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200

Fig. 7. Call admission control policy
4.3 Channel searching and replacement (CSR) algorithm
Although the above proposed CAC can handle call requests in both WLAN and cellular
networks, all admission decisions are made based on the situation of each individual
network. To improve the whole system performance, we propose a channel searching and
replacement (CSR) algorithm based on passive vertcial handoff to implement joint resource
management.
Due to different capacities and user densities, the traffic intensities and QoS levels are often
unbalanced in the WLAN and overlaid cellular network. When WLAN becomes congested,
the traffic will be routed to the cellular network automatically. On the other hand, when the
3G cellular network has no resource available for an incoming call requests, our CSR
algorithm is used to find available resources in the WLAN by switching some 3G

Joint Call Admission Control in Integrated Wireless LAN and 3G Cellular Networks

201
connections staying in WLAN area to the WLAN, as shown in Figure 8. Specifically, if there
exists an ongoing cellular connection and the mobile terminal residing in the WLAN area,
and there is still bandwidth available in the WLAN at the same time, the cellular connection
will be switched to the WLAN by vertical handoff, and then the incoming call request will
take the released bandwidth in cellular network to avoid being blocked or dropped. This
kind of vertical handoff is called “passive“ because it is initiated by the system resource
management instead of by users or signal fading.
To achieve the fairness among different service connections, CSR checks the difference of
QoS provisioning in both networks before switching a cellular connection to WLAN. If there

Search for cellular connections but mobile terminal staying in WLAN;
if (at least one cellular connection in WLAN) & (QoS provisioning in
WLAN ) { return 1; }
else {return 0;}

Fig. 8. Channel searching and replacement (CSR) algorithm
4.4 Analysis and comparsion
In this section, the proposed CSR algorithm is compared with traditional disjoint guard
channel (DGC) scheme with system performance metrics, including new call blocking

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202
prabability and handoff dropping probability. To reduce the complexity, we focus on voice
services in the integrated WLAN and 3G UMTS cellular networks, with fixed total channels
in UMTS cell and bandwidth in WLAN.
4.4.1 DGC algorithm
First the traditional DGC algorithm is considered. Assume that the arrival process for both
new calls and vertical handoff follows Poisson distributions, and the channel holding time
for both vertical handoffs and new calls are exponentially distributed. Let
n
λ
and 1/
n
μ

denote the arrival rate and the average channel holding time for new voice call in the UMTS
cell, respectively. Let
v
λ

=
and
nnn
ω
λμ
=
, respectively.
Based on the stationary state distribution, the vertical handoff dropping probability
v
P and
new call blocking probability
n
P , for disjoint guard channel scheme can be expressed as
follows,

()
(
)
()()
∑∑
+=
−
=
−
+
+
+
⋅+
==
C

()
(
)
()()
∑
∑∑
∑
=
+=
−
=
=
−
+
+
+
⋅+
==
C
Gi
C
Gi
Gi
v
G
vn
G
i
i
vn

In the proposed CSR scheme, the total number of occupied channels in the cell and the idle
channels in the WLAN are the keys to deciding whether a new voice calls or a vertical
handoffs need intersystem channel switching through a passive handoff to the WLAN.
When the total channel number i in the cell is larger than Gc, an incoming new call request
can get admission if there is an ongoing cellular connection residing the WLAN and there is
still bandwidth available in the WLAN. When the total occupied UMTS channel number

Joint Call Admission Control in Integrated Wireless LAN and 3G Cellular Networks

203
equals to C, an incoming vertical handoff from WLAN can also be admitted in cellular
network if there is a successful channel replacement in the WLAN. To avoid over-utlization
on WLAN, it is assumed that a call request can get admission with probability
δ
that is
determined by the total number of occupied channels in the cell, the probability for mobile
terminals using ongoing cellular connection while located in the WLAN, and the state of
current occupied channels in the WLAN. Based on the above descriptions, we can get a
Markov chain model for the cellular network, shown in Fig 9(b).
Using CSR, call request blocking or dropping in a cellular network will happen in following
two scenarios:
Scenario 1: There is no idle channel available in cellular network, and no cellular
connections residing in the WLAN;
Scenario 2: There is no idle channel available in cellular network, and no channel within the
WLAN, although there is a cellular connection residing in the WLAN.
So Let P
f
be the probability of an ongoing cellular call remaining in a WLAN, which is
assumed to be determined by a user’s preference for vertical handoff and mobility velocity.
Let

expressed as,

(
)
(
)
12 3
() ()( ) () ()
nv nvn nvnv
iIi Ii Ii
ρ
ρρ ρρω ρρωω
=⋅++⋅+++⋅+++
(4)
where
n
ρ
is original traffic intensity of new call requests in WLAN,
v
ρ
is original call
intensity of vertical handoff requests from UMTS to WLAN. I
i
() are state indicator functions:
1
()Iiequals to 1 when state i smaller than guard channel Gc, otherwise equals to zero.
2
()Iiequals to 1 when state i larger than Gc-1 and smaller than total channels C in UMTS
cell, otherwise equals to zero.
3

=+−⋅⋅
∑
(5)

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204

[
]
{
}
(
)
() 1 () ()
w
vc c b c
PC CpCC
ψψ π
=+−⋅⋅
(6)
where
()
c
i
π
represents the stationary state of occupied channel i in UMTS cell.
Since probability that there is no cellular connection within the WLAN is alway smaller than
1, and same for blocking probability
w

ωω+
vn
ωω+
n
ωδ⋅
v
ω
1
Gc
C
(a) State -transition model for Disjoint Guard Channel scheme in UMTS
Notations:
: Traffic intensity of new voice calls in UMTS cellular network
: Traffic intensity of voice vertical handoff from WLAN to UMTS cellular network
Gc : Guard channels in UMTS cellular network
10
G
C
1
2
G
C
G+1
G+1
G+2
(b) State -transition model for Channel Searching and Exchange scheme in UMTS
ω
ω
n
v

=
+++

s.t. 0 1
δ
≤≤
where
W
1
, W
2
, and W
3
are cost weights for the blocking probability in the cellular network,
the dropping probability in cellular network, and the blocking probability in the WLAN,
respectively.
It is easy to prove that blocking probability in WLAN is a monotonically increasing
continuous function of
δ
, while blocking probability and dropping probability in UMTS
cell are continuous decreasing functions over
δ
in the interval between zero and one. So the
weighted cost function is also a continuous function over the same interval. According to
the Extreme Value Theorem, target cost function has a minimum and a maximum value
over the interval 0 1
δ
≤
≤ . So it is feasible to find out a optimal admission probability for
passive handoff which minimizes the integrated system cost with linear programming. Here

v
ρ
= 5. Since the weight of
handoff dropping is larger than both the weights of blocking calls in cellular network and in
WLAN, the optimal admission probability increases quickly for W3 = 1.3 and W3 = 2.0, and
is 1 when the handoff intensity is larger than 45. In other words, the integrated system
attempts to allocate each idle resource in the WLAN to handoff in cellular network to avoid
larger system cost caused by dropping probability.
In contrast, when new call intensity
n
ρ
in the WLAN increases (
v
ω
is set as 5), the
admission probability for W3 = 2.0 and W3 = 1.3 is reduced to zero, but remains 1 for W3 = 1,
as shown in Figure 11. Again, it is shown that CSR can adjust the traffic intensity among the
two networks to avoid overloaded situation in the WLAN. For W3 = 1.0, since the cost for
blocking a passive handoff is no more than the costs of blocking a new call or dropping a
connection in cellular network, the passive handoff always get an admission into the WLAN.

Recent Advances in Wireless Communications and Networks

206
20 40 60 80 100
0
0.2
0.4
0.6
0.8

δ
can be obtained as 0.078. DGC
has the highest system cost due to its disjoint resource allocation, while oCSR can achieve
the optimal resource allocation with minimum average system cost. Since the cost of oCSR is
less than that of CSR, original CSR in UMTS cellular network is a sub-optimal solution for
the overall resource allocation for integrated networks.

Joint Call Admission Control in Integrated Wireless LAN and 3G Cellular Networks

207
0 2 4 6 8 10 12
x 10
4
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Time (seconds)
Average System Cost
DGC
CSR
oCSR

Fig. 12. System cost of DGC, CSR, and optimal CSR

New call intensity in cellular network


Recent Advances in Wireless Communications and Networks

208
20 30 40 50 60
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
New call intensity in cellular network
Blocking probability
DGC
oCSR

Fig. 14. Blocking probability with optimal CSR and DGC

10 20 30 40 50
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Handoff call intensit

developed and optimized to balance total system traffics between WLAN and 3G cellular
network, as well as to reduce average system QoS cost. A one-dimensional Markov model
for voice traffic is further developed to analyze interworking system performance metrics.
Both theoretical analysis and simulation results show that our scheme outperforms both
traditional disjoint guard channel scheme and non-optimized joint call admission control
scheme.
Our feature work will focus on more real-time services, such as video services, and
investigate interactions between resource management and user mobility in integrated
WLAN / 3G cellular networks.
7. References
Ahmavaara, K.; Haverinen, H. & Pichna, R. (2003), Interworking Architecture between
3GPP and WLAN Systems,
IEEE Communications Magazine, Vol. 41, No.11, (Nov
2003), pp. 74 – 81, ISSN 0163-6804
Ahmed, M. (2005), Call admission control in wireless networks: a comprehensive survey,
IEEE Communications Surveys & Tutorials, Vol. 7, No. 1, May 2005, pp. 50-69, ISSN
1553-877X.
Fang, Y. & Zhang, Y. (2002), Call admission control schemes and performance analysis in
wireless mobile networks,
IEEE Transactions on vehicular Technology, vol. 51, No.2,
(March 2002), pp. 371-382, ISSN 0018-9545
Guerrero, J. & Barba, A. (2008), Policy-based Network Management Reference Architecture
for an Integrated Environment WLAN-3G,
IEEE Latin America Transactions, Vol. 6,
No. 2, (June 2008), pp. 229-234, ISSN 1548-0992
Jia, D. & Mermelstein, P. (1996), Adaptive Traffic Admission for Integrated Services in
CDMA Wirelessaccess Networks,
IEEE Journal on Selected Areas in Communications,
Vol. 14, No. 9, (Dec. 1996), pp.737–47, ISSN 0733-8716.
Klein, T. & Han, S. (2004), Assignment strategies for mobile data users in hierarchical

, pp. 374-379, ISBN 0-7803-8784-8, Las Vegas, Nevada,
January 1- 10, 2005.
Liu, C. & Zhou, C. (2005), An improved architecture for UMTS-WLAN Tight Coupling,
Proceedings of IEEE Wireless Communications & Networking Conference 2005, pp. 1690-
1695, ISBN 0-7803-8966-2, New Orleans, LA, March 13-17, 2005.
Liu, C. & Zhou, C. (2006), Providing Quality of Service in IEEE 802.11 WLAN,
Proceedings of
IEEE Consumer Communications & Networking Conference 2005
, pp. 374-379, ISBN 0-
7803-8784-8, Las Vegas, Nevada, USA, January 1-10, 2006.
Liu, C. (2006). System Design and Resource Management in the Next-Generation Integrated
WLAN / 3G Cellular Networks,
Doctoral dissertation, Florida International University,
Miami, Florida, USA, August 2006.
Liu, C.; Zhou, C.; Pissinou, N. & Makki, K. (2007), Resource Management in the Next-
Generation Integrated WLAN / 3G Cellular Networks,
Proceedings of IEEE Wireless
Communications & Networking Conference 2007
, pp. 3343-3348, ISBN 1-4244-0658-7,
Hong Kong, China, March 11-15, 2007.
Liu, Z. & Zarki, M. (1994), SIR-based Call Admission Control for DS-CDMA Cellular
Systems,
IEEE Journal on Selected Areas in Communications, Vol.12, no. 4, (May 1994),
pp. 638–44, ISSN 0733-8716
Rashad, S. (2006), Mobility-based predictive call admission control and resource reservation
for next-generation mobile communications networks, Doctoral dissertation,
University of Louisville, In ACM digital library, Available from

Shafiee, K. ; Attar, A. & Leung, V. (2011), Optimal Distributed Vertical Handoff Strategies in
Vehicular Heterogeneous Networks,

bit-error-rate, data rate and coverage) in wireless networks. In this work, we study a two-hop
relay channel in which each node can have multiple antennas. It is well-known that utilizing
multiple-input multiple-output (MIMO) links can significantly improve the transmission rate
(see e.g. (4; 5) and references therein). Thus, one can expect a combination of a MIMO gain and
a relaying gain in a MIMO relay link. We focus on one-shot transmission, where the channel is
used once for the transmission of one symbol representing a message. This is often referred to
as uncoded transmission. The main motivation for such a scenario is in considering applications
requiring either low-delays or limited processing complexity.
The capacity of the MIMO relay channel is studied in (6). The work in (9) establishes the
optimal linear relaying scheme when perfect CSI is available at the nodes. The work in (7; 8)
investigates linear relay processing for the MIMO relay channel. In this paper, in contrast
to (6–9), we study an uncoded system, and we propose a nonlinear relaying scheme which is
superior to linear relaying and performs close to the theoretical bound. Our proposed scheme
is based on constellation permutation (10; 11) at the relay over different streams obtained by
channel orthogonalization.
We investigate a two-hop MIMO fading Gaussian relay channel consisting of a source, a
relay and a destination. We assume that all three nodes have access to perfect channel
state information. We propose a nonlinear relaying scheme that can operate close to the
optimal performance. The proposed scheme is constructed using channel orthogonalization
by employing the singular value decomposition, and permutation mapping. We also
demonstrate that linear relaying can amount to a significant loss in the performance.
1.1 Organization
The remainder of the chapter is organized as follows. Section 2 first introduces the two-hop
relay channel model and then explains the transmission protocol and the assumptions on the
channel state information (CSI) at the nodes and finally formulates an optimization problem.
Section 3 simplifies and reformulates the optimization problem introduced in the preceding
section, by channel orthogonalization using SVD. Section 4 introduces a novel relaying
strategy in which the relay first detects the transmitted message and employs permutation
coding over different streams obtained by channel orthogonalization. This section also
10

then formulate the general problem of finding an optimal relaying strategy for the underlying
channel.
We consider Gaussian two-hop communication between a source and a destination, as
illustrated in Fig. 1. The communication is assisted by a relay node located between the source
and the destination. We assume that the relay node has no own information to transmit and
its sole purpose is to forward the information received from the source to the destination. We
additionally assume that all nodes may have different number of antennas. It is assumed that
there is no direct communication between the source and the destination. (This is reasonable
when e.g., the destination is located far away from the source or there is a severe shadow
fading between the source and the destination.) The communication between the source and
the relay takes place in two phases as described in the following.
First–Hop Transmission: During the first phase, the source transmits its information and the
relay listens to the transmitted signal. The received signal vector at the relay, denoted by y
1
,
is given by
y
1
= H
1
x
1
+ z
1
(1)
where H
1
∈ C
[L×M]
denotes the channel between the source and the relay, x

1
x
†
1
}≤P
1
.
Second–Hop Transmission: During the second phase, only the relay transmits and the source
is silent. We assume that the relay uses a forwarding strategy given by the following
deterministic function
f : C
L
−→ C
L
x
2
= f (y
1
)
Since the function f (·) is arbitrary, our model includes linear as well as nonlinear mappings.
We assume an average power constraint at the relay such that trE
{x
2
x
†
2
}≤P
2
.Thereceived
212

transmitted message using the function (demodulator or detector) β defined as
β : C
N
−→ W
ˆ
w
= β(y
2
)
where
ˆ
w ∈ W denotes the detected message at the destination.
Channel Statistics: We assume that the entries of the channel matrices H
1
and H
2
are
i.i.d. Rayleigh fading, distributed according to
CN(0, 1). The entries of the noise vectors
z
1
and z
2
are assumed to be independent zero-mean circularly symmetric Gaussian noise.
The covariance matrices of the noise vectors are given by R
z
1
z
1
= E[z

Channel State Information (CSI): We assume that the source, the relay, and the destination know
H
1
and H
2
perfectly. The CSI of backward channels at the relay and the destination can be
obtained using training sequences and the CSI of the forward channels at the source and the
relay can be obtained either using reciprocity of the links or feedback. When the channel
matrices are constant or varying slowly, one can obtain accurate CSI at the nodes. Satellite
MIMO link and wireless LAN are two practical examples in which this model is applicable.
2.1 Problem formulation
The goal is to minimize the average message error probability. Thus for a given message set W,
we need to find the triple
(α
∗
, β
∗
, f
∗
) under the average power constraint such that
(α
∗
, β
∗
, f
∗
)=arg min
α,β, f
Pr{
ˆ

2
V
†
2
213
Near-Optimal Nonlinear Forwarding Strategy for Two-Hop MIMO Relaying
4 Will-be-set-by-IN-TECH
x
˜y
g
(·)
V
1
U
†
1
V
2
U
†
2
U
1
D
1
V
†
1
U
2

11
˜
y
1r
x
21
x
2t
√
λ
21
√
λ
2t
˜
z
21
˜
z
2t
˜
y
21
˜
y
2t
.
.
.
.

1
, V
1
, U
2
and V
2
are invertible, linear operations of the form of AG or GA (where
G
∈{U
1
, V
1
, V
2
, U
2
} and A is an arbitrary matrix with an appropriate size) impose no loss of
information. Thus we can preprocess the transmitted signal vectors from the source and the
relay and postprocess the received signal vectors at the relay and the destination as illustrated
in Fig. 2. Consequently, the received signal at the relay after the linear postprocessing is given
by
˜y
1
= U
†
1
y
1
= U

z
1
= D
1
x
1
+ ˜z
1
where the last equality follows from the identities U
†
1
U
1
= I
L
and V
†
1
V
1
= I
M
and the
definition ˜z
1
= U
†
1
z
1

1
and D
2
are diagonal matrices,
we have
˜
y
1i
=

λ
1i
x
1i
+
˜
z
1i
, i ∈{1,2, ,min(M, L)}
˜
y
2j
=

λ
2j
x
2j
+
˜

)
where r = min(M, L ) and t = min(L, N). We consider both linear as well as nonlinear
mappings. One can thus optimize the mapping according to
(
α
∗
, g
∗
( ˜y
1
), β
∗
)
=
argmin
{
α:trE[x
1
x
†
1
]≤P
1
}
,
{
g( ˜y
1
):trE[g( ˜y
1

e
1
and P
e
2
denote the average message error probability of the first- and the second hop,
respectively.
Proof. Consider a two-hop channel where the first hop is noise-free and the second hop is
identical to the original channel in Fig. 1. Denote the average error probability of this new
channel by
¯
P
e
. It is easy to see that P
e
≥
¯
P
e
= P
e
2
. In a similar manner we can obtain P
e
≥
˜
P
e
= P
e

P
2
λ
1i
E[x
1i
x
†
1i
]+N
1
is a power normalization factor and 0 ≤ κ
i
≤ 1isapower
allocation factor where
∑
t
i
=1
κ
2
i
= 1. Note that the number of parallel channels that can be
utilized is min
{r, t}, i.e., the minimum number of parallel streams of the first- and second hop.
In (9), it is shown that the strategy given by (6) is optimal if the relay mapping is constrained
to be linear. However as we show, AF is in general suboptimal for the underlying channel.
The received signal-to-noise ratio (SNR) of the ith stream at the destination is given by
γ
AF

N
1
(7)
where P
1i
 E[x
1i
x
†
1i
]. The fact that the received noise at the relay is forwarded to the
destination is the main drawback of AF relaying.
215
Near-Optimal Nonlinear Forwarding Strategy for Two-Hop MIMO Relaying
6 Will-be-set-by-IN-TECH
w
i
{w
j
}
2
q
j=1,j=i
w
i
P
(i)
e
1
1 − P

largest singular value) with full power when r
= 1. Note that the use of weaker streams at
the relay does not improve the performance of AF since all streams are transmitting the same
signal, thus allocating all power to the strongest mode is the optimal solution. Therefore, the
maximum possible achievable SNR for linear relaying when r
= 1, is given by
γ
∗
AF
=
λ
11
λ
21
P
1
P
2
N
1
N
2
+ λ
11
P
1
N
2
+ λ
21

ri
(β
r
( ˜y
1
)) (9)
where
ˆ
ˆ
w
= β
r
( ˜y
1
) is the detected message and β
r
denotes the detector at the relay. The
modulator for generating x
2i
is denoted by α
ri
.WealsohavetrE[x
2
x
†
2
]=P
2
.
The following proposition derives a simple upper bound on the average message error

respectively denote the ith message error probability of the first- and the second hop
and P
e
1
and P
e
1
respectively are the average message error probabilities of the first- and the second hop.
Proof. Consider the transmission of w
i
from the source. The relay either detects the
transmitted message correctly or declares another message. This is illustrated in Fig. 4. Using
Fig. 4, the ith message error probability can be bounded as
P
(i)
e
=(1 −P
(i)
e
1
)P
(i)
e
2
+ P
(i)
e
1
(1 −
i

j=1,j=i
is
transmitted from the relay, under the constraint that the source is transmitted w
i
.The
inequality in (11) follows from the fact that 0
≤ 
i
≤ 1. By taking average over all possible
messages, we have
P
e
=
2
q
∑
i=1
P
(i)
e
p(w
i
) (13)
≤
2
q
∑
i=1

P

i=1
P
(i)
e
1
P
(i)
e
2
p(w
i
) (15)
≤ P
e
1
+ P
e
2
−

min
1≤i≤2
q
P
(i)
e
1
P
(i)
e

1
, P
e
2
})
at high SNR when N
= M.
Proof. For given modulator and optimal demodulator, the error probability at the destination
is upper bounded as
P
DF
e
≤ P
e
1
+ P
e
2
=
a
1
γ
NL
1
+ O

1
γ
NL+1
1

γ
NL+1
1

if N
< M
a
2
γ
LM
2
+ O

1
γ
LM+1
2

if M
< N
(18)
where we used Lemma 2 and γ
1

P
1
N
1
, γ
2

γ
NL
1
+ O

1
γ
NL+1
1

if N
< M
a
2
γ
LM
2
+ O

1
γ
LM+1
2

if M
< N
(19)
Comparing (18) and (19), we see that the upper bound and lower bound meet each other at
high SNR. This therefore establishes the optimality of DF at high SNR.
Proposition 2. DF achieves the optimal diversity order d

strongest mode.
Proposition 3. Relaying using conventional DF (i.e., transmission using the strongest mode) is
optimal at high SNR when N
> M.
Proof. The proof follows from the observation that using only the stream with the strongest
mode of the second hop, one can obtain higher diversity gain compared to the first hop for
any source modulator. Since M
< N,wehave
P
DF
e
≤
a
1
γ
LM
1
+ O

1
γ
LM+1
1

,andP
e
≥
a
1
γ

r
as
¯
α
ri
= κ
i
π
i
(
¯
α
r
) (21)
where κ
i
is a power allocation factor used at ith stream such that {κ
i
} meets the power
constraint trE
[x
2
x
†
2
] ≤ P
2
. Thus, the transmitted signal from the relay over the ith stream
is given by
x

specializes to finding the optimal permutations and the power allocation factors. That is
({κ
∗
i
}
t
i
=1
, {π
∗
i
}
t
i
=1
)=arg min
{κ
i
}
t
i
=1
,{π
i
}
t
i
=1
Pr{
ˆ

= P
2
= P, N
1
= N
2
= 1. From Fig. 5, we
see that linear relaying performs worst, and the proposed DF relaying scheme provides the
best performance. Surprisingly, the performance of the proposed DF is very close to the lower
bound.
0 3 6 9 12
−5
−4
−3
−2
−1
0 AF
DF: 1 Stream
DF: 2 Streams
Lower Bound
P [dB]
log
10
(P
e
)
Fig. 5. Average message error probability (P

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220
Recent Advances in Wireless Communications and Networks
11
Connectivity Support in
Heterogeneous Wireless Networks
Anna Maria Vegni
1
and Roberto Cusani
2

1

interface cards, providing Vertical Handover capability to autonomously select the best access
network. The design of innovative handover mechanisms —sometimes called as handoff—
between heterogeneous mobile devices (e.g. PDA, laptop, smart phones) and seamless
integration of different integrated network (e.g. GSM, UMTS, HSDPA, GPS, WLAN, Bluetooth
and so on) is an open research issue.

Recent Advances in Wireless Communications and Networks

222
In this way, a mobile user can seamlessly switch between different networks, supporting the
same services. This process must be performed to automatically adapt to change access
networks and environments, without any user participation. In order to do this, cross layer
design for multimedia communications is required. Mobile computing then becomes more
feasible, e.g. a mobile user performing a videoconference using UMTS maintains this service
even though the link breaks down, accessing into a WLAN network.
Vertical Handover (VHO) is a mechanism allowing heterogeneous connectivity by enabling
switches from a serving network to a candidate network, whenever users or network
requirements (i.e. power level, network congestions, or other QoS constraints) impose or
suggest it. Notice that VHO allows switching from one access technology to another, thus
offering additional functionalities with respect to classic horizontal handover where mobile
nodes move from an access point to another without changing the serving access network
(Balasubramaniam & Indulska, 2004), (McNair & Fang, 2004).
In this chapter we show how heterogeneous networks for next generation multimedia
systems can cooperate in order to provide seamless mobility support to mobile users
requiring high multimedia Quality-of-Service (QoS) constraints (Knightson et al., 2005).
We describe the traditional techniques of Vertical Handover in heterogeneous wireless
networks. Basically, in Section 2 we introduce the main characteristics of handover process
and our effort is addressed on a first handover classification, which distinguishes between
horizontal and vertical, hard and soft, upward and downward procedures, and more.
Beyond several handover algorithms, in Subsection 2.1 we give an overview of current IEEE
Fig. 1. Heterogeneous networks scenario
Horizontal handover (HHO) occurs between the APs of the same network technology, while
vertical handover (VHO) occurs between APs belonging to different networks. Several kind of
VHO can be envisaged, as described as follows. According to Figure 1, upward vertical
handover is a handover to a wireless overlay with a larger cell size and generally lower
bandwidth per unit area. It makes a mobile device disconnect from a network providing
faster but smaller coverage (e.g. WLAN) to a new network providing slower but broader
coverage.
Viceversa, a mobile device performing a downward VHO disconnects from a cell providing
broader coverage to one providing limited coverage but higher access speed. In this case, a
link layer trigger can inform the mobile device that it is now under the coverage of a new
network (e.g. WLAN) and the mobile node may wish to execute the handover.
Downward VHOs may be anticipated or unanticipated, such that a mobile device may already
be under the coverage of the new network but may prefer to postpone the handover based
on requirements of the applications running on the mobile node. Handover is then
performed later, being already aware of the coverage status of the new network.
A main issue is to decide if or when to start the handover, and who performs it. Handover
policies are based on different metrics for handover decision. Traditional solutions simply
consider RSSI (Received Signal Strength Indication) and channel availability. More
sophisticated handover policies also consider: (i) Quality-of-Service, as different types of
services require various combinations of reliability, latency, and data rate; (ii) costs, i.e.
different networks may employ different billing strategies; (iii) network conditions like
traffic, available bandwidth, network latency, and congestion; (iv) system performance,