parameter in closed form:
δ =Ni
Ni + No(13)
with Ni = |Ui | and No = |Uo |.For general α:
δ[k + 1] = δ[k] + ε∂Uα(δ[k])
∂δ
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 32 / 56
Active Antenna Systems Vertical Sectorization
VeSn with bandwidth sharing performance
Network layout
0 2 4 6 8 100
10
20
30
40
50
60
70
80
90
100
Arrival rates (users/s)
Load
s (%
)
VS bandwidth sharingVS reuse oneBaselineSON controller
Maximum loads
0 2 4 6 8 100
5
10
15
20
25
30
35
Arrival rates (users/s)
Mea
n U
ser
Thr
ough
puts
(M
bps)
VS bandwidth sharingVS reuse oneBaselineSON controller
Mean User Throughput
0 2 4 6 8 100
2
4
6
8
10
12
Arrival rates (users/s)
Cel
l Edg
e U
ser
Thr
ough
puts
(M
bps)
VS bandwidth sharingVS reuse oneBaselineSON controller
Cell Edge throughput
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 33 / 56
Overview
1 Introduction
2 Background
3 Heterogeneous Networks
4 Active Antenna SystemsVertical SectorizationVirtual SectorizationMultilevel Beamforming
5 SON Coordination
6 Conclusion & Perspectives
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 34 / 56
Active Antenna Systems Virtual Sectorization
Virtual Sectorization (ViSn) description
Evolution of vertical sectorization.
Spatial separation of beam (both vertically and horizontally) using antennaarrays.
Conservation of total power budget leading to resource allocation problems.
Can be implemented with reuse one or frequency sharing (as in VeSn).
Antenna Array Network layout
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 35 / 56
Active Antenna Systems Virtual Sectorization
ViSn performance results
00:00 00:50 01:40 02:30 03:20 04:100
1
2
3
4
5
6
Time (HH:MM)
Arr
ival
rat
es (
user
s/s)
λh
λTotal
Traffic profile
00:00 00:50 01:40 02:30 03:20 04:1030
40
50
60
70
80
90
Time(HH:MM)
Load
s (%
)
ViSn bandwidth sharingViSn reuse oneBaseline (No ViS)
Maximum loads
00:00 00:50 01:40 02:30 03:20 04:108
9
10
11
12
13
14
15
16
17
18
Time(HH:MM)
Mea
n U
ser
Thr
ough
puts
(M
bps)
ViSn bandwidth sharingViSn reuse oneBaseline (No ViS)
Mean User Throughput
00:00 00:50 01:40 02:30 03:20 04:100.5
1
1.5
2
2.5
3
3.5
4
4.5
Time(HH:MM)
Cel
l Edg
e U
ser
Thr
ough
puts
(M
bps)
ViSn bandwidth sharingViSn reuse oneBaseline (No ViS)
Cell Edge throughput
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 36 / 56
Overview
1 Introduction
2 Background
3 Heterogeneous Networks
4 Active Antenna SystemsVertical SectorizationVirtual SectorizationMultilevel Beamforming
5 SON Coordination
6 Conclusion & Perspectives
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 37 / 56
Active Antenna Systems Multilevel Beamforming
Introduction to multilevel beamforming
Challenging topic in Massive MIMO community
State of the art: channel matrix estimation and inversion
Goal: low complexity processing for beamforming in TDD & FDD and lowfeedback in case of FDD.
Our approach: Extend codebook idea to integrate coverage aspect =⇒ Beamplanning.
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 38 / 56
Active Antenna Systems Multilevel Beamforming
Multilevel beamforming idea
Example of beam hierarchy
Design the codebook hierarchically.
Find the best beam available bynavigating iteratively through thecodebook.
Tree search (logarithmic complexity)
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 39 / 56
Active Antenna Systems Multilevel Beamforming
Beam planning for each type of environment
Dense Urban Rural
Problem: automatic generation of beam codebook given basic cellinformation (size, antenna height, etc.). (future work)
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 40 / 56
Active Antenna Systems Multilevel Beamforming
Multilevel beamforming performance
0 5 10 15 20 25 30 35 40 450
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
User throughputs (Mbps)
CD
F
Empirical CDF
No beamforming(baseline)Hierarchical beamformingOptimal beamforming
User throughput CDFs comparison
Data rate function: R = min(Rmax , η log2(1 + SINR))
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 41 / 56
Overview
1 Introduction
2 Background
3 Heterogeneous Networks
4 Active Antenna Systems
5 SON CoordinationConceptProblem formulation and SolutionUse case
6 Conclusion & Perspectives
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 42 / 56
SON Coordination
SON model as control loops
Generic formulation of stochastic approximation algorithms
θn+1 = θn + ε(F (θn) + Mn) (14)
where
θ: parameters,F (·): search directions,Mn: noise.
Mean behavior described by the limiting Ordinary DifferentialEquation (ODE):
θ = F (θ) (15)
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 43 / 56
SON Coordination Concept
Stability and coordination
Jacobian of F (θ) defined as Gθ = JF (θ) where
Gθ(i , j) =∂Fi (θ)
∂θj(16)
Rosen’s sufficient condition for stability:
Theorem
If the matrix Gθ + GTθ is negative definite for every θ in
∏Nj=1 Sj , then θ = F (θ)
has a unique equilibrium point and it is asymptotically stable in∏N
j=1 Sj .
Local stability given by linearization: F (θ) = Aθ then AT + A negativedefinite is a sufficient condition.
Coordination idea: Apply a coordination matrix C (obtaining θ = CF (θ))such that (CA)T + CA is negative definite.
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 44 / 56
SON Coordination Problem formulation and Solution
Coordination matrix computation
minimize ‖C + A−1‖Fs.t. (CA)T + CA ≺ 0;C ∈ C
(17)
where
‖.‖F is the Frobenius norm.
C: the set of coordination matrices satisfying the system constraints.
θi = Fi (θ)www�θi =
∑j
Ci,jFj(θ)
System constraints
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 45 / 56
SON Coordination Use case
Use case description
SON function Parameters KPIs
Load balancing Transmit pilot powerPpilot
Load of the cell ρ
Outage Probabilityminimization
Transmit data powerPTCH
Coverage probability ofthe cell K
Blocking Rateminimization
Admission threshold AT Blocking rate of the cellb
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 46 / 56
SON Coordination Use case
Performance results
500 1000 1500 2000 2500 3000 35000
10
20
30
40
50
60
70
80
90
100
Time (s)
BS
Loa
ds (
%)
BS 1 without coordinationBS 2 without coordinationBS 3 without coordinationBS 1 with coordinationBS 2 with coordinationBS 3 with coordination
Loads
500 1000 1500 2000 2500 3000 35000
10
20
30
40
50
60
70
80
90
100
Time (s)O
utag
e pr
obab
ility
(%
)
BS 1 without coordinationBS 2 without coordinationBS 3 without coordinationBS 1 with coordinationBS 2 with coordinationBS 3 with coordination
Coverage probabilities
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 47 / 56
Overview
1 Introduction
2 Background
3 Heterogeneous Networks
4 Active Antenna Systems
5 SON Coordination
6 Conclusion & PerspectivesConclusionPerspectives
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 48 / 56
Conclusion & Perspectives Conclusion
Conclusion
SON algorithms for small cells
Load balancing (with constrained backhaul)Alpha-fair Interference coordination
SON algorithms for active antenna systems
VeSn feature activationAlpha-fair bandwidth sharing for VeSn and ViSnBeam selection algorithm for multilevel beamforming
Which is the best option?
With low cost backhaul or for non-line-of-sight coverage areas: Small CellsOthers: Active Antenna Systems (multilevel beamforming)
Systematic SON coordination framework
Tested for load balancing with interference coordination in small cells scenario.
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 49 / 56
Conclusion & Perspectives Perspectives
Perspectives
I Extend algorithms to other use cases: D2D, energy saving, etc.
I Backhaul-aware SON functions
I Multi-armed bandits for AAS features activation
I Beam planning automation and application to more use cases
I Which α in α-fair utilities?
I Coordination of highly non-linear systems of SON functions
Abdoulaye Tall Optimization and Self-optimization for LTE December 17th, 2015 50 / 56
Thank you!Questions are welcome.