Context-Aware Handover in HetNets

Context-Aware Handover in HetNets
Francesco Guidolin, Irene Pappalardo, Andrea Zanella, Michele Zorzi
Dept. of Information Engineering, University of Padova, Italy
EuCNC 2014, Bologna, Italy
June 25, 2014
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Introduction
Outline
1 Introduction
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Introduction
Introduction
Macro BS
Femto BS
RSRP
User
γth
TTT
t
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Introduction
State of the Art
Several Handover decision algorithms, based on:
• user speed
• received signal strength
• interference
• energy cost
Most of them are heuristic.
Moreover, they focus on a specific performance metrics (ping pong rate,
handover failure rate, ...)
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Introduction
State of the Art
Several Handover decision algorithms, based on:
• user speed
• received signal strength
• interference
• energy cost
Most of them are heuristic.
Moreover, they focus on a specific performance metrics (ping pong rate,
handover failure rate, ...)
I. Pappalardo
Context-Aware Handover in HetNets
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Introduction
State of the Art
Several Handover decision algorithms, based on:
• user speed
• received signal strength
• interference
• energy cost
Most of them are heuristic.
Moreover, they focus on a specific performance metrics (ping pong rate,
handover failure rate, ...)
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Introduction
Our Contributions
1
Average Shannon capacity as a function of the TTT value, the user
speed and the power profiles of Macro and Femto BSs
2
Optimization of the TTT parameter in different scenarios
3
Context-AWare (CAW) Handover Policy
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System model
Outline
2 System model
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System model
Reference scenario
Propagation model:
Ph (a) = Ptx
h − 10ηh log(dh (a)/d0h ) , h ∈ {M, F }
H
R
M-BS
F-BS
dMF
δ
c
• γth = 0 dB
• H defines the points
where the TTT is
triggered
• R is computed from the
power profiles of the BSs
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System model
User trajectory
b
Incidence angle: ω ∼ U [−π/2, π/2]
T
User States S ∈ {M, F , H}:
TH
R
• connected to M-BS
ω
a
F-BS c
[green]
[red]
PM (a)
PF (a)
• connected to F-BS
PF (a)
PM (a)
CF (a) = log2 1 +
ℓ
CM (a) = log2 1 +
• Handing over from one to the other
CH (a) = 0 [blue]
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System model
User trajectory
b
ℓ1
T
Incidence angle: ω ∼ U [−π/2, π/2]
User States S ∈ {M, F , H}:
R
• connected to M-BS
ω
a
F-BS c
[green]
[red]
PM (a)
PF (a)
• connected to F-BS
PF (a)
PM (a)
CF (a) = log2 1 +
ℓ
CM (a) = log2 1 +
• Handing over from one to the other
CH (a) = 0 [blue]
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System model
Performance Metric
ℓ
a
b
ω
c
I. Pappalardo
Trajectory Capacity
Z X
1
Cℓ =
CS (a)χa (S)da
|ℓ| ℓ S∈{M,F ,H}
..but the incidence angle ω is unknown!
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System model
Performance Metric
L x
Trajectory Capacity
Z X
1
CS (a)χa (S)da
Cℓ =
|ℓ| ℓ S∈{M,F ,H}
a
0
ω
..but the incidence angle ω is unknown!
c
Average Capacity
2
C¯ =
Lπ
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Z
0
π/2 Z L
0
X
CS (a(x, ω))χa(x,ω) (S)dx dω
S∈{M,F ,H}
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System model
Average Capacity
Internal component
C¯ = C¯int + C¯ext
L x
xmax
a
0
X 2
C¯int =
Lπ
S∈{M,F ,H}
Z
0
xmax = 2R cos ω
π/2Z xmax
CS (a)χa (S)dx dω
0
ω
c
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System model
Average Capacity
Internal component
C¯ = C¯int + C¯ext
L x
xmax
d+
0
d−
ω
c
a
X 2
C¯int =
Lπ
S∈{M,F ,H}
X 2
=
Lπ
S∈{M,F ,H}
Z
0
xmax = 2R cos ω
π/2Z xmax
CS (a)χa (S)dx dω
Z π/2Z
0
0
R
χd (S)+χd+ (S)
CS (a) √ −
1−(R/a)2 sin2 ω
da dω
R sin ω
• χd± (M) = 1 if d± < vT
• χd± (F ) = 1
• χd± (H) = 1
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if d± > v (T + TH )
otherwise
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System model
Average Capacity
External component
L
x
C¯ = C¯int + C¯ext
xmax a
0
X 2
C¯ext =
Lπ
S∈{M,F ,H}
ω
Z π/2Z
0
L
CS (a)χa (S)dx dω
xmax
c
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System model
Average Capacity
External component
L
xmax
0
x
d+
C¯ = C¯int + C¯ext
X 2
C¯ext =
Lπ
S∈{M,F ,H}
ω
c
a
X 2
=
Lπ
S∈{M,F ,H}
Z π/2Z
0
Z π/2Z
0
L
CS (a)χa (S)dx dω
xmax
√
R 2 +L2
CS (a) √
χd+ (S)
1−(R/a)2 sin2 ω
da dω
R
if ω < ωT
• χd+ (M) = 1 if d+ > v (T + TH )
• χd+ (F ) = 1 if d+ < vT
• χd+ (H) = 1 otherwise
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System model
Average Capacity
Final expression:
Z
2
C¯ =
Lπ
S∈{M,F ,H}
X
0
L
R
CS (a) G α(a, S), β(a, S),
a
da
where
• α and β are two angles, dependent on T ;
• G (·) is a function computed from the incomplete elliptic integral of
the first kind
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Performance Evaluation
Outline
3 Performance Evaluation
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Performance Evaluation
Average Capacity with ηM = 4 and ηF = 2
2.3
Avarege capacity [bit/s/Hz]
2.2
2.1
2
1.9
1.8
1.7
1.6
1 Km/h
20 Km/h
50 Km/h
100 Km/h
Tmin
1.5
1.4
1.3
0
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0.5
1
T [s]
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2
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Performance Evaluation
vth [Km/h]
Threshold Speeds vth
200
180
160
140
120
100
80
60
40
20
0
200
180
160
140
120
100
80
60
40
20
0
0.3 0.4
0.5 0.6
0.7 0.8
ηF/ηM
0.9
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1 3
3.5
Context-Aware Handover in HetNets
4
4.5
5
5.5
6
6.5
ηM
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Performance Evaluation
Contex-aware policy with fading1: Tmin
2.3
Avarege capacity [bit/s/Hz]
2.2
2.1
2
1.9
1.8
1.7
1.6
1 Km/h
20 Km/h
50 Km/h
100 Km/h
Tmin
1.5
1.4
1.3
0
0.5
1
T [s]
1.5
2
1
K. Fukawa, H. Suzuki, Y. Tateishi, “Packet-Error-Rate Analysis Using Markov
Models of the Signal-to-Interference Ratio for Mobile Packet Systems,” IEEE
Transactions on Vehicular Technology, July 2012.
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Performance Evaluation
Policies comparison
1
Contex-AWare policy [CAW]:
• Handover only when v < vth
• setting T = Tmin
2
Minimum TTT policy [TMIN]:
T = Tmin for every speed
3
Fixed TTT policy [FIX]:
static value of T = 100 ms for every speed
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Performance Evaluation
Policies comparison with ηM = 4 and ηF = 2
1.9
CAW
TMIN
FIX
Average Capacity bit/s/Hz
1.8
1.7
1.6
1.5
1.4
1.3
0
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20
40
60
80
100
Speed [Km/h]
120
Context-Aware Handover in HetNets
140
160
180
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Conclusions
Outline
4 Conclusions
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Conclusions
Conclusions and Future Work
Conclusions:
• we have derived analytically the user capacity during the Handover
process
• we have shown that the impact of the context parameters on the
optimal Handover policy is significant
As future work:
• extend the model to a scenario with multiple cells and users
• exploit a machine-learning estimator to predict the context parameters
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Conclusions
Context-Aware Handover in HetNets
Francesco Guidolin, Irene Pappalardo, Andrea Zanella, Michele Zorzi
Dept. of Information Engineering, University of Padova, Italy
EuCNC 2014, Bologna, Italy
June 25, 2014
I. Pappalardo
Context-Aware Handover in HetNets
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