Application Signal Threshold Adaptation for Vertical Handoff in Heterogeneous Wireless Networks.

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Application Signal Threshold Adaptation for
Vertical Handoff in Heterogeneous Wireless
Networks
Ben Liang1, Ahmed H. Zahran1, and Aladdin O. M. Saleh2
1Department of Electrical and Computer Engineering, University of Toronto
2Wireless Technology Planning, Bell Canada
Abstract. In heterogeneous wireless systems, the seamless and efficient
handoff between different access technologies (vertical handoff) is essen-
tial and remains a challenging problem. The co-existence of access tech-
nologies with largely different characteristics results in handoff asymme-
try that differs from the traditional intra-network handoff problem. In
the case where one network is preferred, the vertical handoff decision
should be carefully executed, based on the wireless channel state, net-
work layer characteristics, as well as application requirements. In this
paper, we present an adaptive preferred-network lifetime-based handoff
strategy, and investigate the effect of an application-based signal strength
threshold on the signalling load, available bandwidth, and packet delay.
We propose an analytical framework to evaluate the performance of the
converged system. We show how the proposed analytical model can be
used to provide guidelines for the optimization of vertical handoff in the
next generation integrated wireless networks.
1Introduction
Wireless technologies are evolving toward broadband information access across
multiple networking platforms, in order to provide ubiquitous availability of mul-
timedia applications. Recent trends indicate that wide-area cellular networks
based on the 3G standards and wireless Local Area Networks (WLANs) will
co-exist to offer multimedia services to end users. By strategically combining
these technologies, a converged system can provide both universal coverage and
broadband access. Therefore, the integration of heterogeneous networks is ex-
pected to become a main focus in the development toward the next generation
wireless networks [1–3].
Mobility management is a main challenge in the converged network [4,5].
Both intra-technology handoff and inter-technology handoff take place. Intra-
technology handoff is the traditional Horizontal Handoff (HHO) process in which
the mobile terminal hands-off between two Access Points (AP) or Base Stations
(BS) using the same access technology. On the other hand, inter-technology
handoff, or Vertical Handoff (VHO) occurs when the MT roams between different
access technologies. The main distinction between VHO and HHO is symmetry.

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While HHO is a symmetric process, VHO is an asymmetric process in which the
MT moves between two different networks with different characteristics. This
introduces the concept of a preferred network, which is usually the underlay
WLAN that provides better throughput performance at lower cost, even if both
networks are available and in good condition for the user.
There are two main scenarios in VHO: moving out of the preferred network
(MO) and moving into the preferred network (MI) [6]. In either case, it is highly
desirable to associate the MT with the preferred network, as long as the preferred
network satisfies the user application. This can improve the resource utilization
of both access networks, as well as improve the the user perceived QoS. Fur-
thermore, handoff should be seamless with minimum user intervention, while
dynamically adapting to the wireless channel state, network layer characteris-
tics, and application requirements.
In this work, we present an adaptive VHO algorithm which takes into con-
sideration the wireless signal strength, handoff latency, and application QoS and
delay tolerance. Furthermore, it can satisfy the system handoff signalling load, as
well as different application requirements by the tuning of an application specific
signal strength parameter. We further propose an analytical model to evaluate
the performance of adaptive VHO, which is then applied to show how the VHO
decision can be optimized based on multiple conflicting criteria.
2Related Work
The traditional HHO problem has been studied extensively in the past. Several
approaches have been considered in cellular networks using the Received Signal
Strength (RSS) as an indicator for service availability from a certain point of
attachment, as well as for comparison between the current point of attachment
and the candidate points of attachment [7]:
– RSS: handoff takes place if the RSS of the candidate point of attachment
is larger than the RSS of the current point of attachment (RSSnew >
RSScurrent).
– RSS plus threshold: handoff takes place if the RSS of the candidate point of
attachment is larger than the RSS of the current point of attachment and
the RSS of the current point of attachment is less than a certain pre-defined
threshold T (RSSnew> RSScurrentand RSScurrent< T).
– RSS plus hysteresis: handoff takes place if the RSS of the candidate point of
attachment is larger than the RSS of the current point of attachment with
a pre-defined hysteresis margin H. (RSSnew> RSScurrent+ H).
– A dwell timer can be added to any of the above algorithms. In this case,
the timer is started when one of the above conditions is satisfied, and the
MT performs a handoff if the condition is satisfied for the entire dwell timer
interval.
For the VHO process, the RSS’s of heterogeneous networks are not compara-
ble due to the asymmetric nature of the handoff problem. However, they can be

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used to determine the availability as well as the condition of different networks.
On the one hand, the MI decision should be based on the availability of the
WLAN, in satisfactory condition, as well as the user preferences according to
predefined policies [8]. On the other hand, the MO decision should maintain ef-
ficient utilization of the wireless resources, which implies reliance on the WLAN
as far as the network can provide satisfactory service to the user.
As far as we are aware, there are few existing works discussing VHO beyond
direct extensions to the common techniques for HHO. Two general directions for
VHO algorithms are recorded in the literature. One is based on the traditional
strategies of using the RSS that may be combined with other parameters such
as network loading. In [9] the authors use the dwell timer as a handoff initi-
ation criterion for their algorithm to extend the residence time of the MT in
the WLAN. They combine simulation and analysis to show that associating the
MT with the WLAN for the longest possible time result in user throughput im-
provement even during the transition period in which the RSS oscillates around
the receiver sensitivity level. However, they did not define a clear mechanism for
choosing the dwell timer value. In [10], Ylianttila et al. present an algorithm that
adapts the dwelling timer according to the available data rates in both networks
that are defined by the standards. In [11], the same analytical framework of [9]
is extended to include multiple radio network environments. Their main results
show that the effect of the handoff delay seems to be dominant even with the
optimal choice of the dwell timer as in [10]. In [12], Zhu and McNair present
two cost-based policies for VHO, which considers the available bandwidth and
RSS of the available networks. The collective handoff policy estimates one cost
for all services, while the prioritized multi-network handoff policy estimates the
cost for each service independently.
The second approach uses artificial intelligence techniques [13–15] such as
fuzzy logic and neural networks. It is worth mentioning that some of the artificial
intelligence based algorithms are complex and may not be easy to implement in
practical systems. It is possible to extend our work to include improvement using
similar artificial intelligence approaches. However, this is outside the scope of this
paper and will be left for future work.
3Application Life-Time Adaptation
3.1 System Model
We study the overlapping of 3G cellular and WLAN networks. The cellular
network is assumed to provide universal coverage, while WLAN availability is
indicated by the presence of the WLAN beacons [14] that are periodically trans-
mitted by the WLAN AP’s. Mobile-IP is assumed for mobility management.
We assume that WLAN hot spots implement loosely coupled connection [16]
with the 3G network using WLAN gateways. These gateways perform several
tasks including serving as Mobile-IP agents and possibly providing QoS in the
form of multiple service classes defined within the WLAN. However, it is worth

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mentioning that end-to-end QoS support requires other mechanisms such as
differentiated services to be implemented over the entire network path. The
details of such implementation is unimportant to the proposed VHO algorithm
and mathematical analysis. We are mainly concerned about the resultant VHO
delay values.
The MT is equipped with dual interfaces that allow it to communicate with
both networks. However, since Mobile-IP provides only one IP tunnel, the MT
can connect to one network only at a time. In addition, multi-interface mobility
client software is installed on the MT. This software performs Mobile-IP signaling
with the foreign and home agents. It periodically scans the available interfaces
and measures the observed RSS. Then it intelligently selects the best access
network according to the predefined VHO algorithm.
Within the WLAN, a log-linear path loss channel propagation model with
shadow fading is used [17]. The RSS is expressed in dBm as RSS = PT− L −
10nlog(d)+f(µ,σ) , where PT is the transmitted power, L is a constant power
loss, n is the path loss exponent and usually has values between 2 - 4, d represents
the distance of the MT from the AP, and f(µ,σ) represents shadow fading which
is modelled as Gaussian with mean µ = 0 and standard deviation σ with values
between 6-12 dB depending on the environment. We assume that when the RSS
is below a certain interface sensitivity level, α, the MT is unable to communicate
with the AP.
3.2Adaptive Preferred-Network Life-Time Vertical Handoff
For the MO scenario when the MT is within a WLAN, we use the RSS to
estimate the expected duration after which the MT is unable to maintain its
connection with the WLAN. We take into consideration the handoff delay due
to MIP tunnelling, authentication, and service initiation. We further consider
an Application Signal Strength Threshold (ASST), which is the required level
of RSS for the active application to perform satisfactorily.
The ASST is an application dependent parameter which represents a com-
posite of the channel bit error rate, application error resilience, and application
QoS requirements. We present here how the ASST can be incorporated into the
VHO decision. We further discuss in the next section how the ASST can be
adjusted to optimize the overall system performance.
In discrete time, the RSS is expressed as RSS[k] = µRSS[k] + N[k] , where
k is the time index, µRSS[k] = PT − L − 10nlog(d[k]), and N[k] = f(µ,σ).
The averaged RSS, RSS[k], can be estimated using a moving average RSS[k] =
1
Waverage
i=0
by
M1[k] − M2[k]
WslopeTsampling
?i=
Wslope
i=
2
?Waverage−1
RSS[k−i] . The RSS rate of change, S[k], can be obtained
S[k] =
,
(1)
where M1[k] =
?i=Wslope−1
2
Wslope
RSS[k − Wslope+ 1 + i] .
Wslope
2
i=0
−1
RSS[k − Wslope+ 1 + i] , and M2[k] =
2
Wslope

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Then, we estimate the MT lifetime within the WLAN, EL[k], as follows.
EL[k] =RSS[k] − γ
S[k]
,
(2)
where γ denotes the ASST. Thus, EL[k] represents the application specific time
period in which the WLAN is likely to remain usable to the MT.
Based on the measured and estimated parameters, the MT will initiate the
MO handoff at time k if the averaged received signal strength is less or equal to
a predefined MO threshold, MOTWLAN, and the estimated lifetime is less than
or equal to the handoff delay threshold, THO. The MOTWLANis usually chosen
to be a few dB above the wireless interface sensitivity. THO can be set to the
expected handoff delay between the two access technologies. This delay includes
several signaling delay components such as discovery delay, authentication delay,
and registration delay.
In general, a larger averaging window size results in better estimation but
also larger delay in handoff performance [7]. Hence, using variable window sizes
that adapt to the MT mobility can improve handoff performance. For example,
Waverageand Wslopecan be determined by Waverage= max
?
the averaging and slope distance windows respectively, Tsamplingis the sampling
interval used in sampling the RSS values, ?·? represents the greatest lower integer
function, and V is the MT velocity away from the AP, which can be obtained
by many velocity estimators proposed in the past, for example [18].
In the MI decision, a main factor is the availability of the WLAN, which
can be determined by the RSS of the WLAN. Other factors including the QoS,
specified in terms of the available bandwidth, security, and user preference can
also be considered. In this work, we assume a simplified model where the MT
performs MI to the WLAN if RSS[k] > MITWLAN and the available band-
width is greater than the required bandwidth. In our simulation and analysis,
we assume that the WLAN is always in good condition, so that the MT always
perform an MI after an unnecessary MO.
In the next section, we provide an analytical framework for evaluating the
performance of the proposed cross-layer adaptive vertical handoff.
?
10,
?
Daverage
V Tsampling
??
and Wslope= 2 ∗ max
50,
?
Dslope
V Tsampling
??
, where Daverageand Dsloperepresent
4Performance Analysis
4.1Transition probabilities
The calculation of the transition probabilities is based on recursive computation
of the handoff probabilities similar to [19]. In the following analysis, we consider
– PW[k]: Pr{MT is associated with the WLAN at instant k}
– PC[k]: Pr{MT is associated with the 3G network at instant k}
– PW|C[k]: Pr{MT associates itself with the WLAN at instant k given that it
is associated with the cellular network at instant k-1}