Channel Utilization and Network Selection for Cellular-WLAN

Channel Utilization and Network Selection Policy for Cellular-WLAN Integration
Wong Hui Shin
Faculty of Computing and Informatics,
Multimedia University,
Cyberjaya, Selangor, Malaysia.
hswong@mmu.edu.my
Daniel K S Wong
Faculty of Information Technology,
Malaysia University of Science and Technology
Petaling Jaya, Selangor, Malaysia.
dwong@must.edu.my
Abstract— The ultimate goal of implementing Cellular-WLAN
Integration is to combine Cellular and WLAN advantages to
allow subscribers to enjoy higher data rate and lower cost of
voice and data services, along with authentication, mobility and
accounting features. This paper illustrates a computer
simulation to justify the Cellular-WLAN Integration policies
from the regional coverage perspective and implementation
cost perspective. A computer simulation for Cellular-WLAN
Integration performance is studied, planned, designed and
implemented. This research focused on assessing the
performance implication of channel utilization and network
selection during frequent traveling.
Keywords: Cellular-WLAN Integration, Channel
Utilization and Network Selection
1.0 Introduction
The idea of Cellular-WLAN integration is getting
great support by the giant companies such as Malaysia’s Maxis
Communications Bhd, Malaysia’s Digi Telecommunication,
Japan’s NTT, Singapore’s StarHub, US-based T-mobile, Italy’s
Telecom Italia, Australia’s Telstra, Avaya/Proxim/Motorola,
Nokia, British Telecom, Texas Instruments, Toshiba, Intel,
Cisco and etc. They have put in the efforts and walk out the
first step to make Cellular-WLAN Integration a reality.
Cellular-WLAN Integration refers to a mixture of Cellular
Network and WLANs interconnected together. Cellular
Networks refer to GSM/GPRS, UMTS Release 99’, cdma2000,
etc. and the WLAN networks refer to wireless LANs (IEEE
802.11), HiperLan/2, etc (J.Blau, 2002).
Cellular-WLAN integration is an advantageous
combination of Cellular Network and WLAN (3GPP TS
23.234, 2004). It offers broad coverage and minimum
deployment and consumption costs for the objective of
providing a better quality and higher bandwidth of
communication to the subscribers. The subscribers are
provided with a seamless service and connection within
roaming agreement interconnected networks environment by
maintaining one account with a cellular operator. Subscribers
also enjoy roaming facilities when they travel into other
cellular operator’s or WLAN service providers’ service
regions. A computer simulation for Cellular-WLAN
integration is used to justify the Cellular-WLAN Integration
policies from the regional coverage perspective and
implementation cost perspective.
2.0 Network Selection Policy
Network Selection Policy is mainly depends on the used
network selection algorithm in a system. Besides, Network
Selection Policy considers the network selection priority list.
The issue rises for the conflicts or difficulties network selection
is from the agreement between the service provider and mobile
operator. The compromised agreement or a win-win
agreement between service provider and mobile operator
definitely reduced the conflicts and difficulties for network
selection.
The users in this content refer to the Cellular Network
Subscribers. The subscribers are flexible enough to choose
either a manual mode or an auto mode for the network
selection within some intersection of Cellular Networks and
WLAN. As also mentioned in Chapter 1, the subscribers have
the right to list down their preference Cellular Network and
WLANs during the service registration time.
In this context, the simulation environment has limited
Network Selection Policy into 3 types.
3.0 Channel Utilization Policy
The simulations are being done to find out the Percentage
of Dropped Call VS Initial Channel Allocation, I as a
Percentage of Total Number of Users, N based on Network
Selection Policy 3 by playing around the Channel Utilization
Policy’s Simulation Parameters by I) fixed value of Average
Time Interval for ON status, Average Time Interval for IDLE
status and same Mobility Model with Total Number of Users is
vary; II) fixed value of Average Time Interval for ON status,
Average Time Interval for IDLE status and Total Number of
Users with Mobility Model is vary, III) fixed value of Average
Time Interval for IDLE status and same Mobility Model with
Total Number of Users is vary and Average Time Interval for
ON status is vary, IV) fixed value of Average Time Interval
for ON status and same Mobility Model with Total Number of
Users is vary and Average Time Interval for IDLE status is
vary. A simulation is being done by using different Network

Selection Policies, V) fixed value of Average Time Interval for
ON status, Average Time Interval for IDLE status, Total
Number of Users and same Mobility Model with types of
Network Selection Policy is vary. Graph 6, Graph 7, Graph 8,
Graph 9, Graph 10, Graph 11, Graph 12, Graph 13, Graph 14
and Chart 1 are plotted based on the simulation processed data
from Channel Utilization Output Calculation.
3.1 The Influences of the different Number of Users
Graph 1: Percentage of Dropped Call VS Initial Channel
Allocation, I as a Percentage of Total Number of Users, N
where Average Time Interval for ON status = 3 min, Average
Time Interval for IDLE status = 9 min, Total Number of Users
= 25, 50 & 75, Policy 3 and Random Mobility Model with
100% moving (Simulation 3, 9 & 15)
Graph 1 shows that simulation with 25 users has the least
percentage of dropped call follows by simulation with 50 users
and simulation with 75 users. The simulation with more users
will have higher percentage of dropped call. The simulation
with 25 users and 50 users reach the lowest percentage of
dropped call when it has 40% of I as a percentage ofN.
3.2 The Influences of the different Mobility Model
The users’ initial location is started with uniform
distribution. Graph 2 shows that there is no significant
different between the simulation with mobility model 20%
users moving and 100% users moving. The graph also shows
the simulation with mobility model 20% users moving and
100% users moving reach the lowest percentage of dropped
call when it has 40% of I as a percentage of N.
3.3 The Influences of different average interval time for
ON status with same IDLE status
In general, the Graph 3, Graph 4 and Graph 5 show that the
simulation with longest Average Time Interval for ON status
will hit the highest percentage of dropped call. This is because
within a longer Average Time Interval for ON status within a
Cellular-WLAN Integration environment, more request
channels are being made by the users.
= 25, 50 & 75, Policy 3 and Random Mobility Model, 100%
Users Moving and 20% Users Moving (Simulation 9 & 19)
where Average Time Interval for ON status is vary, Average
%of Dropped Call VS I asa %of N
-20
0
20
40
60
80
100
120
0.00 20.00 40.00 60.00 80.00 100.00 120.00 140.00 160.00
I as %of N
%
of
Dropped
Call
25 Users 50 Users 75 Users
%of Dropped Call VS I as a %of N
0
20
40
60
80
100
120
0.00 25.00 50.00 75.00 100.00 125.00 150.00
I as a %of N
%
of
Dropped
Cal
100% Users Moving
20% Users Moving
0
20
40
60
80
100
120
0.00 50.00 100.00 150.00 200.00 250.00 300.00
I as a %of N
%
of
Dropped
Cal
N=25, O=3Min
N=25, O=6Min
N=25, O=9Min

= 25, Policy 3 and Random Mobility Model, 100% Moving
(Simulation 3, 5 & 6)
The Graph 3 and Graph 4 also shows those simulations
dropped call when it has 40% of I as a percentage of N
regardless of the time length for Average Time Interval for ON
status. However, those simulations with 75 users reach the
lowest percentage of dropped call when it has 55% of I as a
percentage of N.
3.4 The Influences of different average interval time for
IDLE status with same ON status
In general, the Graph 6, Graph 7 and Graph 8 show that the
simulation with longest Average Time Interval for IDLE status
will hit the lowest percentage of dropped call. This is because
within a longer Average Time Interval for IDLE status within a
Cellular-WLAN Integration environment, less request channels
are being made by the users.
The Graph 6 and Graph 7 also shows those simulations
dropped call when it has 40% of I as a percentage of N
regardless of the time length for Average Time Interval for
IDLE status. This means the total numbers of channel requests
within the simulations with 25 users and 50 users are able to
get communicated although the initial channel allocation is
60% less than the total number of users. However, those
simulations with 75 users reach the lowest percentage of
dropped call when it has 55% of I as a percentage of N. In
simulation with 75 users, the total numbers of channel requests
are able to get communicated although the initial channel
allocation is 45% less than the total number of users.
where Average Time Interval for ON status = 3, Average Time
Interval for IDLE status is vary, Total Number of Users = 25,
Policy 3 and Random Mobility Model, 100% Moving
(Simulation 1, 2, 3 & 4)
20
30
40
50
60
70
80
90
100
110
0.00 50.00 100.00 150.00
I as a %of N
%
of
Dropped
Cal
N=50, O=3Min
N=50, O=6Min
N=50, O=9Min
30
40
50
60
70
80
90
100
110
0.00 20.00 40.00 60.00 80.00 100.00 120.00
I as a %of N
%
of
Dropped
Cal
N=75, O=3Min
N=75, O=6Min
N=75, O=9Min
0
20
40
60
80
100
120
0.00 50.00 100.00 150.00
I as a %of N
%
of
Dropped
Cal
N=25, Id=3Min
N=25, Id=6Min
N=25, Id=9Min
N=25, Id=12Min

(Simulation 7, 8, 9 & 10)
(Simulation 13, 14, 15 & 16)
3.5 The Influences of the type of Network Selection
Policies
Chart 1: Percentage Dropped Call VS Channels Condition
= 50 and Random Mobility Model (Simulation 11) (Please
refer to Table 11 for the definition of Channel Condition)
Chart 1 shows that the Percentage Dropped Call in general
versus the channel conditions in Cellular-WLAN Integration
Environment with respect of 3 different Network Selection
Policies. Observation shows Network Selection Policy 2 has
the highest percentage dropped call. Network Selection Policy
3 achieves a higher percentage of dropped call than Network
Selection Policy 1. As a general conclusion from Chart 1,
Network Selection Policy 1 which has applied the “Directed
Retry” network selection method within the Cellular-WLAN
Integration environment gains the least percentage of dropped
calls while the Network Selection Policy 2 gains the highest
dropped call as a result of only applying the “Directed Retry”
network selection method within the Cellular network only.
0
20
40
60
80
100
120
0.00 20.00 40.00 60.00 80.00 100.00 120.00
I as a %of N
%
of
Dropped
Cal
N=50, Id=3Min
N=50, Id=6Min
N=50, Id=9Min
N=50, Id=12Min
0
20
40
60
80
100
120
0.00 20.00 40.00 60.00 80.00 100.00 120.00
I as a %of N
%
of
Dropped
Cal
N=75, Id=3Min
N=75, Id=6Min
N=75, Id=9Min
N=75, Id=12Min
%of Dropped Call VS Channel Condition
0
10
20
30
40
50
60
70
80
90
100
A B C E F G H
Channel Condition
%
of
Dropped
Call
Policy 3 Policy 2 Policy 1

= 50 and Random Mobility Model (Simulation 11)
From Graph 9, the observation shows Network Selection
Policy 2 and Network Selection Policy 3 have achieved lowest
percentage of dropped call (<20%) when they are 40% I as a
percentage N. This means the total numbers of channel
requests are able to get communicated although the initial
channel allocation is 60% less than the total number of users.
Network Selection Policy 1 is using the “directed retry”
network selection approach to aim for having less initial
channel allocation (the total channels available for each of the
networks) within the Cellular-WLAN integration, but it able to
support more users. Observation from Graph 1 shows the
Network Selection Policy 1 have achieved up to 80% initial
channel allocation less than the total number users. This
encourages a broader coverage networks with lower cost
implementation. And, the simulation with Network Selection
Policy 1 has minimum of percentage ofdropped call.
4.0 Conclusion
Based on the simulation results, in general, a small
conclusion is drawn that each of the simulation parameters
does affect the percentage of Dropped Call within a simulation
environment. Based on all simulations with Network Selection
Policy 3, the following conclusions are found.
There is no significant different between the simulation
with mobility model 20% users moving and 100% users
moving.
Higher number of users (75 users) leads to higher
percentage of dropped call (≈0%) at low initial channel
allocation as a percentage of total number of users (≈40%).
Lower number of users leads to lower percentage of dropped
call (<40%) at high initial channel allocation as a percentage of
total number of users (<60%).
Longer time length of average time interval for ON status
and lower number of total users (25 users) lead to higher
percentage of dropped call (<40%) at low initial channel
Shorter time length of average time interval for ON status and
lower number of total users (25 users) lead to lower percentage
of dropped call (≈10%) at low initial channel allocation as a
percentage of totalnumber of users (≈40%).
Longer time length of average time interval for OFF status
and lower number of total users (25 users) lead to lower
percentage of dropped call (<10%) at low initial channel
Shorter time length of average time interval for OFF status and
lower number of total users (25 users) lead to higher
percentage of dropped call (≈40%) at low initial channel
allocation as a percentage oftotal number of users (≈40%).
After the observation from simulations with Network
Selection Policy 3, the percentage of dropped call can be
minimized more by implementing appropriate network
selection method like Network Selection Policy 1 instead of
Network Selection Policy 3. Network Selection Policy 1 leads
to the lowest percentage of dropped call (<20%) by having
maximum of 80% initial channel allocation less than the total
number of users. Network Selection Policy 2 and Network
Selection Policy 3 achieve higher percentage of dropped call
than Network Selection Policy 1 by having maximum of 60%
initial channelallocation less than the totalnumber of users.
5.0 Future Enhancement & Suggestion
The random uniform movement model adopted in a
building simulation environment in this research is a weakness
of this research. The mobile equipments start with a random
uniform distributed location and their next movement location
is a random uniform distributed location too. There is an
option setting to limit only 20% of mobile equipment will
move around in a building while 80% of the mobile equipment
will stay static in a building. This might not be a realistic
movement model within a building however; the research
questions are answered by compromising the usage of random
uniform movement model in a one-dimensional building view
simulation environment. The reason behind is that the
planning of the location and the radius coverage of a WLAN
Access Point are not properly done, therefore, the random
uniform movement model can project some situation whereby
the simulation is not done within a building. Also, the user
equipment within a building is very rarely to walk out from
their subscribed WLAN, this shows the difficulties to justify
the distinctions between the defined policies.
This research still can be improved from the following 3
perspectives.
0
20
40
60
80
100
120
0.00 20.00 40.00 60.00 80.00 100.00 120.00 140.00 160.00
I as %of N
%
of
Dropped
Call
Policy 3 Policy 2 Policy 1

Network Selection Policy Perspective - Load balancing is
proposed as an enhancement for the “Directed Retry” network
selection method. The load balancing is used in the situation
of the number of users in a Network exceeds a certain
threshold, the existing users are advised to switch to adequate
neighboring cell to avoid disappointment from the new users’
request.
Channel Utilization Policy Perspective – The observation
from Graph 6 shows the Network Selection Policy 1 have
achieved up to 80% initial channel allocation less than the total
number of users. It is suggested to work future to improve
Network Selection method or algorithm for the Network
Selection Policy. The user mobility model should be improved
by implementing some movement pattern instead of randomly
uniform distributed regardless ofspeed and direction.
Simulator Perspective – The interface of the simulator
should be more user friendly, so that the users are able to add
in the simulation parameters fromthe interface and get the final
out come result with graph and charts. Besides, the simulations
in table 12 should be tested by a more realistic movement
model in different simulation environment such as the user
equipment moving on the road and the user equipment moving
outside the building with different traveling speed.
6.0 Summary
The influence of different type of network selection policy,
total number of initial channel allocation versus total number of
users for each of the networks on channel utilization are
analyzed by using mathematical analysis and computer
simulations. The analysis results are considered with the
proposed Cellular-WLAN integration communication
scenarios.
Direct roaming agreement, indirect roaming agreement and
service agreement between Cellular operator and WLAN
service providers ensure subscribers to get connected with
maximum coverage when they travel.
It is crucial to have an excellent Network Selection Policy
with the dynamic network selection approach or algorithm,
such as Network Selection Policy 1 as stated in Table 10 which
is able to work on the statement to minimize the initial channel
available in each of the network to accommodate the maximum
number of userchannelrequest.
A best combination of simulation parameters within a
Channel Utilization Policy is needed. The proposed Channel
Utilization Policy states a Network Selection Policy with a
network selection approach or algorithm is needed to achieve
more than 80% of Initial Channel Allocation less than the total
number of User Channel Request within a Cellular-WLAN
Integration. It is hard for us to predict the call arrivals pattern
from the users, however, with the negatively-exponential
distribution of Random Communication Time or Idle time, the
time period for Average Time Interval for ON state or Average
Time Interval for IDLE state is crucial. The Average Time
Interval affects the percentage of Dropped Call in Cellular-
WLAN integration system.
The Chart 1, the Percentage Dropped Call VS Channel
Condition shows the outstanding of the Network Selection
Policy 1 where network uses “Directed Retry” in Cellular-
WLAN Integration to dynamically relocate some of the
channel requests to another networks according to Network
Selection Priority List when the network faces less channel
supply.
The Channel Utilization Policy includes the proposal of the
best combination of the optimal number of users, number of
WLAN channel, number of Cellular Network channel in an
exponential call arrival communication scenario to maximize
overall performance of the Cellular-WLAN Integration. By
excluding the movement pattern of the users’ mobility model,
according to our observation in plotted graphs in Chapter 7, the
channel utilization policy within the Cellular-WLAN
integration is proposed as following:
The ratio of the total number of initial channel allocation, I
to total number of users, N within Cellular-WLAN Integration
Environment is 5I:3N. It means the total number of initial
allocation could be set at a level with 60% less than the total
number of users.
Total number of initial channel allocation for WLANs must
be more than total number of initial channel allocation for
Cellular WLAN at a ratio of at least 7:3.
A Cellular-WLAN integration environment with excessive
communication traffic should be incorporated with a network
selection algorithm to achieve a minimum percentage of
dropped call.
To conclude, Cellular-WLAN Integration is a feasible
solution to the community for the purpose of providing broad
coverage and low cost in term of technology implementation
and subscribers usage charges perspective.
6.0 References
[1] 3GPP TS23.234. (2004). "3rd Generation Partnership Project; Technical
Specification Group Service and System Aspects; 3GPP system to
Wireless Local Area Network (WLAN) interworking; System
Description V6.2.0", Sept 2004.
Specification Group Service and System Aspects; 3G Security; Wireless
Local Area Network (WLAN)interworkingsecurity V6.2.1", Sept 2004.
[3] 3GPP TS22.234 (2004). "3rd Generation Partnership Project; Technical
Specification Group Service and System Aspects; Requirement on 3GPP
system to Wireless Local Area Network (WLAN) interworking V6.2.0",
Sept 2004.
[4] 3GPP TR 22.934. (2003). "3rd Generation Partnership Project;
Technical Specification Group Service and System Aspects; Feasibility
Study on 3GPP System to Wireless Local Area Network (WLAN)
InterworkingV6.2.0",Sept 2003.
Specification Group Service and System Aspects; 3G Security; Security
architecture",Sept 2003.
[6] 3GPP TR 21.133. (2001). "3rd Generation Partnership Project;
Technical Specification Group Service and System Aspects; 3G
Security; V4.1.0 (2001-12)".
[7] 3GPP S2-031430 (2003). “PLMN Selection for 802.11 Type of
WLAN”, Nokia,

[8] Retrieved April, 2003, from Web site:
http://www.3G.org/ftp/tsg_sa/WG2_Arch/TSGS2_31_Seoul/tdocs/ S2-
031430.zip
[9] A. A. William, S. Narendar & W. YC. Justion. (2001). "Your 802.11
wireless network has no clothes”. April 2001.
[10] B. James. F (2004). “Traffic Generation Concepts Random Arrivals”,
retrieved Nov. 20, 2004, from Web site:
http://www.perfdynamics.com/Classes/Materials/BradyTraffic.pdf
[11] B. Nikita, G. Ian, and W. David (2001). “Security of WEP Algorithm”,
retrieved June. 20, 2004, from Web site:
http://www.isaac.cs.berkeley.edu/isaac/wep-faq.html
[12] C. Rigney et al. (2000). “Remote Authentication Dial In User Service
(RADIUS)”, IETF RFC 2865, June 2000.
[13] C. Roy, M. Dennis & M. Suvda. (2002). “Analysis of 3G Mobile
Security”, Motorola, Software Research Group, Department of
Computer Science, University of Illinois at Urbana-Champaign,
retrievedMay. 01, 2004, from Website:
[14] http://choices.cs.uiuc.edu/MobilSec/#Tools
[15] Cisco Systems, Inc. (1993-2003). “Cisco ITP MAP Gateway for Public
WLAN SIM Authentication andAuthorization whitepaper”.
[16] Digi Malaysia. (2004). “DiGi's first HighSpeed Mobile NetworkTM
”,
retrieved Jun. 06, 2004, from Web site:
http://www.digi.com.my/mobile/gprsedge/
[17] ETSI TC-GSM. (1993). “GSM Security Aspects (GSM 02.09)”,
Recommendation GSM 02.09, Version 3.1.0, European
Telecommunications Standards Institute (ETSI), June 1993.
[18] ETSI TC-GSM. (1994). “European Digital Cellular Telecommunications
System (Phase 2): Security Related Network Functions (GSM 03.20).
ETS 300 534, EuropeanTelecommunications Standards Institute (ETSI),
September 1994.
[19] Gartner, Inc. (2003). “Enterprises Must Consider Six Types of WLAN
‘Hot Spots’”, June.
[20] Global Information, Inc. (n.d.). “Wi-Fi Hotspot Operator Case Studies”,
retrieved Oct. 06, 2004, from Web site:
http://www.gii.co.jp/english/ba11884_hotspot_operator_toc.html
[21] G. Schäfer. (2003). “Netzsicherheit - Algorithmische Grundlagen und
Protokolle”, dpunkt.verlag, 2003.
[22] H. Haverinen & J. Salowey. (2005). Work in progress: “Extensible
Authentication Protocol Method for GSM Subscriber Identity Modules
(EAP-SIM)”, draft-haverinen-pppext-eap-sim-14.txt, April 25, 2005,
Internet Draft.
[23] IEEE Std 802.11a-1999. (1999). “Part 11: Wireless LAN Medium
Access Control (MAC) and Physical Layer (PHY) Specifications; High
Speed Physical Layerin the 5 GHz Band.”
[24] Intel Corporation. (2003). “Public WLAN Hotspot Deployment and
Interworking”. Intel Technology Journal, Volume 07, August, 19,
retrieved Sept. 19, 2004, from Web site:
http://www.intel.com/technology/itj/2003/volume07issue03/art02_hotsp
ot/p02_intro.htm
[25] Intel Corporation and Infocomm Development Authority of Singapore
(2003-2004).
[26] (2004). “Public WLAN Interworking Study”, Draft Revision 0.8,
January 12, 2004.
[27] G. Schafer, A. Festag & H. Karl. (2004). “Current Approaches to
Authentication in Wireless and Mobile Communications Networks”.
Telecommunication Networks Group, Technical University of Berlin, 26
March 2001, Version1.0
[28] J. Arkko & H. Haverinen. (2005). Work in progress: “Extensible
Authentication Protocol Method for UMTS Authentication and Key
Agreement (EAP-AKA)”, draft-arkko-pppext-eap-aka-13.txt, April 25,
2005, Internet Draft.
[29] J.Blau. (2002). “WiFi” Hotspot Networks Sprout Like Mushrooms”,
IEEE Spectrum,Sept. 2002,pp.18-20.
[30] J. Schiller. (1994). “Mobile Communications - The Course”, Retrieved
Sept.
[31] 18, 2004, from Web site: http://www.inf.fuberlin.de/inst/ag-
tech/resources/mobile_communications.htm
[32] K. Ahmavaara, H. Haverinen & R. Pichna. (2003). "Interworking
architecturebetween
[33] 3GPP and WLAN systems, IEEE Communications Magazine, Volume:
41, Issue: 11, Pages:74– 81, November 2003.
[34] L. Blunk & J. Vollbrecht. (1998). “PPP Extensible Authentication
Protocol (EAP)”, IETF RFC 2284,Mar 1998.
[35] L. Blunk et al. (2003). Work in progress: “Extensible Authentication
Protocol (EAP)”, draft-ietf-eap-rfc2284bis-04.txt, June 2003, Internet
Draft.
[36] L.William.(2003). "The effort has been done."
[37] M.Arunesh & A. A. William. (2002). "An initial security analysis of the
IEEE802.1xstandard ".
[38] M.Burrows, M. Abadi & R. Needham. (1989). "A logic for
authentication". DES System Research Technical Report No. 39, Feb
1989.
[39] M. K. Koien, T. Haslestad. (2003). "Security Aspects of 3G-WLAN
Interworking”, IEEE
[40] Communications Magazine, Volume: 41, Issue: 11, Pages:82 – 88,
November 2003.
[41] MIT, 18.013A. (2001). “Mathematic 18.013a Online Textbook:
Calculation With Applications, Fall 2001”. Chapter 2, USA, retrieved
Jan. 06, 2005, fromWebsite:
[42] http://ocw.mit.edu/ans7870/18/18.013a/textbook/HTML/chapter02/secti
on01.html
[43] S. Fluhrer, I. Mantin & A. Shamir. (2001). "Weaknesses in the Key
Scheduling Algorithm" of 8th
Annual Workshop on Selected Areas in
Cryptography, August 2001.
[44] S. K. Apostolis, Motorola. (2004). “Interworking Techniques and
Architectures for
[45] WLAN/3G Integration Toward 4G Mobile Data Networks”. IEEE
Wireless Communications, June 2004.
[46] The Star News (2004). “Global WiFi roaming from Maxis”, Retrieved
Thursday, November.
[47] 18, 2004, fromWebsite: http://www.thestar.com.my
[48] Trillium Digital Systems, Inc. (2000). “Third Generation (3G)
Whitepaper”.Los
[49] Angeles, CA., retrievedJun. 06,2004, from Website:
[50] http://www.trillium.com/assets/wireless3g/whitepaper/8722019.pdf
[51] Wong, Hui Shin., & KDaniel, Wong. (2004). Re-authentication Policy
for Cellular-WLAN Integration. Proceedings of the International
Computer Engineering Conference 2004, Computer Engineering
Department, Faculty ofEngineeringCairoUniversity, Egypt.

Channel Utilization and Network Selection for Cellular-WLAN

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Channel Utilization and Network Selection for Cellular-WLAN

Similar to Channel Utilization and Network Selection for Cellular-WLAN (20)

More from Hui-Shin Wong

More from Hui-Shin Wong (20)

Recently uploaded

Recently uploaded (20)

Channel Utilization and Network Selection for Cellular-WLAN