"Present & Future: Current Progress In Wireless & Communications Research Group", ANGKASA Seminar Series 2/2012, 4th June, 2012, UKM

1. ANGKASA Seminar Series 2/2012
PRESENT & FUTURE: CURRENT
PROGRESS IN WIRELESS &
COMMS. RESEARCH GROUP
Ir. Dr. Rosdiadee Nordin, Associate Fellow ANGKASA

2. Rosdiadee Nordin, Gita Mahardhika, Nasharuddin Zainal
Faculty of Engineering and Built Environment, UKM
Ahmad Khaldun Ismail, Nurul Saadah, Yap Yah Yun, Ainun Abdul Ghani
Faculty of Medicine, UKM
PRESENT: Applied, Cross-Discipline, Quali. + Quanti.
TransERV: Improvement of Emergency
Medical Response via GPS Navigation
(UKM-GUP-2011-326)

3. INTRODUCTION
• Emergency response vehicle (ERV): essential pre-
hospital service
• Emergency response performance affects emergency
patients’ survivability [1], [2], [3]
• GPS is applicable for improving performance of land
transportation, including emergency response
vehicle [4]
[1] T. H. Blackwell and J. S. Kaufman, "Response Time Effectiveness: Comparison of Response Time and Survival in an Urban
Emergency Medical Services System," Academic Emergency Medicine, vol. 9, 2002.
[2] E. B. Lerner and R. M. Moscati, "The golden hour: Scientific fact or medical "Urban Legend"?," Academic Emergency Medicine,
vol. 8, p. 3, July 2001 2001.
[3] J. P. Pell, et al., "Effect of reducing ambulance response times on deaths from out of hospital cardiac arrest: cohort study,"
BMJ, vol. 322, pp. 1385-1388, 2001.
[4] G. Mintsis, et al., "Applications of GPS technology in the land transportation system," European Journal of Operational
Research, vol. 152, p. 11, 2004.

4. CURRENT PRACTICE
• GPS system limited to tracking only, not as navigation
tool

5. OBJECTIVE
Reduce emergency transport/response time via GPS
navigation
METHOD
Compare ambulance travel time based on two
navigation systems (conventional map vs GPS)

6. RESEARCH ENVIRONMENT (1)
• Coverage area of Emergency Department, Universiti
Kebangsaan Malaysia Medical Centre (15 km radius)
• 40 emergency calls, i.e. different coordinates
• Simulation during working days (Mon-Fri, 8 am-5
pm)
• Emergency call served by two different methods:
– First trip, ambulance went to emergency scene using map
navigation
– Second trip, ambulance was following directions and paths
given by GPS device
– Limited to 30 mins (max.) interval to reduce bias

7. RESEARCH ENVIRONMENT (2)
• Each trip consists of response travel and transport travel
– Response: from hospital (base) to scene
– Transport: from scene to base
• VARIABLE(1) : Response/transport time and distance
– map navigation
– GPS navigation
• VARIABLE(2):
– During travel using map navigation:
• Response and transport time estimated by the emergency team
– During travel using GPS navigation:
• Response and transport time calculated calculated by the GPS device

8. RESEARCH ENVIRONMENT (3)
• VARIABLE (3): Profile of paramedics and drivers (19
respondents) includes:
– Years of experience
– Familiarity with ambulance coverage area
– Opinions on relevance of GPS application on emergency
vehicles
• Performance Measurement
– Average speed = Actual distance (km)
travel time (hr)
– Qualitative opinion of emergency team

9. RESULT (1)

10. RESULT (2)
Empirical CDF Empirical CDF
1 1
0.9 0.9
0.8 0.8
0.7 0.7
0.6 0.6
F(x)
F(x)
0.5 0.5
0.4 0.4
0.3 0.3
0.2 0.2
0.1 Response average speed using map 0.1 Transport average speed using Map
Response average speed using GPS Transport average speed using GPS
0 0
10 15 20 25 30 35 40 45 50 55 60 15 20 25 30 35 40 45 50 55
x x
CDF of response average speed, CDF of transport average speed,
improvement of 3.21% reduction of 3.08%

11. GPS improves transport time
Result (3): Emergency team’s opinion
Unsure
GPS improve response time 16%
No
Unsure 16%
21%
Yes
68%
No
16% Yes
63% Ambulance service should be equipped with GPS
Unsure
No 5%
11%
Yes
84%

12. FUTURE WORKS

13. CONCLUSIONS
• GPS navigation has higher average speed compare to
map navigation
• Quantitatively, GPS is stated as useful by most
emergency team (paramedics and drivers)

14. *Ibraheem Abdullah Mohammed Shayea, *^Rosdiadee Nordin, *^Mahamod Ismail
*Faculty of Engineering and Built Environment
^ANGKASA Space Institute
FUTURE: Fundamental
Carrier Aggregation (CA) Techniques
in LTE- Advanced (4G) Network
(GUP-2012-036)

15. FUTURE OF WIRELESS NETWORKS
• Existing LTE standard (Release 8&9) suffers from limited
capacity and lower transmission data throughput
• LTE-A (Rel. 10) [1] is the potential candidate for IMT-A’s
Fourth Generation (4G) network
• Need several new technology ‘enablers’ to allow higher
throughput
• 3GPP has identified following new technologies:
– Coordinated Multipoint (CoMP)
– Enhanced MIMO
– HetNet
– Self-Organized Network (SON)
– Carrier Aggregation (CA)
[1] 3GPP, TS36.211 V10.4.0 (2011-12) Evolved Universal Terrestrial Radio Access (E-UTRA); Physical channels
and modulation

16. OBJECTIVE
• To investigate the impact of different parameters on
the performance of CA technique in the DL for LTE-
Advanced System (throughput and system capacity)

17. INTRODUCTION TO CA
• Carrier aggregation provides higher peak data rate
for UEs based on CA over a wider transmission
bandwidth up to 100 MHz [4]
– Up to 1 Gbps for downlink
– Up to 500 Mbps for uplink
• Possibly to aggregate up to five component carriers
with:
– Contiguous Carrier Aggregation
– Non-contiguous Carrier Aggregation.
[4] István Z. Kovács “Carrier Aggregation in LTE-Advanced (from physical layer to upper layers(layers)”
Workshop Session 10c, (Nokia Siemens Networks, Denmark), Luis Garcia (Aalborg University). 17 June 2011

18. COMPONENT CARRIERS TYPES
Contiguous carrier aggregation Non-contiguous carrier aggregation
• Up to 5 component carriers • Multiple available component
carriers, separated along
• One FFT module and one radio
frequency band
front-end [2]
• Aggregation of fragmented
spectrum [5]
[2] Daren McClearnon and Wu HuanSystem , “LTE-Advanced: Overcoming Design Challenges for 4G PHY
Architectures “ , Agilent Technologies, June 2, 2011.
[5] Yuan G, Zhang X, Wang W, Yang Y. Carrier Aggregation for LTE-Advanced Mobile Communication systems.
IEEE Communications Magazine, 2010;(February):88-93

19. METHODOLOGY (1)
Table 1: Scenarios in the Simulation
• Five scenarios proposed (Table Scenario NO Description
1) Scenario # 1 Non-CA with 1 CC (CC1) ,
CC bandwidth = 20MHz
• Operating carrier frequency Total System BW = 1 * 20MHz = 20 MHz
from 2 GHz for all five carriers’ Scenario # 2 CA with 2 CC (CC1 and CC2) ,
f1, f2, f3, f4 and f5 located in CC bandwidth = 20MHz
the same band with different Total System BW = 2 * 20MHz = 40 MHz
system bandwidth for each Scenario # 3 CA with 3 CC (CC1, CC2 and CC3) ,
CC bandwidth = 20MHz
scenario Total System BW = 3 * 20MHz = 60 MHz
• Antenna gains and Tx power on Scenario # 4 CA with 4 CC (CC1, CC2, CC3 and CC4) ,
five carriers are identical, while CC bandwidth = 20MHz
shadow fading depends on the Total System BW = 4 * 20MHz = 80 MHz
location of the receiver Scenario # 4 CA with 5CC (CC1, CC2, CC3, CC4 and
CC5) CC bandwidth = 20MHz
antenna Total System BW = 5 * 20MHz = 100 MHz

20. METHODOLOGY (2)
3000
• Figure 8 illustrates the
eNB
9
UE simulation scenario in the
2000 8 10
DL LTE-Advanced system
19 7 11
1000 • Unity Frequency Reuse
Distance in meter
6 2
0 18 1 12 Factor (FRF) is used
-1000
5 3
• Each eNB coverage is
17 4 13
hexagon in shape located at
16 14
-2000
it center with radius of 750
15
-3000 m.
• 40 UEs generated randomly
-4000 -3000 -2000 -1000 0 1000 2000 3000 4000
Distance in meter
Figure 8: LTE cellular layout

21. METHODOLOGY (3)
• The transmitted power for each subcarrier assumed to be
similar across all subcarriers. Cell capacity, which considers
the effect of frequency reuse factor can be expressed as:
• Where
– BW is total system bandwidth (Hz)
– BWeff is system bandwidth efficiency
– SINRα is achieved SINR, α is frequency reuse factor, assumed to
be unity (α =1); i.e. only 1/α of the spectrum can be used by one
cell
– SINReff is SINR implementation efficiency

22. RESULTS AND DISCUSSIONS (1)
scenario #5, aggregated 5 CCs is 94 Mbps/cell
profit rate scenario #5 achieves user throughput
gains of 20 (#4), 40 (#3), 59 (#2) and 82 (#1)
**Non-CA scenario utilizing 1 CC Mbps/cell
User Throughput with CA Technique Propobility of User Throughput with CA technique
1
100
Non-CA
0.9 CA-2CCs 90
CA-3CCs
0.8
CA-4CCs 80
CDF function for User Throughput
CA-5CCs
0.7
70
User Throughput [Mbps]
0.6
60
0.5
50
0.4
40
0.3
30
0.2
20
0.1
10
0
0 20 40 60 80 100 120 140 160 180 200 0
User Throughput [ Mbps] 1 2 3 4 5
1CC 2CC 3CC 4CC 5CC
Figure 11: CDF of average user Figure 12: Average user throughput
throughput

23. RESULTS AND DISCUSSIONS (2)
CA achieves around 87.5% gain over Non-
CA techniques
Active Users Propobility each time Cell Throughput
0.8 200
Non-CA
180 CA
0.7
The Percentage of Active UE/cell x 100%
160
0.6
140
Cell Throughput [Mbps]
0.5
120
0.4 100
0.3 80
60
0.2
40
0.1
20
0
1 2 3 4 5 0
1CC 2CC 3CC 4CC 5CC 250 300 350 400 450 500
Normalized Distance [meter]
Figure. 13. Average active users per cell Figure. 14. Average cell throughput
everywhere in the cell

24. CONCLUSIONS
• Carrier Aggregation has been introduced in LTE-Advanced
system (Rel 10) to address the following main features:
– Flexible spectrum usage: provide wider system bandwidth up to,
e.g., 100 MHz based on 5 CCs with 20 MHz per CCs
– Higher transmission data rate: peak data rates up to 1Gbps in DL
• Simulation results prove that, implementing CA with higher
numbers of CCs improve system performance in term of
user throughput everywhere in the cell
• Implementing CA enhance system capacity in term of active
user’s numbers in the cell in LTE-Advanced systems

25. What’s next?
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BG 1G 2G 3G 4G 5G XG