I. J. Communications, Network and System Sciences. 2008; 1: 1-103
Published Online February 2008 in SciRes (http://www.SRPublishing.org/journal/ijcns/).
Copyright © 2008 SciRes. I. J. Communications, Network and System Sciences. 2008; 1:1-103
Any Resource Sharing via HWN* Routing
Chong SHEN, Susan REA, Dirk PESCH
Centre for Adaptive Wireless Systems, Department of Electronic Engineering
Cork Institute of Technology Ireland
Abstract
In this paper we present an adaptive distributed cross-layer routing algorithm (ADCR) for hybrid wireless network
with dedicated relay stations (HWN*). To support versatile multimedia communication for Mobile Terminals (MTs),
the HWN* integrates a cellular network, an ad hoc network and fixed relay nodes (RNs). A MT may borrow cellular
data channels that are available thousands mile away via secure multi-hop RNs, where RNs are placed at flexible
locations in the network. The MT can also communicate with each other or access internet ubiquitously. We discuss
cross media access and network layers routing issues. The ADCR establishes routing paths across RNs or cellular
network while providing appropriate QoS (quality of service). Through simulation, we verify the routing performance
benefits of HWN* over conventional cellular systems and other hybrid network frameworks. It is anticipated that the
simulation results reported in this paper will serve as a guideline for research based on distributed source routing
involving heterogeneous wireless technologies.
Keywords: Routing, QoS, Cross-layer, Traffic Management, HWN*
1. Introduction
The recent widespread uptake of high-data rate and
multimedia services has led to a shift in how cellular
networks are being used. This is motivating wireless
providers to develop innovative infrastructure and
accompanying routing and radio access algorithms. Due
to the small size of the cells of micro cellular networks
and the uneven nature of the time varying spatial
distribution [1], network performance metrics such as
overall throughput, Mobile Terminal (MT) throughput,
call blocking probability, handover rate, end-to-end delay,
etc. are not sufficient for today’s wireless network where
more ad hoc features are being introduced. The traffic in
heterogeneous environments is a mixture of real-time
packets with widely varying support on Quality of
Service (QoS) levels. Recently, Channel Access
Scheduling (CAS), Medium Access Control (MAC) and
distributed routing schemes were investigated for Mobile
Ad Hoc Networks (MANETs) with scenarios involving
military networks, emergency services, and inexpensive
deployment of sensor networks [2]. But these are energy
constrained networks with limitations on capacity and
QoS support since as the ad hoc network topologies can
rapidly change.
To effectively manage problems stated above, we
propose to combine the advantages of different networks
so that the MTs can utilise an optimised MANET or the
Base Station Oriented Network (BSON) and packet relay
Figure 1. The hybrid wireless network with fixed relay
stations and IP network
services. Figure 1 presents our Hybrid Wireless Network
with Relay Nodes (HWN*) connected to the Internet
Protocol (IP) networks where relay nodes (RNs) compose
a mesh like structure through RN gateways, while Base
Stations (BSs) are connected to the IP networks via
switches. Other than the improvement of downlink power
efficiency, the primary goal of the RN incorporation in
HWN* is to provide uniform coverage and support the
hybrid network relay. Two MTs may communicate
directly, or through an intermediate node. This node can

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Copyright © 2008 SciRes. I. J. Communications, Network and System Sciences. 2008; 1:1-103
be a MT, a fixed RN or a group of RN matrices. When a
MT transmits packets to a BS through RNs, the RNs
extend the signaling coverage of BSON thus we can
expect an enhanced resource sharing performance. A
further discussion on the HWN* topology description can
be found in [3].
We propose an Adaptive Distributed Cross-layer
Routing (ADCR) algorithm in HWN* to (a) route packet
using the minimal number of relay hops to reduce latency
preserve communications, (b) deliver good overall
throughput and per node throughput and (c) enable low-
complexity router implementation. A cross-layer network
design [4] that seeks to enhance the performance of a
system by jointly designing MAC and NETWORK layers
is desirable. In the design stage of the ADCR algorithm
development, we analysed the theoretical cellular network
media access capacity, multi-hop traffic relaying issues
and inter network traffic handovers. The cascaded ADCR
scheme includes three sub packet transmission modes
labeled as One-Hop Ad-Hoc Transmission (OHAHT) for
point to point ad hoc direct communication, Multi-Hop
Combined Transmission (MHCT) for cellular resource
relaying using fixed RNs and Cellular Transmission (CT)
for traditional cellular service. Our approach allows upper
layers to better adapt their strategies to varying link and
network conditions while the resulting flexibility helps to
improve access speed, end-to-end delay and dynamic
resource management performance.
This paper begins with a hybrid wireless networks
literary review, including up to date network deployment
scenarios and infrastructure dimensioning analysis. In
Section 3, we present the cellular network, ad hoc
network and relay network formation algorithm. The
practical RN positioning strategy is also investigated to
boost the HWN* performance. We then discuss ADCR
algorithm design criteria with three traffic transmission
modes in Section 4. A novel MT mobility model is
presented in Section 5. In Section 6, we verify the ADCR
performance benefits of the HWN* over conventional
cellular system, ad hoc network and other hybrid wireless
networks in terms of network capacity, per MT capacity,
access speed and end-to-end delay. Finally in Section 7,
we present conclusions and discuss future directions for
this work.
2. Hybrid Wireless Networks
Based on hop distance, we can classify hybrid wireless
networks as single-mode, where MTs only perform single
hop communication, or multi-mode, where either multi-
hop or single-hop MT communications occur. Recently,
the IST WINNER project proposed a novel hybrid relay
network [5] to setup new 4G standards in Europe. This
work mainly focuses on specific radio interface
technologies, which are needed for a ubiquitous radio
system. The RNs share the same RAT with BSs and MTs
to realise dynamic spectrum usage.
Multi-hop Cellular Network (MCN), Multi-Power
Architecture for Cellular network (MuPAC), integrated
Cellular and Ad hoc relaying system (iCAR), Self-
organising Packet Radio Networks with Overlay
(SOPRANO) [6] and Hybrid Wireless Network (HWN)
[7] are the architecture designs that have been proposed
Table 1. Recent Hybrid Networks Classification and Summary
Project
HWN* WINNER SOPRANO
Basic
Infrastructure
Incorporate a MANET to increase
system capacity while realising
differentiated QoS services
Novel interface technologies for
ubiquitous networks
Test CDMA and connect a
cellular network with an IP and
MANET network
Main objectives BSON, BSON with RN,
MANET, MANET with RN
Unified radio access technology,
RN and BS may change
transmission range
RNs transfer the traffic from a
hot spot to the neighbouring
cooler
Fixed node
Antenna
Not investigated Smart antenna / directional
antenna
Not investigated
Traffic
handover
Heterogeneous network handover
with QoS support
Ubiquitous network handover Not investigated
Call admission Coordinated admission controlled
by BS
Coordinated admission controlled
by both BS and RN
Central admission controlled by
BS
Routing issues BS switch and RN assisted traffic
diversion
BS and RN switch BS switch
Load Balance Multihop based load balancing
considering QoS
Not investigated Multihop load balancing

38 C. SHEN ET AL.
Copyright © 2008 SciRes. I. J. Communications, Network and System Sciences. 2008; 1:1-103
for hybrid networks. The iCAR is derived from existing
cellular networks and enable the network to achieve
theoretical capacity through adaptive traffic load
balancing. Excessive bandwidth in surrounding cells can
be borrowed for the congested cell through RNs with
primary, secondary and cascaded orders. The SOPRANO
is a scalable architecture that assumes the use of
asynchronous Code Division Multiple Access (CDMA)
with spreading codes to support high data rate internet &
multimedia traffic. It is similar to iCAR other than IP
network support and cross network connection methods.
The HWN, without RN support, requires Global
Positioning System (GPS) capability to extract
geographical location. Each cell operates either in cellular
structures or ad hoc structures depending on the MTs
topology information from the GPS. Table 1 presents the
main features for the HWN*, WINNER and SOPRANO
architectures. A comparison of the iCar, MuPAC, HWN
and MCN can be found in [8].
Table 2 identifies the technologies used and the
features considered for each of the four HWN*
communication structures. This table specifies the
physical layer mode; media access method, spectrum
usage, mobility characteristics and data rate. Time Duplex
Division (TDD) is implemented on all four modes: BSON,
MANET, RN supported BSON and RN supported
MANET. Typical physical layer parameters are used [9].
The log-normal standard deviation
σ
is set as 10 dB,
shadowing correlation distance s
χ
is set to 50 m, and the
mean SIR value d r is set to 17 dB. The default energy
mode provided by OMNET++ [10] is implemented,
specifically, for a 250m transmission range the transmit
power used is 0.282W. The transmit power used for a
transmission range of d is proportional to4
d. For the
MANET and RN supported MANET, we implemented
the CSMA/CA Distributed Coordination Function (DCF)
of the IEEE 802.11 standard. The air interface can be
adapted for TDD/CDMA of 3GPP as described in [11],
where multiple data rates are achieved by using various
spreading codes.
In Time Division Multiplex Access (TDMA) cellular
systems, when an MT is involved in a call admission or
handover process in a congested cell, but is not able to
find an admission channel or alternative channel,
respectively, the data transmission will be terminated. For
example, consider a scenario in Figure 2 where MT A is
Figure 2. Multi-Hop Combined Transmission Example of
cellular resource relaying using fixed RNs
currently connected to MT B and is moving out of Cell 1
into Cell 6. A request for a BS handover will be sent as
soon as the power level by MT A goes below a certain
threshold, its trajectory is indicated by the red line. A
successful handover will take place within a few hundred
milliseconds depending on speed before the received
power from BSs reaches an unacceptable level. When MT
A arrives in Cell 6, if the congestion persists for a period
of time during which the MT moves farther away from
the other neighbouring cell border, thus causing the
Table 2 HWN* Four Communication Structures
BS cellular network Ad hoc network RN supported cellular
network
RN supported
MANET
Physical layer Time division duplex Time division duplexTime division duplex Time division duplex
Media access layer TDMA CSMA/CA TDMA CSMA/CA
Spectrum regulation Licensed Unlicensed Proposed to use same
spectrum as cellular
network
Proposed to use same
spectrum as cellular
network
Mobility speed Low, Medium and
High
Low Low, Medium and High Low and Medium
Transmission data
rate
Low, Medium and
High
Low Low, Medium and High Low and Medium

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Copyright © 2008 SciRes. I. J. Communications, Network and System Sciences. 2008; 1:1-103
received power level from BS A to fall below the
acceptable level, handover will fail and the call will be
permanently terminated.
However in MHCT mode of HWN*, the data session
does not have to be dropped even though the congestion
in Cell 6 persists. For example, when MT A moves into
the congested Cell 6, apart from trying cellular
connections, it also associates itself with a RN using ad
hoc frequencies, then the RN may continue the data
session with any BS in the network via the multi-hop
relaying structure and the relaying path can be also
extended to the area with no cellular coverage.
Since the ultimate goal of this framework is to
improve the resource sharing performance in terms of
data admission, traffic handover and routing optimization,
in addition, OHAHT of point-to-point ad hoc
communications is added to the routing strategy to further
balance traffic load while sharing media resources. The
analytical as well as simulation results we experienced
have proven that inter-network traffic management can
significantly improve the grade of service, reduce the
traffic blocking probability, while maintaining the
individual MT QoS.
The spectrum for each RN could be also allocated
dynamically. Multiple non-interfering relay frequencies
operate in parallel through the use of intelligent radios.
The spectrum where a RN operates can be leased for a
limited time depending on the network status. The
spectrum on which it is operating is reclaimed when
network performance improves. For example, two RNs
operating on non-interfering spectrums form a network
relay link with multiple orthogonal bands. Multiple nodes
within range of each other may also transmit
simultaneously on different channels without relying on a
media access protocol or distributed scheduling algorithm
to resolve contention.
Although the large scale deployment of dual-interfaced
RNs could be costly, the use of RNs extends the BSON
service range, optimizes cell capacity, minimizes transmit
power, covers shadowed areas, supports inter network
load balancing and supports MANET routing.
Theoretically, both the HWN* system capacity and the
transport capacity per MT, when compared to a cellular
network, should be improved because the RNs provide
relay capability as the substitution of a poor quality
single-hop wireless link with a better-quality link being
encouraged whenever possible. Also a higher end-to-end
data rate could be obtained if a MT had two
simultaneously communicating interfaces.
Using three scaling approaches proposed in [12], we
can implement simulation dimensioning and estimate how
many RNs should be deployed when the number of MTs
changes. The three parameters are the number of RNs m,
the number of MTs n and the system capacity C. The
asymptotic scaling for the per user throughput as n
becomes large is:
nnm log/≤ (1)
The per user throughput is of the ordernnC log/
and can be realised by allowing only ad hoc
communications which does not necessarily need RN
support, when:
nnmnn log/log/ ≤≤ (2)
The order for the per user throughput is nCm /
therefore the total additional bandwidth provided by m
RNs is effectively shared among n MTs. Finally, when:
mnn ≤log/ (3)
the order of the per user throughput is only nC log/
which implies that further investments in relay nodes will
not lead to an improvement in throughput and bandwidth
optimization.
3. HWN* Formation Algorithm
In this section we present the basis for our HWN*
fixed RN placement plan and related formation algorithm.
The positioning and formation scheme are addressed
where RNs operate in the same ad hoc frequency band.
Various plans have already been proposed to select
sites for fixed RNs, the solutions depend on the varied
network performance objectives. The network spectral
efficiency was taken by [13] as the objective to optimize
RN positioning. For HWN* we adopt a bandwidth
oriented criterion to investigate the proper RN positioning,
the RN positioning is formulated as a constrained
optimization problem, of which the goal is to maximize
the overall network throughput and per node throughput
so that 95% of MTs are better served with guaranteed
quality than by the conventional cellular network without
relaying. By imposing such a constraint we can improve
the performance for MTs at disadvantaged locations and
enlarge network dimensioning and help deliver a more
uniform communication experience. In searching for the
proper RN locations, [13] made the assumption that the
quality on the links connecting BS and RN is always
better than the link between RN to from RN. Therefore
the authors stated that this assumption can be satisfied by
establishing Line of Sight (LOS) links between the BS
and RN or by designing links that enhance the antenna
gains. But the former solution imposes extra difficulty on
network planning, while the latter complicates the
transceiver design. Therefore, it is of importance to
investigate the RN positioning with limited change on
existing radio technologies to save the infrastructure cost.
When considering pre-engineered RN deployment, it

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is well known from planar geometry that to cover a two
dimensional district with equal sized circles, the best
possible packing solution can be obtained by surrounding
each circle by six circles as shown in Figure 3. But to
have connections between the RNs, we need an overlap
between relay cells. We therefore consider a situation
where the location of the RNs is pre-computed and the
best deployment strategy is chosen. The deployments
shown in Figure 3 are two examples of such pre-
engineered approach with a specific number of RNs in
HWN*. The first deployment tries to cover the entire area,
without considering MT mobility and service requirement.
The second one tries to cover densely populated regions
of MTs near the edge of the cell. Our MT mobility model
is based on a city layout that consists of n probability
based attractor points that MTs will move towards.
Figure 3. Pre-engineered placement of relay nodes
We propose to form the relay network of HWN* in
two stages, which are Association and Route
identification. At the former stage, each MT begins a
searching process to associate itself with one or more
RNs based on Signal-to-Interference Ratio (SIR). If the
BS has spectrum available for one or more RNs, this
information is broadcast over the cellular control channels
so all nodes within the cell receive it simultaneously. The
transmission radius of a node on the relay network is
small compared to the cellular coverage. Thus, a RN only
connected to a group of MTs and group members of a RN
usually overlap with other RN's group members. To select
a serving RN, reduce computation complexity and isolate
groups, every MT with multiple membership broadcasts a
neighbour advertisement (NADV) message with a Time-
To-Live (TTL) value. The NADV message contains the
identification of the source node and its received signal
quality from different RNs. When a node receives NADV
message back from multiple RNs, it compares various
signal qualities. The RN with the best SIR is chosen as
the serving RN and connections with other RNs are
terminated.
In stage two, if required, MTs join the relay network
by forming a path through serving RN using MHCT
transmission mode. We consider several methods, which
will be described in the next section to overcome high
contention or overload at the serving RN, which may
occur if several MTs attempt to join the relay network
simultaneously. The MHCT uses a modified version of
label routing as the main strategy to find the path from the
MT via RNs to BS. When an intermediate RN forwards a
route request (RREQ) message, it appends its
identification and its distance from the BS to the message.
Therefore, the serving RN can learn of the nodes involved
in the relay path.
4. Adaptive Distributed Cross-layer Routing
The heterogeneous network media resources are
shared by multiple traffic classes. Service classes that
deliver QoS to applications have priority over others that
do not. In such a network, routing decisions for high
priority QoS sessions can affect what resources are
available for lower priority traffic. Poor route selection
can result in congestion for, or even starvation of, lower
priority traffic. Many studies [14] have focused on
routing algorithms that optimise the network throughput
for individual service classes, minimise call blocking rate
and improve per-session throughput. Little effort has been
devoted to routing algorithms that address interclass
resource sharing. But the fact is routing in a multi-class
network is not as simple as selecting an “optimal” path
for each flow, assuming that the traffic class of this flow
is the only service class in the network. Such an
“optimal” path can adversely affect the congestion
condition of flows in other traffic classes. For example, in
a network that supports both QoS traffic requiring
bandwidth guarantees and best-effort traffic, which
resources are available to best-effort traffic depends on
how QoS flows are routed. QoS flows can consume all
the bandwidth on certain links, thus creating congestion
for, or even starvation of best effort sessions. Statically
partitioning the link resources can result in low network
throughput if the traffic mix changes over time. Thus, a
mechanism that dynamically distributes link resources
across traffic classes based on the current load conditions
in each traffic class is critical for performance.
By proposing a cascaded Adaptive Distributed Cross-
layer Routing (ADCR) for HWN*, we discourage
applications from using any route that is heavily loaded
with low priority traffic. Traditional routing strategies
that use global state information are not considered.
Problems associated with maintaining global state
information and the staleness of such information are
avoided by having individual MTs infer the network
states based on route discovery statistics collected locally,
and perform traffic routing using this localised view of
the network QoS state. Each application, categorised by
service class with the choice of three possible
transmission modes, maintains a set of candidate paths to
each possible destination and routes flows along these
paths. The selection of the candidate paths is a key issue
in localised routing and has a considerable impact on how
the ADCR performs. The high priority traffic is given
high priority in accessing comparatively expensive
cellular resource, while low priority traffic tries to access

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low cost ad hoc resource. Per MT bandwidth is used as
the only metric for route local statistics collection since it
is one of the most important metrics in QoS routing,
furthermore, important metrics such as end-to-end delay,
jitter can be expressed as a function of the bandwidth.
We first divide all traffic sessions into three simple
service classes to fairly share spectrum between end user
and provider equipment, further reduce the cost
implementation, monitoring and management. The three
service classes are:
z High profile users (HPUs)
z Normal profile users (NPUs)
z Low profile users (LPUs)
The HPUs have the highest access priority among the
three communication modes in HWN* and traffic
admission of NPUS and LPUs are considered based on
current HPUs sessions. The NPUs are configured to have
a higher probability than LPUs in terms of resource
acquisition and this probability is decided by an
Association Level (AL) set. In the case of network
congestion, CT mode may temporarily become
unavailable to NPUs when HPUs are not fully
accommodated, while LPUs sessions may be only granted
MHCT and OHAHT mode access to mitigate network
congestion, reduce transmission delay and improve per
MT throughput.
A MT locally links each application with a service
class based on particular QoS requirement. The choices
are flexible as one subscriber may link business voice
calls with the HPUs class and Voice over IP (VOIP) calls
to the LPUs class. Another subscriber may link all voice
and data services to HPUs class when moving at high
speed and then change them to the LPUs class when
walking on the street. For the purpose of simulation
evaluation of the HWN* applications are temporarily
fixed to specific classes. For example, real time
collaboration, wireless gaming, and geographic real time
datacast applications are associated with the HPUs class.
Interactive multimedia, media telephony and rich data
applications are linked to the NPUs class. Lightweight
browsing, LAN access and file exchange applications are
classed as LPUs. As HPUs are liable to require more
channels than NPUs and LPUs, applications such as large
volumes of file exchanges are not suitable for the ad hoc
structures.
Secondly, as radio resource management on fixed RNs
directly affects the performance of the media access layer
and network layer we adopt a policy based management
approach and two policies are proposed to realise
effective packet transmission. We define:
The RN has the right to reserve QoS guaranteed free
channels for packet transmission and it maintains a
status table that's refers to be other RNs and it provides
information on change busy conditions or relay failure.
The purpose of bandwidth reservation is to let RNs
that receive the relaying discovery command check if
they can provide the bandwidth required for the
connection. Meanwhile, to decrease queuing delay, high
profile traffic should be given higher priority than low
profile traffic. Thus, the routing packets and content
packets for high profile applications have less waiting
time in queues. The other way to raise priority is that the
packets related to a high profile have shorter back-off
time to increase the probability of early medium access.
To more accurately guarantee queuing delay and to avoid
having high profile traffic being influenced by other data
traffic, we place a limitation on queues. For example, if
the transmission time of LPUs exceeds the starting time
of HPUs, the transmission of LPUs is suspended. As for
the status table maintenance, information flooding is
restricted to a limited scope. Once a positive
acknowledgment message is confirmed by a requesting
RN, the relay paths will not be changed unless resource
contention happens. Given the fact that maintaining
global RNs channel status in each RN slows down RN
response time, we only require each RN update
neighbouring RNs’ information, periodically.
The transmission model defines the methods that
nodes employ for communicating with one another. For
ADCR based equipment, several commands are defined
as:
1) ACK/ACCEPT/REJECT/REJHO for the message
delivery acknowledgment, packet acceptance,
packet rejection and after rejection handover
request.
2) SEARCH/SETUP/DATA/BREAK for destination
node finding, new connection establishment, packet
delivery and connection teardown.
3) MOS for MT to choose adaptive transmission mode.
4) FAIL used to acknowledge any failure on RN or
MT.
5) LREQ to request a label during the routing. The
label is a short, fixed-length identifier. Multiple
labels can identify a path or connection from the
source MT to the destination MT. The structure of a
label message contains flag, label, cost and TTL.
6) LREP to request a label replay during the label
routing in MHCT model.
Time-sensitive multimedia applications have
restrictions on end-to-end transmission delay, while FTP
data transfers need a minimum guarantee on packet losses.
Further actions such as channel transfer and re-routing are
required before service termination. The routing
algorithms in HWN* should allow subscribers seamlessly
move without dropping their communications and
considers differentiated QoS issues, for example, the QoS
guarantee for HPUs require that they agree to pay more

42 C. SHEN ET AL.
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than NPUs and LPUs.
The cascaded ADCR scheme includes three sub packet
transmission models, which are the OHAHT for point-to-
point ad hoc communications, the MHCT for cellular
resource relaying using fixed RNs and the CT for
traditional cellular service, as illustrated in Figure 4 We
further define the HPU has having the highest priority to
access any packet transmission models, in the order of CT,
MHCT then OHAHT. However, due to the high priority
of premium traffic, the global network behaviour as a
consequence of this service class, including routing and
scheduling of premium packets, may impose significant
influences on traffic of other classes as we explained in a
previous section. These negative influences, which could
degrade the performance of low-priority classes with
respect to some important metrics such as the packet loss
probability and the packet delay, are often called the
inter-class effects. To reduce the inter-class effects, the
premium-class routing algorithm must be carefully
selected, must work correctly under the hop-by-hop
routing paradigm, and minimize the congestion resulting
from the traffic of premium classes over the network. We
also proposed mechanisms for load balancing of high-
priority traffic on DiffServ networks. The Association
Level (AL) calculation was proposed to differentiate
between user classes. The AL is a set of parameters
monitoring channel availabilities, an AL that scores
higher than the threshold means that the channels are
already occupied by ongoing sessions. Our extensive
simulations clearly demonstrated that the proposed
methods distribute the premium bandwidth requirements
more efficiently over the whole network. We also present
corresponding computerised process of MT’s association
with a serving BS and RN, and simplified ADCR
algorithm with three transmission models in Figure 4.
4.1. One-Hop Ad-Hoc Transmission
In this model, the requesting MT first broadcasts
SEARCH messages to every node in its transmission
range including its associated RN and BS. For example,
MT A in Figure 4 broadcasts SEARCH messages, if the
destination MT B is within its transmission range and
there is no ad hoc based media contention between MT A
and MT B, MT B can respond to MT A with an ACK
message. Once MT A confirms the acknowledgement, it
starts a connection SETUP session immediately. MT A
continues transmitting directly until the SIR falls to a
certain level, where traffic re-routing or handover process
will be initiated.
4.2. Multi-Hop Combined Transmission
The multi-hop combined transmission model involves
RNs acting as intermediate nodes for message relaying.
Figure 4 shows the connection setup process for
communication between MT A and MT B via the RN
infrastructure. MT A first broadcasts SEARCH messages
to every node to find MT B. After the SEARCH session,
MT A may find that the cellular resources can be
Figure 4. ADCR transmission

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borrowed through RNs by receiving three ACK messages
from the serving BS of MT B, RNs and the MT B. The
positive acknowledgement requires MT B to send an
ACK to its serving BS, then the serving BS sends an ACK
to the RN infrastructure and finally the RNs feedback the
ACK to MT A. Once the positive ACK is confirmed, the
MT A starts a connection SETUP from MT A to RN,
then RN to BS, and finally BS to MT B. The DATA
transmission process follows the same packet delivery
route, and further route discovery is prohibited to reduce
the signaling overhead. MT A continues transmitting
directly until the SIR falls to certain level, where traffic
re-routing or a handover process will be to setup,
configure, and maintain a path between two MTs. Media
resources are largely saved when compared to other
traditional routing algorithms, where paths are initiated.
Different from MANET nodes, which have limited
energy and processor resources, protocols and algorithms
running on the RNs can have a high level of efficiency
with reasonable complexity. We introduce the label
routing concept [15] in MHCT. The connectionless label
routing is a distributed routing protocol that is able
maintained regularly. The path from source node works
as a tunnel identified by multiple labels, which requires
information from both the media access layer and
NETWORK layer. With label routing, intermediate RNs
can provide fast and efficient forwarding without
checking the Internet Protocol (IP) address and accessing
a large routing table in the memory of the host RN.
In order to find a path to the destination MT, the
source MT creates a LREQ message in which the packet
contains IDs, sequence number and service class of the
source MT. This packet also contains a broadcast ID and
a hop count that is initialized to zero. All RNs that receive
this message will increment the hop count. If a RN does
not have any information about the destination node, it
will record the neighbour’s ID where the first copy of
LREQ is from and send this LREQ to its neighbours.
LREQs from the same node with the same broadcast ID
will not be processed more than once. Figure 5 gives an
example of label routing in MHCT. In this example, there
are eight nodes with duplex connection link.
The MT A first creates a LREQ message and sends it
out to its associated RN. Figure 5 illustrates the
propagation of LREQ across the RNs and the reverse
path at every RN. The reverse path entry is created for the
transmission of the reserved label for this path. This label
is embedded in the label reply message LREP. The
reserve path entry will be maintained long enough for the
LREQ to traverse the path and for RNs to send a LREP
to the source MT. Once a path is found in the relay
structure, the source MT will check the sequence number
(SEQ) of the destination MT in the current path in order
to avoid old path information. It should be at least as
great as the value entry in the LREQ. Otherwise the
existing path in the table will be discarded. If
LREQ
SEQSEQ ≥, it will also check in the current path
whether the QoS requested by the source MT has been
satisfied. If not, this request will be discarded. If the
Figure 5. Label Routing illustration

44 C. SHEN ET AL.
Copyright © 2008 SciRes. I. J. Communications, Network and System Sciences. 2008; 1:1-103
source MT still can not find the destination MT B, MT A
will increment the hop count in the LREQ by one and
then broadcast it to its neighbors. Any duplicated LREQ
with same source node ID and same broadcast ID will be
discarded. Normally relay based label routing should
have a maximum hop count. However, there is no energy
constraints and node motilities issues in our relay
infrastructure, thus theoretically any hop count threshold
can be possible. We specify the hop court in LREQ as not
being larger than 10 as a simulation limitation to avoid
computation complexity and if the sender of a LREQ
does not receive the reply message, each node only re-
sends the LREQ once for each connection request.
The RN only creates a LREP with the total hop count
of this path if hop count, sequence number, and path QoS
are all acceptable, the new sequence number of the
destination MT is the largest one between SEQ
and LREQ
SEQ , the best QoS, and a label from its label
pool. Then this LREP will be sent back to the source MT
along the reverse path entry. The third plot in Figure 5
shows the propagation of the LREP along the reserve
paths. Note that both RN C and RN F fail to send the
LREP due to hop count, sequence number or QoS issues.
The path between the source MT and the destination MT
is composed of multiple segments and all data packets are
relayed by these segments. Each segment is a real
connection between two nodes and labeled by the
sending-side node of the LREP in this segment. For
example, in the path MT A to RN A to RN D to RN E to
RN B to MT B showed in the last plot of Figure 5, RNs
A, D, E and B set up the labels of the segments between
A and D, D and E, and E and E respectively. MT A and
RN A, MT B, RN B and its associated BS are the other
two segments. Since the topology of the relay structure is
meshed, the source MT can receive more than one LREP.
There is a hop count field in the LREP. This field records
the total number of hops of the path. The source MT will
choose the smallest hop count from the LREPs in the
specific limited time. All LREPs that are received after
this time threshold will be ignored. And if some available
LREPs have the same hop count, the path that has the
largest destination sequence number, which means it is
the latest path, will be chosen.
4.3. Cellular Transmission
The cellular transmission model applies when both the
transceiver MT and the receiver MT are within the
transmission range of a BS or BSs. The last plot in Figure
4 shows the connection setup process for communication
between MT A and MT B via cellular BSs. MT A first
broadcasts SEARCH messages to every node to find MT
B. After the SEARCH session, the MT A finds that it is
able to communicate with MT B directly via BSs, while
the connection can be setup through a virtual wireless
backbone. The positive acknowledgement of a connection
requires MT B to send an ACK to its serving BS, then the
serving BS informs the serving BS of MT A or the BS
feedbacks the ACK to MT B when both MT A and MT B
share the same serving BS. Once the positive ACK is
confirmed, MT A starts connection SETUP from MT A
to BS, then BS to BS, and finally BS to MT B. The
DATA transmission process follows the same packet
switched delivery route. MT A continues transmitting
directly until the SIR falls to certain level, where traffic
re-routing or an inter network handover process will be
initiated.
Dynamic channel allocation in each base station can be
managed in a distributed manner given that the channel
usage does not break the two cellular network channel
interference constrains [16] which are cosite constraint
where there are minimum channel separations within a
cell and non-cosite constraint where there is a minimum
channel separation between two adjacent base stations.
5. Mobile Terminal Mobility Model
The HWN* system should be tested under realistic
mobility conditions. To characterise the mobility pattern,
we implement a HWN* Attractor Point Mobility
Model (HPMM) based on the random waypoint mobility
model and attractor point mobility model [17].
At the simulation start, a MT schedules an ACK
message to itself before it determines a new two
dimensional <x, y> position. After messages saving, the
MT sends a MOVE message to the physical layer and
reschedules the ACK to be delivered in 2
move seconds.
The layout of a metropolitan environment consists of n
probability based attractor points which MTs will move
towards. We provide an approach which influences user
mobility in a distributed manner based on attractor points,
instead of grouping MTs with macro mobility, each MT
selects a destination using micro mobility. We consider
two Behaviours options when a MT approaches the
simulation environment map borders. These are
Rebounding Behaviour that makes the nodes reverse
direction according to the elastic impact theory and
Toroidal behaviour, which makes the nodes leave the
map. Speed Control is introduced on each MT and the
speed continuously increases or decreases. For each step,
a new MT speed sample )( 1+k tv is calculated:
))(()()( 11
*
1kkkkk tttatvtv −+= +++ (4)
where )(k tv is the current speed, the Acc. speed * a is a
non-linear variable associated with the distance between a
MT and its destination point and )( 1kk ttt −=∆+ is the
sampling period. Two limits are introduced, which are
upper
vand lower v and Equation 4 applies to a MT only if
upperklower vtvv
≤
≤
+)( 1, otherwise at next sampling

ANY RESOURCE SHARING VIA HWN* ROUTING 45
Copyright © 2008 SciRes. I. J. Communications, Network and System Sciences. 2008; 1:1-103
point, the speed will remain unchanged. The current
speed )( 1+k tv is correlated with the previous speed
value )( k tv . A small sampling time leads to a smoother
speed change.
6. Simulation and Results
The HWN* system and routing algorithm are
evaluated by extensive discrete event computer
simulations. Both the Transmission Control Protocol
(TCP) and User Datagram Protocol (UDP) are used. As
more than 90 percent of web services consist of TCP
flows it is reasonable to presume that traffic due to a MT
web application will not deviate from this behavior if we
only use TCP for web services in simulations. The H.263
codec is imported into the OMNET++ simulator with
video file downloaded from the internet. We generalize
and refer to all video streaming as real-time services,
while all web transports are referred to as non real-time
services. Table 3 shows the default QoS profile used
consisting of 30% 64 kbps streaming video, 45% general
voice calls and 25% non real-time web services according
to the 3GPP [11] specification. The service request
portion is distributed and shared among these three user
classes.
We randomly distribute the MTs in 13 regular
hexagonal cells (1km length, 2.62
km ) in an 8 km X 8
km grid. The BS is located in the centre of each cell, and
each cell owns a RN located at a random position. From
300 to 1300 MTs are scattered in the grid to simulate
varied scenarios. To ensure that the same cellular
frequencies are repeatedly used in the cellular network, 7
frequencies are allocated to each cell and 128 channels
are available on each BS. The MT travels from 0 to 80
km/hour since a relative speed higher than 160 KM/hour
is not suitable for the 802.11 radio propagation model,
which has limited compensation for channel fading.
Typical simulation parameters are used [9] as illustrated
in Section 2.
6.1. HWN* Capacity Analysis
Our first objective is to show the pre-engineered RN
positioning strategy influence on the HWN* capacity
under various network conditions. Operations of the
proposed ADCR are simulated, including the process of
RN & BS registration, routing path discovery,
transmission mode selection and data delivery. 100, 200,
300, 400 and 500 MTs are placed gradually in the system
to study the impact of QoS support. The implementation
of service classification will guarantee the delay of real-
time packets.
The per cell capacity in the planned HWN* should be
greater than that in random HWN* and traditional cellular
network, especially when the number of MTs is larger
and the cellular service percentage is lower. This is
because MTs not serviced in a cell or outside of any cell
coverage can use the carefully planned relaying path to
access nodes with cellular coverage and communicate
with other nodes far away. Therefore, when the number
of nodes is large enough, the HWN* can achieve
complete connectivity regardless of the cellular service
percentage. But the capacity and topology connection of a
network are very much influenced by infrastructure
support and traffic volume. On the other hand, one hop ad
hoc transmission is a part of three HWN* transmission
modes. Therefore, the capacity in any HWN* and cellular
system should be much larger than that in any pure ad hoc
network, so we do not compare ADCR based novel
infrastructure with MANET capacity.
All the simulations run up to 1000 simulated seconds.
The first 100 seconds is a simulation warm-up time thus
traffic intensity remains unchanged. Figure 6 records per
cell capacity performance of three scenarios when the
cell’s traffic load is increasing. The capacities of both the
planned HWN* and the random HWN* go up till
maximum throughputs reach around 5.6 Mbps and 4.7
Mbps respectively. As we can see from the trend of the
capacity lines, when the traffic load becomes higher the
pre-engineered HWN* outperforms the random HWN* in
terms of network fairness and its maximum capacity gets
near to the theoretical gain with a more uniform
communications experience.
For packet delivery ratio in the HWN*, the System
Throughput (ST) is defined as the delivery ratio:
%100
____
____
sentdataofnumberTotal
receiveddataofnumberTotal
ST = (5)
In this scenario, we only implement UDP traffic
(without handshaking mechanism) on each MT instead of
the default QoS traffic profile, and operations of the
proposed ADCR are also simulated. The packets are sent
Table 3. Characteristics of QoS differentiated users
Low profile user Normal profile user High profile user
Portion of arrival
request
20% Voice, 10% Web
and 5% Video
15% Voice, 8% Web
and 10% Video
10% Voice, 7% Web
and 15% Video
Voice Dwell / Session time: Web Dwell / Session time: Video Dwell / Session time:
60s / 120s 120s / trace 120s/240s

46 C. SHEN ET AL.
Copyright © 2008 SciRes. I. J. Communications, Network and System Sciences. 2008; 1:1-103
at constant bit rate (CBR) with a packet size of 1500
bytes but the MTs are added from 0 to 500 gradually as
an input parameter to increase the offered load on the
system. Figure 7 shows the impact of increased traffic on
the packet delivery ratio. Results show that whichever
traffic load is used, the proposed ADCR with pre-
engineered RNs placement gives a higher throughput than
the HWN* with random RN placement and cellular
system. We can also see that the packet delivery ratio
decreases when the UDP traffic load increases, this is
mainly due to the congestion. However, the pre-
engineered HWN* outperforms random HWN* or
TDMA network by 12% and 26% respectively, when the
maximum traffic load is achieved.
6.2. Packet Transmission Delay Analysis
The average packet transmission end-to-end delay of a
traffic flow should be directly proportional to the number
of hops traversed by the flow, and inversely proportional
Figure 6. Average capacity per cell per second
to the flow’s end-to-end throughput, this is an interesting
metric to study since the HWN* system itself has
complicated transmission models, which can be seen as
hybrid traffic migration of both ad hoc and cellular
network with RNs. We first define Average End-to-end
Delay (AED) as:
timedeliveryTotal
receiveddataofnumberTotal
AED __
____
= (6)
The cellular network is only packet-switched and the
multi-hop ad hoc network routes traffic in unstable
topology using various routing algorithms, thus the
packet transmission delay should not be compared
between HWN* and any single layer network. We
therefore simulate simplified WINNER and SOPRANO
hybrid network infrastructures with traffic routing
functionality, respectively. For WINNER, through
cooperative relaying algorithm, the RN operates full
resource management functions like a cellular BS does.
However, for distributed SOPRANO, the routing
calculation is the sole responsibility of the local MT, thus,
we implemented minimum energy routing protocol as
recommended in [18]. Figure 8 shows the average end to
end delay versus quantized load offered for three hybrid
networks. We observe that there is a significant
improvement in the delay performance of HWN* ADCR,
compared to the cooperative relaying in WINNER and
the minimum energy routing in the SOPRANO. In Figure
8, at 15 erlangs, the corresponding average end-to-end
days are 0.10, 0.21, and 0.033. The delay in other systems
is two or three times larger than that of ADCR,
respectively. This shows that the proposed ADCR
adaptively selects paths with better quality, and it can
prevent to wasting transmission time.
6.3. QoS Based Routing Analysis
Experiments are also conducted to verify that the
proposed ADCR algorithm meet the goal of providing
Figure 7. System throughput versus offered load
QoS differentiation among different users based on their
class profile. To setup a comparison benchmark, we
simulated a simple HWN* without any dedicated
resource management and routing algorithms. In this
network, each session has the same privileges when
accessing the media resource. The arriving packets are
accommodated on a first-come-first-serve basis until all
available channels have been used. A MT terminates the
routing process when it can not find an alternative route.
Figure 9 shows the route acquire success ratio with
increasing of the traffic load. The ADCR has the best
success ratio since it always returns a path on-demand
and performance improvement is marginal when the
system is heavily loaded. The simple routing algorithm in
the HWN* performs worse than the ADCR due to its
limitation on route selection and lack of alternate paths.
Table 4 shows the individual class successful path
acquiring probabilities against traffic loads offered for
differenct user classes. It can be seen that different results
are experienced by user applications in different service
classes and for unclassified users in the simple HWN*.
Under low and medium traffic intensities, the success
rates are similar among HPUs, NPUs and LPUs, since

ANY RESOURCE SHARING VIA HWN* ROUTING 47
Copyright © 2008 SciRes. I. J. Communications, Network and System Sciences. 2008; 1:1-103
sufficient routes are available and LPUs are not largely
affected by HPUs and NPUs communications. However,
in the high traffic intensity case, HPUs and NPUs
application encounter large resource competition in the
MAC layer, which consumes a considerable fraction of
the radio resource. This may adversely affect the route
finding performance of LPUs, in particular when a HPU
and NPU traffic hot spot occurs; LPUs are pushed to use
the ad hoc communications modes, where the routing
process are comparatively unstable compared against the
infrastructure based modes.
7. Conclusion
Routing is an essential part for realizing HWN*
environment. In this article we first present a review of
current hybrid networks addressing the issue of system
Figure 8. Average end to end delay versus offered load
infrastructure, radio technology, media access methods,
routing strategies and QoS based algorithm constraints,
merits and deficiencies. We have devised a self-
organising routing scheme, ADCR. In order to manage
traffic in a combined ad hoc, cellular and relay system,
ADCR employs three sub-transmission modes,
respectively, to effectively reduce its resultant
communication overhead to acquire cost-effective delay-
constrained paths. ADCR also employs a service class
based approach to discourage applications from using any
route that is heavily loaded with low priority traffic.
Simulation results demonstrate that the performance of
ADCR meets our design goals. Moreover, this cascaded
routing algorithm can also be used to support cost-
effective routing with differentiated user classes, referred
to as High profile users, Normal profile users and Low
profiles users, which are based on different service
requirements.
The routing in HWN* is challenging. Although some
work has been carried out to address this critical issue for
other types of hybrid networks, research in this area is far
from adequate. The scalability issue is always critical in
designing large networks. 1). The performance of an
algorithm for selecting routes primarily depends on
hybrid network layers structures, domain (cell) definition
Figure 9. Comparisons of route acquire ratio vs. traffic
as well as proper information gathering method, since
there exists a tradeoff between efficient data
dissemination and communication overhead. 2).The route
selection method should not only consider NETWORK
layer constrains, but also constrains posed by the Media
access layer and Physical layer, to meet individual
application QoS requirements. For example, in a cross-
layer co-design approach, if MAC layer issues are not
Table 4. Success route acquiring ratio comparison between different user classes
HPUs NPUs LPUs Simple HWN*
5 Erlangs/cell 100.0% 100.0% 98.0% 98.5%
10 Erlangs/cell 98.1% 93.2% 91.9% 86.2%
15 Erlangs/cell 97.3% 87.0% 86.7% 77.7%
20 Erlangs/cell 96.1% 84.4% 82.5% 57.5%
25 Erlangs/cell 95.0% 77.1% 72.1% 45.1%

48 C. SHEN ET AL.
Copyright © 2008 SciRes. I. J. Communications, Network and System Sciences. 2008; 1:1-103
considered, then the route selection might ignore
bandwidth requirements over time slot or frequency slot.
This would pose a big problem for radio resource
management. 3). Future work on relay positioning will
involve using evolutional strategies or genetic algorithms
to dynamically allocate the RNs.
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