# Modeling and Performance Analyses of Hybrid Cellular and Broadcasting Networks

Authors: Peter Unger; Thomas Kürner
Journal: International Journal of Digital Multimedia Broadcasting
 (2009)

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## Abstract

Mobile communication services are getting more and more important and, in particular, multimedia services have
attracted the interest of the users. Mobile TV is one of the most demanded candidates. Powerful and efficient communication
systems are needed, which provide high capacities, especially at the downlink. Furthermore, interactivity is essential for
supporting the user needs and to extend the service offering. As one possible solution to meet the mentioned requirements, we
consider the combination of the cellular network UMTS and the mobile broadcast network DVB-H, which form a hybrid network.
We investigate the performance of hybrid networks and develop a system model, which describes the hybrid network and the load
switching between both networks. One of the contributions is the definition of the switching bound concept, which represents an
efficient tool to assess the necessity and the feasibility of hybrid networks and the amount of load switching. The performance
indicators cell load and grade of service are analyzed by using theoretical and realistic scenarios.


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## Body

## 1. Introduction

With the introduction of the 3rd Generation cellular network Universal Mobile Telecommunications System (UMTS), higher data rates have been made possible and the variety of services has been increased, compared to the previous cellular networks such as General Packet Radio Service (GPRS). In addition to the traditional services, such as voice telephony and messaging, the multimedia services and high data rates have been made possible and accepted by the users. The new offered services, for example, video telephony, Internet-based data communications, and streaming services, make high downlink capacities and efficient networks necessary.The currently available terminals are equipped with several receivers, which allow for the reception of data from different networks, such as UMTS and DVB-H. Hybrid networks are possible for these types of terminals and the broadcast downlink channel provided by DVB-H can be used very efficiently to serve many users at the same time with high data rate services.The DVB-H technology is based on the terrestrial digital TV system DVB-T, which has been successfully introduced in many countries [1, 2]. The signal has been made more robust in order to enable mobile reception with high velocities and at mobile environments, for example, to cope with multipath reception. An additional error protection scheme has been included at the link layer, called Multiprotocol Encapsulation Forward Error Correction (MPE-FEC) [3]. Furthermore, the time slicing concept has been introduced for power saving at the terminal side, while services are transmitted in time slots and the receiver front end can be switched off in the meantime. Furthermore, the time slicing approach enables soft handover and optimized handover algorithms [4], and local content areas in single frequency networks [5]. Depending on the modulation and coding scheme, and the used channel bandwidth, a total downlink data rate of about 6–10 Mbps can be achieved. The individual user data rate depends on the time slicing setup and is typically set to 100–400 kbps.The currently deployed cellular system UMTS is standardized by the 3rd Generation Partnership Project (3GPP) and makes use of the Wideband Code Division Multiple Access (W-CDMA) technology. All users are served at a common frequency with a 5 MHz bandwidth and are separated by orthogonal codes. The network capacity and coverage suffer from interference and have to be planned carefully. The network is deployed in a cellular structure, which is dense in urban environments and larger cells occur in less populated areas. Mobile terminals are assigned to one or more cells, which are established by the Node-Bs. Different bearer services can be used in a circuit or packet switched mode and thus different types of user services are available, such as voice telephony, web browsing, streaming multimedia, and file download.The combination and the cooperation of both network types has been enabled by the IP Datacast standard, which has been specified within the International DVB Project [6]. It defines the higher layers and form an Internet Protocol- (IP-) based end-to-end system, together with the DVB-H specification as the physical layer [7]. In addition to the broadcast downstream, the IP Datacast reference architecture defines an optional link with an interactive cellular network, which is used, for example, for service requests. In terms of hybrid networks, the connection to a cellular network is defined as a mandatory feature.The combination of both network types has several advantages. On the one hand, popular content, which is requested by several users at the same time, can be switched from UMTS to a broadcast transmission for a more efficient delivery. The UMTS network can be unloaded in order to avoid a cell overload or to reduce the overall cell power and the intercell interference. On the other hand, the broadcast services are enhanced by an uplink channel and new features are possible, such as on-demand services.Hybrid networks do not exist nowadays. But at this stage, the deployment and operation are possible, since the fully standardized and currently deployed networks UMTS and DVB-H, the IP Datacast standard, and load balancing algorithms as, for instance, proposed in [8] exist. Nevertheless, efficient models are still needed for hybrid network planning and network Performance AnalysisWe propose a performance model, performance criteria, and constraints for load switching in hybrid network, which is called the switching bound concept. The performance is shown by applying different extents of load switching for a theoretical and a realistic scenario.This paper is structured as follows. In Section2 the services and the scenario are defined, which have been considered for our analyses. Section 3 describes models for the individual networks UMTS and DVB-H. In Section 4, the model for hybrid networks is derived, the network performance is evaluated, and the switching bounds are defined. In Section 5, the simulation results are shown by means of a realistic network scenario.


## 2. Definition of Services and Scenario

As mentioned above, several types of services are enabled by UMTS, and eight different types have been summarized in [9]. The defined service set S includes voice and video telephony, web browsing and email, location-based services, short/multimedia massaging service, file download, and streaming multimedia. It is assumed, that some of these services, namely, file download and streaming multimedia, are suitable to be transmitted by both network types, UMTS and DVB-H. For this subset of services, denoted as S(H), load switching can be applied by transferring the transmission from UMTS to the broadcast network. In this paper, we focus on the streaming multimedia service with an average stream data rate of 128 kbps. It has been turned out that this service generates a much higher contribution to the cell load, compared to the file download service. The other six services are called cellular services and have always to be delivered by the UMTS network.In addition to the definition of the service characteristics and the requirements, several scenarios have been proposed in [10], which contain reference network architectures with reference sites, antenna configurations, and predictions of the signal propagation loss. Furthermore, realistic traffic demands have been provided in the form of user intensity maps, which describe the spatial distribution of the service request rate.We select the public reference scenario of Berlin for our investigation, which is publicly available at [11]. Figure 1 shows the selected UMTS sites and the user intensity of the streaming multimedia service for the entire scenario area. A subset of 48 sites has been selected from the reference scenario, in which the angles of azimuth and elevation have not been modified. However, new signal prediction maps have been calculated with a more sophisticated propagation model, based on [12].Figure 1
UMTS network structure and user intensity for the streaming multimedia service in a 7.5 km×7.5 km scenario with a resolution of 50 m. The network is based on the public reference scenario of Berlin available at [10].


## 3. Modeling the UMTS and the DVB-H Network

The UMTS network is a bidirectional network, which has to be planned for both uplink and downlink. In general, the downlink part is the more critical one, due to a higher amount of traffic demand, caused by asymmetric services. The hybrid network can be beneficial for the downlink part, if the additional capacity and the combing capability of the broadcast network are used efficiently. It is assumed that all users are connected to the UMTS network at the uplink, regardless of the way of downlink delivery. Thus, load switching does not effect the uplink and traditional models can be applied. Therefore, we focus on the modeling of the downlink part.


### 3.1. Modeling the UMTS Downlink

An efficient model for UMTS network evaluation has been proposed in [13] and described in detail in [14]. We review this model in this section, since it is the basis for developing the model for hybrid networks. The UMTS model describes the service requirements, the generated user load, the intracell interference and the interference coupling between cells, which is caused by intercell interference. It provides an efficient estimation of the necessary transmit power for the mobile terminals and the Node-Bs by solving a linear equation system, which is called interference coupling system.The model can be used in conjunction with snapshot analyses using Monte-Carlo simulations. Several details can be implemented with this approach and thus, it generates accurate results for a high number of snapshots. But it is computational expensive. A more efficient approach has been proposed in [15] and is described more in detail in [14], which uses expected values for the user demand, instead of individual users. Thus, it replaces the computational expensive snapshot approach with an average view on the current network load. The drawback is that the accuracy of the results is somewhat decreased and some details are not modeled, for example, different velocities.In the context of this work, both methods have been considered. First, the downlink of hybrid networks is modeled and the principal behavior of load switching is shown by means of the expected approach. For network simulations performed in Section5, a realistic network and the snapshot approach are applied.For determining the cell load and the coupling between the cells, mobile terminals are assigned to the Node-Bs by the best server approach, whereas that cell is selected, which provides the strongest pilot (CPICH) signal. The best server areaAi of cell i is determined by using the pilot channel power pi(CPICH) and the end-to-end channel gain γi(x) from antenna i to location x, which basically includes the propagation loss and antenna gains.Each service requires a specific carrier-to-interference-and-noise ratio (CINR) target, denoted asμs, which includes the processing gain of the selected bearer service and the required Eb/N0 target [13]. The activity of the bearer usage is denoted by αs and the orthogonality loss factor of a location x∈Ai is denoted by ω(x). The orthogonality of the signals is partly lost due to multipath propagation effects and thus, intracell interference is caused. The caused load of service s at location x is calculated by
(1)ls(x):=αsμs1+ω(x)αsμs.
The interference coupling approach defines the coupling factors for cell i (2) as expected values, which form the interference-coupling downlink matrix C=(cij)i,j∈{1,...,Ncell}. The parameter Ts(x) represents the user intensity of service s at location x:
(2)cii=∫x∈Aiω(x)∑s∈Sls(x)Ts(x)dx,cij=∫x∈Aiγj(x)γi(x)∑s∈Sls(x)Ts(x)dx.The coupling system for all cellsi can be written as
(3)p̅i=ciip̅i+∑j≠icijp̅j+p̅i(η)+pi(CC)
with p̅i(η)=∫x∈Ai(η(x)/γi(x))∑s∈Sls(x)Ts(x)dx being the average required power to overcome the noise in a non interfered cell. The constant power values of all common channels including the pilot channel are summed up in pi(CC). The parameter p̅i denotes the average transmit power of cell i in order to serve all users and to overcome noise and interference.The resulting power valuesp̅i must not exceed the maximum feasible power pmax. In order to avoid cell overloading caused by high traffic demand and high interference, the scaling factor λi∈[0,1] has been proposed in [14]. It is used to scale the coupling matrix with diag(λ)C in order to reduce the traffic demand per cell and the coupling to the other cells. This approach assumes perfect load control with applying an exact downgrade of cell load. The scaling factor λi represents the Grade of Service (GoS); that is, the fraction of served traffic compared to the offered traffic in cell i, and (1-λi) defines the blocking probability of cell i. In [14] an iterative process has been defined to estimate the parameters λi and p̅i for each cell. The interference coupling concept assumes perfect power control, which enables to precisely assign the necessary power to each user.Another measure of the quality in network planning is the average other-to-own interferenceι̅i, which describes the total intercell interference to cell i (4). Note that this definition has to be divided by the average orthogonality loss (e.g., ω̅=0.673 for urban environments) in order to be comparable to traditional definitions, for example, in [13]:
(4)ι̅i=∑j≠icijp̅jciip̅i.
In [14], the so-called pole equations have been defined to calculate the necessary average transmit power p̅i (5) and the grade of service λi(C) (6) per cell with a given other-to-own interference. We have used the notation(C) to denote the cellular network, since we have also defined the GoS criteria for the broadcast(B) and the hybrid(H) network:
(5)p̅i={p(CC)+p̅i(η)1-(1+ι̅i)ciiif(1+ι̅i)cii<pmax-p(CC)-p̅i(η)pmax,pmax,otherwise,(6)λi(C)={1if(1+ι̅i)cii<pmax-p(CC)-p̅i(η)pmax,pmax-p(CC)pmax(1+ι̅i)cii+p̅i(η),otherwise.The transmit powerp̅i and the GoS λiC form a complementary system. The transmit power increases until the offered traffic load can be served or the maximum power is reached. Additional traffic load is blocked, which reduces the GoS of the cell. In this work, alternative concepts of solving a network overload as described in [16] are not considered.The GoS of the entire UMTS networkλ̅(C) is estimated by a weighting sum, considering the offered traffic load τi=∫x∈Ai∑s∈Sls(x)Ts(x)dx:
(7)λ̅(C)=∑iτiλi(C)∑iτi.
The utilization of the transmit power in each cell is measured with the downlink cell load. It is defined by the ratio of the necessary downlink transmit power compared to the total available maximum power: Li(C)=p̅i/pmax.


### 3.2. Modeling the Broadcast Network

In contrast to the UMTS network, the required system capacity of broadcast network is independent on the actual number of users. The necessary amount of capacity depends on the number of different content items, that are requested by the users [17].The requests for different content have different probabilities; that is, some items are more popular compared to others. A common description of the popularity is the Zipf-distribution defined in (8). Detailed analyses and measurements have been performed and described in [18].The probability that a user request corresponds to itemi is defined as
(8)P(X=i)=1Ωi-κwithΩ=∑i=1Nitemi-κ.
The parameter Nitem represents the number of items offered by the content provider and κ≥0 is the shape parameter of the popularity distribution. For a value of κ=0, the requests are uniformly distributed to all items. For an increasing value of κ, the popularity concentrates more and more on specific items.As mentioned above, we consider the streaming multimedia service for hybrid delivery, whereas several requested streams are switched to the broadcast network. Since streaming is a real-time service, the broadcast capacity is shared among all switched streams in a parallel transmission. Each stream requires a constant data ratedi in order to guarantee sufficient Quality of Service (QoS).We define the broadcast cell loadL(B) in (9), which depends on the number of delivered streams and the capacity D(B), reserved for switchable services. If L(B) exceed one, the broadcast cell is overloaded, some streams have to be blocked and the corresponding users cannot be served:
(9)L(B)=∑i=1ndiD(B).
The maximum number of streams, which can be delivered in parallel, is calculated by nmax=⌊D(B)/d̅⌋ with d̅ being the average required data rate of all streaming services.


## 3.1. Modeling the UMTS Downlink

An efficient model for UMTS network evaluation has been proposed in [13] and described in detail in [14]. We review this model in this section, since it is the basis for developing the model for hybrid networks. The UMTS model describes the service requirements, the generated user load, the intracell interference and the interference coupling between cells, which is caused by intercell interference. It provides an efficient estimation of the necessary transmit power for the mobile terminals and the Node-Bs by solving a linear equation system, which is called interference coupling system.The model can be used in conjunction with snapshot analyses using Monte-Carlo simulations. Several details can be implemented with this approach and thus, it generates accurate results for a high number of snapshots. But it is computational expensive. A more efficient approach has been proposed in [15] and is described more in detail in [14], which uses expected values for the user demand, instead of individual users. Thus, it replaces the computational expensive snapshot approach with an average view on the current network load. The drawback is that the accuracy of the results is somewhat decreased and some details are not modeled, for example, different velocities.In the context of this work, both methods have been considered. First, the downlink of hybrid networks is modeled and the principal behavior of load switching is shown by means of the expected approach. For network simulations performed in Section5, a realistic network and the snapshot approach are applied.For determining the cell load and the coupling between the cells, mobile terminals are assigned to the Node-Bs by the best server approach, whereas that cell is selected, which provides the strongest pilot (CPICH) signal. The best server areaAi of cell i is determined by using the pilot channel power pi(CPICH) and the end-to-end channel gain γi(x) from antenna i to location x, which basically includes the propagation loss and antenna gains.Each service requires a specific carrier-to-interference-and-noise ratio (CINR) target, denoted asμs, which includes the processing gain of the selected bearer service and the required Eb/N0 target [13]. The activity of the bearer usage is denoted by αs and the orthogonality loss factor of a location x∈Ai is denoted by ω(x). The orthogonality of the signals is partly lost due to multipath propagation effects and thus, intracell interference is caused. The caused load of service s at location x is calculated by
(1)ls(x):=αsμs1+ω(x)αsμs.
The interference coupling approach defines the coupling factors for cell i (2) as expected values, which form the interference-coupling downlink matrix C=(cij)i,j∈{1,...,Ncell}. The parameter Ts(x) represents the user intensity of service s at location x:
(2)cii=∫x∈Aiω(x)∑s∈Sls(x)Ts(x)dx,cij=∫x∈Aiγj(x)γi(x)∑s∈Sls(x)Ts(x)dx.The coupling system for all cellsi can be written as
(3)p̅i=ciip̅i+∑j≠icijp̅j+p̅i(η)+pi(CC)
with p̅i(η)=∫x∈Ai(η(x)/γi(x))∑s∈Sls(x)Ts(x)dx being the average required power to overcome the noise in a non interfered cell. The constant power values of all common channels including the pilot channel are summed up in pi(CC). The parameter p̅i denotes the average transmit power of cell i in order to serve all users and to overcome noise and interference.The resulting power valuesp̅i must not exceed the maximum feasible power pmax. In order to avoid cell overloading caused by high traffic demand and high interference, the scaling factor λi∈[0,1] has been proposed in [14]. It is used to scale the coupling matrix with diag(λ)C in order to reduce the traffic demand per cell and the coupling to the other cells. This approach assumes perfect load control with applying an exact downgrade of cell load. The scaling factor λi represents the Grade of Service (GoS); that is, the fraction of served traffic compared to the offered traffic in cell i, and (1-λi) defines the blocking probability of cell i. In [14] an iterative process has been defined to estimate the parameters λi and p̅i for each cell. The interference coupling concept assumes perfect power control, which enables to precisely assign the necessary power to each user.Another measure of the quality in network planning is the average other-to-own interferenceι̅i, which describes the total intercell interference to cell i (4). Note that this definition has to be divided by the average orthogonality loss (e.g., ω̅=0.673 for urban environments) in order to be comparable to traditional definitions, for example, in [13]:
(4)ι̅i=∑j≠icijp̅jciip̅i.
In [14], the so-called pole equations have been defined to calculate the necessary average transmit power p̅i (5) and the grade of service λi(C) (6) per cell with a given other-to-own interference. We have used the notation(C) to denote the cellular network, since we have also defined the GoS criteria for the broadcast(B) and the hybrid(H) network:
(5)p̅i={p(CC)+p̅i(η)1-(1+ι̅i)ciiif(1+ι̅i)cii<pmax-p(CC)-p̅i(η)pmax,pmax,otherwise,(6)λi(C)={1if(1+ι̅i)cii<pmax-p(CC)-p̅i(η)pmax,pmax-p(CC)pmax(1+ι̅i)cii+p̅i(η),otherwise.The transmit powerp̅i and the GoS λiC form a complementary system. The transmit power increases until the offered traffic load can be served or the maximum power is reached. Additional traffic load is blocked, which reduces the GoS of the cell. In this work, alternative concepts of solving a network overload as described in [16] are not considered.The GoS of the entire UMTS networkλ̅(C) is estimated by a weighting sum, considering the offered traffic load τi=∫x∈Ai∑s∈Sls(x)Ts(x)dx:
(7)λ̅(C)=∑iτiλi(C)∑iτi.
The utilization of the transmit power in each cell is measured with the downlink cell load. It is defined by the ratio of the necessary downlink transmit power compared to the total available maximum power: Li(C)=p̅i/pmax.


## 3.2. Modeling the Broadcast Network

In contrast to the UMTS network, the required system capacity of broadcast network is independent on the actual number of users. The necessary amount of capacity depends on the number of different content items, that are requested by the users [17].The requests for different content have different probabilities; that is, some items are more popular compared to others. A common description of the popularity is the Zipf-distribution defined in (8). Detailed analyses and measurements have been performed and described in [18].The probability that a user request corresponds to itemi is defined as
(8)P(X=i)=1Ωi-κwithΩ=∑i=1Nitemi-κ.
The parameter Nitem represents the number of items offered by the content provider and κ≥0 is the shape parameter of the popularity distribution. For a value of κ=0, the requests are uniformly distributed to all items. For an increasing value of κ, the popularity concentrates more and more on specific items.As mentioned above, we consider the streaming multimedia service for hybrid delivery, whereas several requested streams are switched to the broadcast network. Since streaming is a real-time service, the broadcast capacity is shared among all switched streams in a parallel transmission. Each stream requires a constant data ratedi in order to guarantee sufficient Quality of Service (QoS).We define the broadcast cell loadL(B) in (9), which depends on the number of delivered streams and the capacity D(B), reserved for switchable services. If L(B) exceed one, the broadcast cell is overloaded, some streams have to be blocked and the corresponding users cannot be served:
(9)L(B)=∑i=1ndiD(B).
The maximum number of streams, which can be delivered in parallel, is calculated by nmax=⌊D(B)/d̅⌋ with d̅ being the average required data rate of all streaming services.


## 4. Modeling Hybrid Networks

In a hybrid system, the delivery of services can be switched between the downlinks of both networks, UMTS and DVB-H. The overall network performance can be optimized with properly applying load switching.The load switching in hybrid networks is modeled by specifying the amount of traffic, which is switched from the UMTS network to the broadcast network. In contrast to GoS scaling, the switched traffic is not lost, but served by the broadcast system. Thus, the UMTS network is unloaded, while improving the overall grade of service.First of all, the offered traffic has to be classified into the cellular service demand and switchable services (see Section2). Since the coupling matrix C is generated by summing up the user demand for each service, this matrix can be split into the cellular C(C) and the hybrid part C(H). All elements in the matrices can be calculated by using (2) and the appropriate set of services S/S(H) and S(H).We define the load share parameterβ, which specifies the fraction of cellular (nonswitchable) traffic load as β=c(C)/(c(C)+c(H)). For β=0, it is possible to switch the entire traffic load to the broadcast network and for β=1, no switchable traffic load exists at all. The ratio parameter β is dependent on the location. An average value of about 43% occurs for evaluating the area of the Berlin reference scenario by considering the streaming multimedia service for load switching. That means, about 57% of the traffic can be used for unloading the UMTS network.


### 4.1. Definition of Load Switching

The fraction of traffic load is estimated, which is actually needed to be switched to the broadcast network for a successful unloading of the UMTS network. For example, a typical target criteria for network planning is to achieve a cell blocking rate of 2% or less, which corresponds toλi≥0.98.We define the switching parameterδi∈[0,1], which is used to control the amount of traffic load, switched between both networks. A value of δi=0 indicates that all requests for the switchable service are switched to the broadcast network. For example, all streaming services are served by the DVB-H network. Therewith, we define c̃ij as the new coupling factor for the UMTS network with
(10)c̃ij=cij(C)+δicij(H).
It has been shown in [8] that those service items are beneficial for optimizing hybrid networks, which causes a high load in the UMTS network. The product of arrival rate and required resources (i.e., data rate) has been taken as a measure to define the switching order. In this work, we assume equal data rates for all streams and therefore, the item request rate is directly proportional to its popularity. Thus, we assume that the most popular items are switched first.As mentioned before, both networks can be described by different schemes, the user and content dependence. In order to estimate the broadcast cell load, we have to transform the applied load switching with the switching parameterδ into the corresponding number of streams nδ, which have to be delivered by the broadcast network. The fraction (1-δ) corresponds to the n most popular items. Thus, we look for the minimum n, which fulfils 1-δ≤P(X≤n). In other words, the (Nitem-n) low popular items have to be served by the UMTS network. Thus, the relation between n and δ can be defined as
(11)nδ=minn{n∣P(X>n)≤δ}.
On the other hand, the estimation of δ for a given number of streams n, which have to be switched, can be described by δn=P(X>n).In Figure2, the number of streams is shown, which is offered to the broadcast system, depending on δ and the popularity shape parameter κ. The total number of items is set to Nitem=100. It can be seen that, with κ=0, all items have the same popularity and thus, n increases linearly for more broadcast traffic. For an increasing κ, a higher fraction of traffic corresponds to the more popular items. The broadcast technology allows for combining user requests to the corresponding high popular items. Thus, for the same δ, the broadcast network is less loaded for higher values of κ.Figure 2
Comparing the switching parameterδ with the number of streams n, which are offered to the broadcast system, depending on the popularity distribution.


### 4.2. Definition of Switching Bounds

Depending on the system parameters and the traffic demand, the possible range of switching may be limited, if cell load values ofL(B)≤1 and L(C)≤1 have to be guaranteed. We introduce the switching bound concept, which defines a bound for δ for each network separately. In order to apply reasonable load switching in a hybrid network, the broadcast network should define the lower and the UMTS network the upper bound of δ. The switching bounds indicate the necessity and feasibility of applying a hybrid network. It is obvious, if no bound has been set by the UMTS network, no unloading is required.


#### 4.2.1. Broadcast Bound

The boundδ(B)∈[0,1] is defined by the parameters of the broadcast network and the streaming service. Since the broadcast capacity is limited, the number of parallel transmittable streams is limited too. If the broadcast cell load defined in (9) equals one, the maximum number of streams nmax is reached. The broadcast bound δ(B) is calculated by
(12)δ(B)=P(X>nmax)=P(X>⌊D(B)d¯⌋).
Therefore, δ(B) depends on the broadcast capacity D(B), the service data rate di, and the popularity distribution, that is, Nitem and κ.Figure3 shows δ(B) depending on the popularity shape parameter κ and the broadcast cell capacity D(B). The number of items is set to Nitem=100 and the average streaming data rate is d¯=128kbps. It is obvious that for higher broadcast data rates, more traffic can be switched to the broadcast network and δ(B) is decreased and less restrictive. For a more even popularity distribution (smaller values of κ), the bound increases due to a higher variety of the requested streams and thus, a higher broadcast capacity demand. For a very large values of κ, the popularity corresponds to a very small number of items, in extremum to one single item. Thus, all requests can be delivered by the broadcast network. For D(B)=Nd¯=12.8Mbps, it is possible to serve all items by the broadcast network, thus δ(B)=0 and no restrictions exist from the broadcast network side. For a too small capacity, that no stream can be transmitted, the broadcast bound equals one for any value of κ.Figure 3
Broadcast boundδ(B) depending on the popularity shape κ and the total broadcast capacity.


#### 4.2.2. Cellular Bound

The cellular boundδi(C)∈[0,1] describes the minimum necessary unloading of cell i by switching streaming services to the broadcast network, until the GoS target λ̂ is reached. In this work, we use λ̂=0.98. Once the cell is able to handle the amount of offered traffic, a further unloading yields to a reduction of the required transmit power p̃i. In order to estimate δ(C), we use  (6) with c̃ii=cii(C)+δicii(H) and p̃i(η)=pi(η,C)+δipi(η,H), and transform it to δi:
(13)δi(C)={pmax-p(CC)-λ̂pmax(1+ι̅i)cii(C)-pi(η,C)λ̂pmax(1+ι̅i)cii(H)+pi(η,H),λi(C)<λ̂,1,λ̂≤λi(C)≤1.
It is obvious that a solution is only possible, if switchable traffic exists in cell i, so that cii(H)>0. If all services are switchable (β=0) and the GoS target is set to one, the bound δi(C) becomes equal to the GoS λi(C).In the following, we investigate a theoretical scenario of a single cell, in which the influence from the surrounding cells, that is, the average other-to-own interferenceι̅i, is assumed to be constant. The curves in Figure 4 depict the cellular bound δ(C). The bound is shown for different cell loads cii and depending on load share parameter βii. For βii=1, no hybrid traffic exists and no load switching can be applied. For βii=0, all services can be transmitted by both network types. This case shows the less restrictive bound values, since no cellular background traffic exists.Figure 4
Cellular boundδi(C) for cell i depending on the load share parameter β and the total offered traffic load.In this example, the following parameters have been assumed:pmax=14W, p(CC)=4W, ι̅i=0.6, and p(η)=0.96mW. If λi(C)≥λ̂, no traffic needs to be switched, since the QoS target is already achieved without applying unloading. The threshold in our case is cii=0.45. This threshold can be calculated by using (6) with λi(C)=λ̂. Thus, δi(C) is set to one for all values of cii, which are lower or equal as this threshold.In general, it can be seen thatδi(C) decreases for higher cell load, since the cell needs to be unloaded more and more. For high traffic loads, the proportion of switchable traffic needs to be high, that is, low values of β, in order to apply sufficient load switching for successfully unload the cells.


#### 4.2.3. Discussion on Switching Bounds

We have defined two bounds for the load switching, which depend on the offered traffic, the load share, and on the parameters of the broadcast and UMTS network, respectively. The broadcast bound is valid for the entire broadcast cell. Each UMTS cell defines an individual bound, depending on the current loading, the grade of service, and the cell parameters.In order to successfully apply unloading in a hybrid network the broadcast bound needs to be lower than the cellular bound,δ(B)≤δ(C). All UMTS cells, which are covered by a broadcast cell and which are considered for a hybrid mode, need to be evaluated. Therefore the minimum of all δi(C) has to be considered, if the quality target has to be reached. Nevertheless, strategies for network planning and estimating the appropriate cellular bound are beyond the scope of the paper.If no broadcast bound is defined, the necessary broadcast data rate can be determined. The estimated switching parameterδ is based on the evaluation of the UMTS cells. It defines the necessary capacity of the broadcast network, which can be estimated by the number of streams to be transmitted (11). By summing up the required data rates of all items switched to broadcast the necessary broadcast data rate can be calculated.


### 4.3. Grade of Service in Hybrid Networks

The grade of service of a broadcast network can be defined in two ways, a content- or a user-based description. The user-based definition has been selected, due to compatibility reasons to the cellular network and for a common GoS estimation of hybrid networks. It is estimated by evaluating the probability of the blocked streams.Ifδ<δ(B), the capacity of the broadcast network is not sufficient for transmitting the switched content and users have to be blocked. The probability of not served streams is
(14)P(X≤nδ)-P(X≤nmax)=(1-δ)-(1-δ(B))=δ(B)-δ.Thus the grade of service of the broadcast network is calculated byλ(B)=min{1,1-(δ(B)-δ)}. It is obvious that for δ≥δ(B), no blocking occurs and λ(B)=1.In order to estimate the grade of serviceλi(H) for the hybrid network, the GoS parameters λi(C) and λ(B) have to be combined. Equation (15) describes the ratio of the traffic served by the hybrid network compared to the total offered traffic. This ratio is transformed into a weighted term; whereas the weights describe the fraction of cellular and switchable traffic:
(15)λi,δ(H)=λ̃i,δ(C)(cii(C)+δcii(H))+λδ(B)(1-δ)cii(H)cii=[δ(1-βii)+βii]λ̃i,δ(C)+(1-δ)(1-βii)λδ(B).
For δ=1, the total traffic has to be transmitted by the UMTS network, thus, λ(H)=λ(C). In the case that δ=0, the UMTS network is unloaded by all switchable traffic, but this traffic is partly lost due to an overloaded broadcast network. The GoS of the broadcast network results in λ(B)=1-δ(B). The GoS of the UMTS network can be calculated by (6) using c̃ii=cii(C) and p̃i(η)=pi(U,η).


### 4.4. Performance Analysis

A single cell is considered with fixed interference from the surrounding cells. This simplified scenario is used in order to show the basic behavior of the network performance indicators cell load (L(C) and L(B)) and grade of service (λ(C), λ(B), and λ(H)).Figure5 shows these performance indicators. In this example, the parameters of Section 4.2 and the following parameters have been used: D(B)=2Mbps, di=128kbps, Nitem=100, κ=1, cii=0.5, and β=0.6.Performance indicators cell load and grade of service in a hybrid network for a single cell, depending on the switching ratioδ.
(a)(b)The used parameter values result in the bound values ofδ(C)=0.78 and δ(B)=0.36. The cell load of the broadcast cell increases with a decreasing δ until the broadcast bound is reached. The nonuniform steps are caused by the different content popularity values. The UMTS cell load decreases while unloading beyond the cellular bound, caused by a lower necessary transmit power p̃i.The grade of service is shown for both network types and the hybrid combination. For high values ofδ the curves of λ(C) and λ(H) are similar due to a small contribution of the broadcast part with a small amount of traffic switched to the broadcast network. A significant gain of the GoS exists, if the initial case (λδ=1(C)) is compared to compared to the hybrid network with applied unloading (λδ=δ(B)(H)).In the depicted case, a wide operating range is shown for achieving sufficient GoS. From a network planning point of view, applying anδ<δ(B) is not preferable, since the overall GoS λ(H) decreases very fast, due to the high influence of the overloaded broadcast network (see (15)).


## 4.1. Definition of Load Switching

The fraction of traffic load is estimated, which is actually needed to be switched to the broadcast network for a successful unloading of the UMTS network. For example, a typical target criteria for network planning is to achieve a cell blocking rate of 2% or less, which corresponds toλi≥0.98.We define the switching parameterδi∈[0,1], which is used to control the amount of traffic load, switched between both networks. A value of δi=0 indicates that all requests for the switchable service are switched to the broadcast network. For example, all streaming services are served by the DVB-H network. Therewith, we define c̃ij as the new coupling factor for the UMTS network with
(10)c̃ij=cij(C)+δicij(H).
It has been shown in [8] that those service items are beneficial for optimizing hybrid networks, which causes a high load in the UMTS network. The product of arrival rate and required resources (i.e., data rate) has been taken as a measure to define the switching order. In this work, we assume equal data rates for all streams and therefore, the item request rate is directly proportional to its popularity. Thus, we assume that the most popular items are switched first.As mentioned before, both networks can be described by different schemes, the user and content dependence. In order to estimate the broadcast cell load, we have to transform the applied load switching with the switching parameterδ into the corresponding number of streams nδ, which have to be delivered by the broadcast network. The fraction (1-δ) corresponds to the n most popular items. Thus, we look for the minimum n, which fulfils 1-δ≤P(X≤n). In other words, the (Nitem-n) low popular items have to be served by the UMTS network. Thus, the relation between n and δ can be defined as
(11)nδ=minn{n∣P(X>n)≤δ}.
On the other hand, the estimation of δ for a given number of streams n, which have to be switched, can be described by δn=P(X>n).In Figure2, the number of streams is shown, which is offered to the broadcast system, depending on δ and the popularity shape parameter κ. The total number of items is set to Nitem=100. It can be seen that, with κ=0, all items have the same popularity and thus, n increases linearly for more broadcast traffic. For an increasing κ, a higher fraction of traffic corresponds to the more popular items. The broadcast technology allows for combining user requests to the corresponding high popular items. Thus, for the same δ, the broadcast network is less loaded for higher values of κ.Figure 2
Comparing the switching parameterδ with the number of streams n, which are offered to the broadcast system, depending on the popularity distribution.


## 4.2. Definition of Switching Bounds

Depending on the system parameters and the traffic demand, the possible range of switching may be limited, if cell load values ofL(B)≤1 and L(C)≤1 have to be guaranteed. We introduce the switching bound concept, which defines a bound for δ for each network separately. In order to apply reasonable load switching in a hybrid network, the broadcast network should define the lower and the UMTS network the upper bound of δ. The switching bounds indicate the necessity and feasibility of applying a hybrid network. It is obvious, if no bound has been set by the UMTS network, no unloading is required.


### 4.2.1. Broadcast Bound

The boundδ(B)∈[0,1] is defined by the parameters of the broadcast network and the streaming service. Since the broadcast capacity is limited, the number of parallel transmittable streams is limited too. If the broadcast cell load defined in (9) equals one, the maximum number of streams nmax is reached. The broadcast bound δ(B) is calculated by
(12)δ(B)=P(X>nmax)=P(X>⌊D(B)d¯⌋).
Therefore, δ(B) depends on the broadcast capacity D(B), the service data rate di, and the popularity distribution, that is, Nitem and κ.Figure3 shows δ(B) depending on the popularity shape parameter κ and the broadcast cell capacity D(B). The number of items is set to Nitem=100 and the average streaming data rate is d¯=128kbps. It is obvious that for higher broadcast data rates, more traffic can be switched to the broadcast network and δ(B) is decreased and less restrictive. For a more even popularity distribution (smaller values of κ), the bound increases due to a higher variety of the requested streams and thus, a higher broadcast capacity demand. For a very large values of κ, the popularity corresponds to a very small number of items, in extremum to one single item. Thus, all requests can be delivered by the broadcast network. For D(B)=Nd¯=12.8Mbps, it is possible to serve all items by the broadcast network, thus δ(B)=0 and no restrictions exist from the broadcast network side. For a too small capacity, that no stream can be transmitted, the broadcast bound equals one for any value of κ.Figure 3
Broadcast boundδ(B) depending on the popularity shape κ and the total broadcast capacity.


### 4.2.2. Cellular Bound

The cellular boundδi(C)∈[0,1] describes the minimum necessary unloading of cell i by switching streaming services to the broadcast network, until the GoS target λ̂ is reached. In this work, we use λ̂=0.98. Once the cell is able to handle the amount of offered traffic, a further unloading yields to a reduction of the required transmit power p̃i. In order to estimate δ(C), we use  (6) with c̃ii=cii(C)+δicii(H) and p̃i(η)=pi(η,C)+δipi(η,H), and transform it to δi:
(13)δi(C)={pmax-p(CC)-λ̂pmax(1+ι̅i)cii(C)-pi(η,C)λ̂pmax(1+ι̅i)cii(H)+pi(η,H),λi(C)<λ̂,1,λ̂≤λi(C)≤1.
It is obvious that a solution is only possible, if switchable traffic exists in cell i, so that cii(H)>0. If all services are switchable (β=0) and the GoS target is set to one, the bound δi(C) becomes equal to the GoS λi(C).In the following, we investigate a theoretical scenario of a single cell, in which the influence from the surrounding cells, that is, the average other-to-own interferenceι̅i, is assumed to be constant. The curves in Figure 4 depict the cellular bound δ(C). The bound is shown for different cell loads cii and depending on load share parameter βii. For βii=1, no hybrid traffic exists and no load switching can be applied. For βii=0, all services can be transmitted by both network types. This case shows the less restrictive bound values, since no cellular background traffic exists.Figure 4
Cellular boundδi(C) for cell i depending on the load share parameter β and the total offered traffic load.In this example, the following parameters have been assumed:pmax=14W, p(CC)=4W, ι̅i=0.6, and p(η)=0.96mW. If λi(C)≥λ̂, no traffic needs to be switched, since the QoS target is already achieved without applying unloading. The threshold in our case is cii=0.45. This threshold can be calculated by using (6) with λi(C)=λ̂. Thus, δi(C) is set to one for all values of cii, which are lower or equal as this threshold.In general, it can be seen thatδi(C) decreases for higher cell load, since the cell needs to be unloaded more and more. For high traffic loads, the proportion of switchable traffic needs to be high, that is, low values of β, in order to apply sufficient load switching for successfully unload the cells.


### 4.2.3. Discussion on Switching Bounds

We have defined two bounds for the load switching, which depend on the offered traffic, the load share, and on the parameters of the broadcast and UMTS network, respectively. The broadcast bound is valid for the entire broadcast cell. Each UMTS cell defines an individual bound, depending on the current loading, the grade of service, and the cell parameters.In order to successfully apply unloading in a hybrid network the broadcast bound needs to be lower than the cellular bound,δ(B)≤δ(C). All UMTS cells, which are covered by a broadcast cell and which are considered for a hybrid mode, need to be evaluated. Therefore the minimum of all δi(C) has to be considered, if the quality target has to be reached. Nevertheless, strategies for network planning and estimating the appropriate cellular bound are beyond the scope of the paper.If no broadcast bound is defined, the necessary broadcast data rate can be determined. The estimated switching parameterδ is based on the evaluation of the UMTS cells. It defines the necessary capacity of the broadcast network, which can be estimated by the number of streams to be transmitted (11). By summing up the required data rates of all items switched to broadcast the necessary broadcast data rate can be calculated.


## 4.2.1. Broadcast Bound

The boundδ(B)∈[0,1] is defined by the parameters of the broadcast network and the streaming service. Since the broadcast capacity is limited, the number of parallel transmittable streams is limited too. If the broadcast cell load defined in (9) equals one, the maximum number of streams nmax is reached. The broadcast bound δ(B) is calculated by
(12)δ(B)=P(X>nmax)=P(X>⌊D(B)d¯⌋).
Therefore, δ(B) depends on the broadcast capacity D(B), the service data rate di, and the popularity distribution, that is, Nitem and κ.Figure3 shows δ(B) depending on the popularity shape parameter κ and the broadcast cell capacity D(B). The number of items is set to Nitem=100 and the average streaming data rate is d¯=128kbps. It is obvious that for higher broadcast data rates, more traffic can be switched to the broadcast network and δ(B) is decreased and less restrictive. For a more even popularity distribution (smaller values of κ), the bound increases due to a higher variety of the requested streams and thus, a higher broadcast capacity demand. For a very large values of κ, the popularity corresponds to a very small number of items, in extremum to one single item. Thus, all requests can be delivered by the broadcast network. For D(B)=Nd¯=12.8Mbps, it is possible to serve all items by the broadcast network, thus δ(B)=0 and no restrictions exist from the broadcast network side. For a too small capacity, that no stream can be transmitted, the broadcast bound equals one for any value of κ.Figure 3
Broadcast boundδ(B) depending on the popularity shape κ and the total broadcast capacity.


## 4.2.2. Cellular Bound

The cellular boundδi(C)∈[0,1] describes the minimum necessary unloading of cell i by switching streaming services to the broadcast network, until the GoS target λ̂ is reached. In this work, we use λ̂=0.98. Once the cell is able to handle the amount of offered traffic, a further unloading yields to a reduction of the required transmit power p̃i. In order to estimate δ(C), we use  (6) with c̃ii=cii(C)+δicii(H) and p̃i(η)=pi(η,C)+δipi(η,H), and transform it to δi:
(13)δi(C)={pmax-p(CC)-λ̂pmax(1+ι̅i)cii(C)-pi(η,C)λ̂pmax(1+ι̅i)cii(H)+pi(η,H),λi(C)<λ̂,1,λ̂≤λi(C)≤1.
It is obvious that a solution is only possible, if switchable traffic exists in cell i, so that cii(H)>0. If all services are switchable (β=0) and the GoS target is set to one, the bound δi(C) becomes equal to the GoS λi(C).In the following, we investigate a theoretical scenario of a single cell, in which the influence from the surrounding cells, that is, the average other-to-own interferenceι̅i, is assumed to be constant. The curves in Figure 4 depict the cellular bound δ(C). The bound is shown for different cell loads cii and depending on load share parameter βii. For βii=1, no hybrid traffic exists and no load switching can be applied. For βii=0, all services can be transmitted by both network types. This case shows the less restrictive bound values, since no cellular background traffic exists.Figure 4
Cellular boundδi(C) for cell i depending on the load share parameter β and the total offered traffic load.In this example, the following parameters have been assumed:pmax=14W, p(CC)=4W, ι̅i=0.6, and p(η)=0.96mW. If λi(C)≥λ̂, no traffic needs to be switched, since the QoS target is already achieved without applying unloading. The threshold in our case is cii=0.45. This threshold can be calculated by using (6) with λi(C)=λ̂. Thus, δi(C) is set to one for all values of cii, which are lower or equal as this threshold.In general, it can be seen thatδi(C) decreases for higher cell load, since the cell needs to be unloaded more and more. For high traffic loads, the proportion of switchable traffic needs to be high, that is, low values of β, in order to apply sufficient load switching for successfully unload the cells.


## 4.2.3. Discussion on Switching Bounds

We have defined two bounds for the load switching, which depend on the offered traffic, the load share, and on the parameters of the broadcast and UMTS network, respectively. The broadcast bound is valid for the entire broadcast cell. Each UMTS cell defines an individual bound, depending on the current loading, the grade of service, and the cell parameters.In order to successfully apply unloading in a hybrid network the broadcast bound needs to be lower than the cellular bound,δ(B)≤δ(C). All UMTS cells, which are covered by a broadcast cell and which are considered for a hybrid mode, need to be evaluated. Therefore the minimum of all δi(C) has to be considered, if the quality target has to be reached. Nevertheless, strategies for network planning and estimating the appropriate cellular bound are beyond the scope of the paper.If no broadcast bound is defined, the necessary broadcast data rate can be determined. The estimated switching parameterδ is based on the evaluation of the UMTS cells. It defines the necessary capacity of the broadcast network, which can be estimated by the number of streams to be transmitted (11). By summing up the required data rates of all items switched to broadcast the necessary broadcast data rate can be calculated.


## 4.3. Grade of Service in Hybrid Networks

The grade of service of a broadcast network can be defined in two ways, a content- or a user-based description. The user-based definition has been selected, due to compatibility reasons to the cellular network and for a common GoS estimation of hybrid networks. It is estimated by evaluating the probability of the blocked streams.Ifδ<δ(B), the capacity of the broadcast network is not sufficient for transmitting the switched content and users have to be blocked. The probability of not served streams is
(14)P(X≤nδ)-P(X≤nmax)=(1-δ)-(1-δ(B))=δ(B)-δ.Thus the grade of service of the broadcast network is calculated byλ(B)=min{1,1-(δ(B)-δ)}. It is obvious that for δ≥δ(B), no blocking occurs and λ(B)=1.In order to estimate the grade of serviceλi(H) for the hybrid network, the GoS parameters λi(C) and λ(B) have to be combined. Equation (15) describes the ratio of the traffic served by the hybrid network compared to the total offered traffic. This ratio is transformed into a weighted term; whereas the weights describe the fraction of cellular and switchable traffic:
(15)λi,δ(H)=λ̃i,δ(C)(cii(C)+δcii(H))+λδ(B)(1-δ)cii(H)cii=[δ(1-βii)+βii]λ̃i,δ(C)+(1-δ)(1-βii)λδ(B).
For δ=1, the total traffic has to be transmitted by the UMTS network, thus, λ(H)=λ(C). In the case that δ=0, the UMTS network is unloaded by all switchable traffic, but this traffic is partly lost due to an overloaded broadcast network. The GoS of the broadcast network results in λ(B)=1-δ(B). The GoS of the UMTS network can be calculated by (6) using c̃ii=cii(C) and p̃i(η)=pi(U,η).


## 4.4. Performance Analysis

A single cell is considered with fixed interference from the surrounding cells. This simplified scenario is used in order to show the basic behavior of the network performance indicators cell load (L(C) and L(B)) and grade of service (λ(C), λ(B), and λ(H)).Figure5 shows these performance indicators. In this example, the parameters of Section 4.2 and the following parameters have been used: D(B)=2Mbps, di=128kbps, Nitem=100, κ=1, cii=0.5, and β=0.6.Performance indicators cell load and grade of service in a hybrid network for a single cell, depending on the switching ratioδ.
(a)(b)The used parameter values result in the bound values ofδ(C)=0.78 and δ(B)=0.36. The cell load of the broadcast cell increases with a decreasing δ until the broadcast bound is reached. The nonuniform steps are caused by the different content popularity values. The UMTS cell load decreases while unloading beyond the cellular bound, caused by a lower necessary transmit power p̃i.The grade of service is shown for both network types and the hybrid combination. For high values ofδ the curves of λ(C) and λ(H) are similar due to a small contribution of the broadcast part with a small amount of traffic switched to the broadcast network. A significant gain of the GoS exists, if the initial case (λδ=1(C)) is compared to compared to the hybrid network with applied unloading (λδ=δ(B)(H)).In the depicted case, a wide operating range is shown for achieving sufficient GoS. From a network planning point of view, applying anδ<δ(B) is not preferable, since the overall GoS λ(H) decreases very fast, due to the high influence of the overloaded broadcast network (see (15)).


## 5. Simulation of a Realistic Scenario

In this section, the behavior of the performance indicators GoS and the cell load is shown by means of a realistic scenario. In the shown example, it is assumed that the broadcast cell covers the entire scenario area. This can be assured by, for example, reusing a DVB-T transmitter for DVB-H, a real transmitter is available at the Berlin scenario.As mentioned before, the snapshot approach has been used in order to achieve accurate results of the performance indicators. The snapshot method has been implement, considering indoor and outdoor users, and signal shadowing with spatial correlation. We have performed a sufficiently large number of snapshots and testedt confidence interval [19,Ch. 4] in order to achieve average values of the transmit power and the GoS, that are within a range of ±1% around the expected value with a 99% confidence.


### 5.1. Simulation Results

For the network scenario, the parameters have been used as described in Section4.4. The cells of the UMTS network are unloaded, if content is switched from the UMTS network to the broadcast network. The grade of service λi is increased, and the transmit power pi is decreased significantly.Figure6 shows the empirical cumulative distribution of the GoS of all cells in the network. Each curve represents a switching value for δ from zero to one in 0.1 steps and the broadcast bound δ(B)=0.36. It can be seen that the GoS increases for lower values of δ. For the full-loaded network (δ=1) about 31% of the cells are above the GoS target λ̂=0.98. By unloading the cells with δ=δ(B)=0.36 about 55% and for δ=0 about 89% of the cells meet the target threshold. The dotted lines represent the cases with δ<δ(B), in which the broadcast network is overloaded.Figure 6
Empirical CDF for grade of service depending on the switching parameterδ. Dotted curves represent the cases with the broadcast network in overload.In order to estimate, which UMTS cells benefit from load switching, the GoS gain is defined by the ratio of the unloaded cells(λi,δ=δ(B)(C)) to the fully loaded case (λi,δ=1(C)). Figure 7 shows the gain values, based on the best server cell structure. A high gain occurs for those cells, in which a high traffic demand exists (compare to Figure 1). For the cells with a gain equal to one, unloading has not been necessary due to a low traffic load. A maximum gain of about 75% increase in GoS can be seen in some selected cells, which benefit most of using a hybrid mode.Figure 7
Gain of grade of service for unloading the UMTS cells byδ=δ(B).We use (7) in order to calculate the grade of service of the entire network. For network planning the grade of service should be λ̂=98% or higher. In Figure 8 the performance of the hybrid network is shown. The GoS of the UMTS network λ̅(C) increases by unloading but does not meet λ̂ for an unloading till δ(B)(λδ=δ(B)(C)=92.6%). The UMTS bound δ(C) is not depicted since for δ=0 a GoS of λ̅(C)=97.9%<λ̂ is achieved. Since the δ(C)<δ(B) the maximum total GoS occurs for δ=δ(B) with λ̅(H)=95.5%.Figure 8
Grade of service of the entire network depending on the amount of load switching.If cells are unloaded and the GoS target is reached, a further unloading leads to a reduction of the necessary transmit cell power. In Figure9, the power reduction gain is shown per cell. A maximum reduction gain of 1.45 is achieved. It can be seen that cells with high GoS gain do not show a power gain, since the GoS target has not been reached and thus the power is still at its maximum pmax. The complementarity to the GoS can also be seen in this case.Figure 9
Power gain per cell resulting from cell unloading withδ=δ(B).


## 5.1. Simulation Results

For the network scenario, the parameters have been used as described in Section4.4. The cells of the UMTS network are unloaded, if content is switched from the UMTS network to the broadcast network. The grade of service λi is increased, and the transmit power pi is decreased significantly.Figure6 shows the empirical cumulative distribution of the GoS of all cells in the network. Each curve represents a switching value for δ from zero to one in 0.1 steps and the broadcast bound δ(B)=0.36. It can be seen that the GoS increases for lower values of δ. For the full-loaded network (δ=1) about 31% of the cells are above the GoS target λ̂=0.98. By unloading the cells with δ=δ(B)=0.36 about 55% and for δ=0 about 89% of the cells meet the target threshold. The dotted lines represent the cases with δ<δ(B), in which the broadcast network is overloaded.Figure 6
Empirical CDF for grade of service depending on the switching parameterδ. Dotted curves represent the cases with the broadcast network in overload.In order to estimate, which UMTS cells benefit from load switching, the GoS gain is defined by the ratio of the unloaded cells(λi,δ=δ(B)(C)) to the fully loaded case (λi,δ=1(C)). Figure 7 shows the gain values, based on the best server cell structure. A high gain occurs for those cells, in which a high traffic demand exists (compare to Figure 1). For the cells with a gain equal to one, unloading has not been necessary due to a low traffic load. A maximum gain of about 75% increase in GoS can be seen in some selected cells, which benefit most of using a hybrid mode.Figure 7
Gain of grade of service for unloading the UMTS cells byδ=δ(B).We use (7) in order to calculate the grade of service of the entire network. For network planning the grade of service should be λ̂=98% or higher. In Figure 8 the performance of the hybrid network is shown. The GoS of the UMTS network λ̅(C) increases by unloading but does not meet λ̂ for an unloading till δ(B)(λδ=δ(B)(C)=92.6%). The UMTS bound δ(C) is not depicted since for δ=0 a GoS of λ̅(C)=97.9%<λ̂ is achieved. Since the δ(C)<δ(B) the maximum total GoS occurs for δ=δ(B) with λ̅(H)=95.5%.Figure 8
Grade of service of the entire network depending on the amount of load switching.If cells are unloaded and the GoS target is reached, a further unloading leads to a reduction of the necessary transmit cell power. In Figure9, the power reduction gain is shown per cell. A maximum reduction gain of 1.45 is achieved. It can be seen that cells with high GoS gain do not show a power gain, since the GoS target has not been reached and thus the power is still at its maximum pmax. The complementarity to the GoS can also be seen in this case.Figure 9
Power gain per cell resulting from cell unloading withδ=δ(B).


## 6. Conclusion

We have proposed a system and performance model for hybrid networks, which are composed of the cellular network UMTS and the mobile broadcast network DVB-H. The developed model is based on the existing interference coupling model of the UMTS network, which has been enhanced by the load switching concept. Furthermore, a broadcast system model has been developed. The two network performance indicators cell load and grade of service (GoS) have been defined; whereas the GoS parameter is used as the primary performance criteria for hybrid networks.Based on the developed models and the GoS definition, the switching bound concept has been been proposed, which define bounds for the load switching for each network separately. They determine thresholds for the necessary unloading of the UMTS network and for the maximum loading of the broadcast network in order to achieve the GoS target. These bounds are used to evaluate the conditions for reasonable load switching and define the necessity and feasibility of hybrid networks. The impact of load switching for the streaming multimedia service has been analyzed by a theoretical and a realistic scenario. We have shown the behavior of the cell load, the grade of service, and the appropriate gain values on a cell basis and for the entire network. The results have shown that a hybrid network can be applied to unload the UMTS network and to serve the multimedia services very efficiently.



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*Source: 329073-2010-03-04.xml*