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COST 289 15-16 March 2004, Zurich
Traffic Hotspots in UMTS Networks : influence on RRM
strategies Ferran Adelantado i Freixer
COST 289 15-16 March 2004, Zurich
Outline
•Introduction
•Simulation environment
•ResultsPath loss analysis
CAC performance
•Conclusions and future work
COST 289 15-16 March 2004, Zurich
Introduction
•The main goal of the study is to analyse non-uniformly traffic distributed scenarios.
•It is important to be able to maintain the target QoS.
•All alternatives should be taken into account before deploying hotspot WLAN networks.
•Assessment of RRM strategies becomes necessary to deal with high traffic density areas (hotspots).
•Is it possible to dynamically react to environment changes?
COST 289 15-16 March 2004, Zurich
Simulation Environment
• A single isolated cell (radius R).
• A traffic hotspot with radius r and placed D meters from base station.
• Ttotal=THS+TNo HS
THS=αTtotal
TNo HS=(1-α)Ttotal
•Only videophone users considered
•Propagation model:Lp(d)=Lo+ log(d)
DR
where
COST 289 15-16 March 2004, Zurich
Results
Simulation Parameters (1/2)
BS parameters
Cell type Omnidirectional
Thermal Noise -103 dBm
Pilot and common control channel power
32 dBm
Shadowing deviation 3 dB
Shadowing decorrelation length
20 m
UE parameters
Maximum transmitted power
21 dBm
Minimum transmitted power
-44 dBm
Mobile speed 10 km/h
Cell radius 1000 m
Hotspot radius 50 m
COST 289 15-16 March 2004, Zurich
Results
Traffic model
Call duration 120 seg
Offered bit rate 64 kb/seg (CBR)
Activity factor 1
Call rate 15 calls/h/user
QoS parameters
BLER target 1 %
Eb/No target 2.95 dB
Simulation Parameters (2/2)
Propagation model
Lo 128.1
37.6
COST 289 15-16 March 2004, Zurich
Results
Impact of traffic distribution (1/5)
Path loss distribution
variation
BLER variation
Path loss pdf :
)(zf HSZ
)(zf HS NoZ
where
no hotspot users path loss pdf
:
hotspot users path loss pdf
:
)()1()()( zfzfzf HS NoZ
HSZZ
Non-uniformly distributed traffic
scenario
COST 289 15-16 March 2004, Zurich
Results
Impact of traffic distribution (2/5)
No hotspot users path loss :
2z
2z
HS NoZ
-az if az
erfceReA
-az if za
erfceReA
zf
221
221
1
)(2
2
2
2
2
2
22
22
oL
A2
10
)10ln(
2
COST 289 15-16 March 2004, Zurich
Results
Impact of traffic distribution (3/5)
Hotspot users path loss:
2
22
22
2arcsin2
2*
21
)( 2
2
z
zzzHS
Z
eAD
AerDreA
ezf
oL
A2
10
)10ln(
2
COST 289 15-16 March 2004, Zurich
Results
Impact of traffic distribution (4/5)
Path loss pdfCell radius =1000m
D=150m
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
75 95 115 135
Lp(dB)
0.0
0.3
0.7
1.0
Hotspot close to the base station Path loss pdf
Cell radius =1000mD=950m
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
75 85 95 105 115 125 135 145
Lp(dB)
0.0
0.3
0.7
1.0
Hotspot far from the base station
Path loss pdfCell radius =1000m
=0.2
0
0.005
0.01
0.015
0.02
0.025
75 85 95 105 115 125 135 145
Lp(dB)
D = 150mD = 350mD = 550m
D = 750mD = 950m
Variation of hotspot location
COST 289 15-16 March 2004, Zurich
Results
Impact of traffic distribution (5/5)
=0.0 =0.3 =0.5
BLER 1.53 1.86 2.04
HS BLER N/A 2.63 2.60
No HS BLER
1.53 1.53 1.53
D=150m
D=550m
D=950m
BLER 1.46 1.48 2.04
HS BLER 1.00 1.07 2.60
No HS BLER
1.93 1.89 1.53
•No hotspot users BLER is maintained when increasing
•Total BLER grows as is increased.
•As D increases, total BLER increases.
•Hotspot users BLER grows for large D.
•No hotspot users BLER is lower for high D.
COST 289 15-16 March 2004, Zurich
Results
Call Admission Control design (1/3)
T
b
b
NpT
NE
RW
PLP
0
1
11
Transmitted power for mobile terminal
1
1
0
max
00
T
b
b
N
TP
T
bb
NE
RW
PP
LpNE
NE
p
Outage probability in UL
1
11
0
max
*
max
T
b
b
T
Np
N
E
R
WP
PL
Maximum admission
threshold for a certain Lp
COST 289 15-16 March 2004, Zurich
Results
Call Admission Control design (2/3)
Outage probability = 0.5 %
BLER ≈ 1.3 %
Admission threshold may be determined with Path Loss statistics (Cumulative density function) :
max
0.0 0.77
0.5 0.68
BLER (%)
1
1.1
1.2
1.3
1.4
1.5
1.6
1.7
30 35 40 45 50 55 60 65 70
Number of users
0 0.770.5 0.680.5 0.77
BLER Hotspot(%)
1
1.2
1.4
1.6
1.8
2
2.2
30 35 40 45 50 55 60 65 70
Number of users
0 0.770.5 0.680.5 0.77
BLER can be maintained by adjusting max
COST 289 15-16 March 2004, Zurich
Results
Call Admission Control design (3/3)
Admission probability
60
65
70
75
8085
90
95
100
105
30 40 50 60 70 80
Number of users
0 0.770.5 0.680.5 0.77
Maintaining low BLER with hotspots leads to an admission probability decrease.
COST 289 15-16 March 2004, Zurich
Conclusions and Future Work
•In non-uniformly distributed traffic scenarios, without applying CAC, hotspots with high D and cause a QoS degradation.
•Suitable admission control threshold (max) can be determined if path loss statistics are known.
•Maintaining low BLER implies an admission probability decrease.
•Future work will be focused on dynamic hotspot detection.
•Design and assessment of adapted RRM strategies will determine if it is necessary to include a hotspot WLAN .
