On the economics of GPRS networks with Wi-Fi integration

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European Journal of Operational Research 187 (2008) 1459–1475 www.elsevier.com/locate/ejor

On the economics of GPRS networks with Wi-Fi integration

q

Saravut Yaiparoj, Fotios Harmantzis *, Vinoth Gunasekaran Stevens Institute of Technology, School of Technology Management, Telecommunications Management, Hoboken, NJ 07030, USA Available online 22 November 2006

Abstract Wi-Fi provides an appealing opportunity for GSM/GPRS operators to enhance their data capability. By integrating both networks, operators are able to provide 3G-like services. However, both networks have different data rates and capacity, which makes economics of the network integration and pricing of services a challenging issue. In this paper we introduce a novel pricing model for GPRS networks integrated with Wi-Fi networks. The model identifies how the integration can play a significant role in increasing operators’ overall revenue and potentially improve the performance of GPRS networks. We identify the optimal GPRS charging rate and Wi-Fi connection fee that yields maximum network revenue. In addition, we conduct a case study of a wireless operator that considers network integration, adopting our pricing model. The investment analysis provides the insightful information for profitable business cases of GPRS networks with Wi-Fi integration.  2006 Elsevier B.V. All rights reserved. Keywords: Economics; Pricing; Incentive Compatibility; Wireless Networks; Network Integration

1. Introduction q

Earlier versions of the pricing model were presented at: the ‘‘20th EURO Conference on Operational Research’’ that was held in Rhodes, Greece, on July 4–7, 2004, organized by the Association of European Operational Research Societies; the ‘‘4th New York Metro Area Networking’’ workshop, that was held in New York City, on September 10, 2004, co-sponsored by The City University of New York and Cisco Systems, Inc.; and the ‘‘2nd International Conference on e-Business and Telecommunication Networks’’ that was held in Reading, UK, on October 3–7, 2005. Earlier version on the case study was presented at the ‘‘31st Annual Conference of the Northeast Business and Economics Association’’, hosted at Yeshiva University, New York City, on September 26–27, 2004. * Corresponding author. Tel.: +1 201 216 8279; fax: +1 201 216 8084. E-mail address: [email protected] (F. Harmantzis).

As wireless technologies emerge and improve, the boundary of their applications becomes blur. Wireless technologies, which were once intended for specific applications, can now provide comparable services. The obvious examples are the General Packet Radio Service (GPRS) and the Wireless Local Area Networks (WLANs) (Lehr and Mcknight, 2003; Oliver and Poiraud, 2002). In recent WLAN developments, WLANs have improved towards larger coverage and better mobility of their data services (not to mention increased capacity which is significantly higher than that of cellular networks). They can now provide mobility features that were traditionally offered by cellular

0377-2217/$ - see front matter  2006 Elsevier B.V. All rights reserved. doi:10.1016/j.ejor.2006.09.025

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networks. Regarding cellular technologies, GPRS1 has become a significantly improved data service in terms of capacity, which was once a serious weakness of data services in cellular networks. Even though these two technologies are quite far from completely replacing each other, some major GSM/GPRS operators have realized the benefit of integrating these technologies. With their high capacity and low implementation cost, some operators consider WLANs the ideal candidate for expanding GPRS data capability (Doufexi et al., 2003). The integration of these technologies has a significant impact on their pricing structure. Pricing affects the allocation of the network resources in both technologies. With increasing user demand, engineers produce applications with considerable resource requirements. The focus in recent years has been towards real-time data services in the wireless environment. In a resource-limited wireless network architecture, such as GPRS, a growing user base will complicate the resource allocation problem. It is reasonable for GPRS networks to charge end users according to the availability of network resources. In addition, GPRS networks constantly experience congestion due to the limited resource architecture. An appropriate pricing scheme could help resolve the allocation of scarce resources to users, as well as generate additional revenue for network expansion. Regarding the user perspective on charges, users are inherently price-sensitive. By using pricing incentives, networks could send signals to the users that would influence their behavior and decisions towards more efficient network usage (Hou et al., 2002; Yaipairoj and Harmantzis, 2006). In addition, pricing can be used to discourage network users from accessing the network when it experiences congestion. Pricing thus could become an effective mean to perform traffic management and congestion control. The interplay of technologies and pricing has lead to much discussion on the design of network architecture for the integration of GPRS and WiFi networks, as well as the economics aspects of this new integrated network. By integrating networks, users would be given choices for their data transmis-

1

GPRS tutorial can be accessed in http://www.item.ntnu.no/ fag/tm8100/Pensumstoff2004/GPRS_Tutorial.pdf.

sion. The main factors that influence their network preferences are transmission rate, coverage and price. Even though these factors are crucial, transmission rate and coverage are highly subjective and depend heavily on user experiences. In order to quantify the effect of those factors, extensive surveys are needed. On the other hand, prices are more concrete. Network operators could set prices for network services that systematically influence user demand. In the case when network users are highly motivated by price, data traffic transmitted over the integrated networks depends heavily on the price charged in each particular network. In this paper, we propose a viable business case interlinked with our novel pricing model for the integration of GPRS and Wi-Fi networks. We believe that pricing plays a crucial role in the next generation of integrated networks. Our pricing model is considering a demand function, which determines the portion of network usage in the particular network. Since network users are heavily motivated by prices, the pricing model allows us to understand the effect of prices charged in the integrated network in terms of overall revenue. By taking investment analysis into account, we can determine the appropriate user charges and the net profit by deploying an integrated network. Our main aim is to bring insight to real world problems faced by the cellular industry. Accordingly, the case study focuses on up-to-date investment issues of the industry.2 Since there is a price war going on in the wireless arena due to heavy competition, we have performed sensitivity analyses which can help managers to make decisions regarding the investment strategy in alternative technologies. This paper is structured as follows. In Section 2, we provide background on different Wi-Fi deployment methods and an architectural overview of Wi-Fi integrated with GPRS as well as the pricing structure of these technologies. In Section 3, we propose our pricing model for a GPRS network integrated with Wi-Fi. We then present numerical results of the pricing model in Section 4. In Section 5 we analyze a case study using investment and cost analysis. We draw our conclusions in Section 6.

2

Assumptions are realistic for year 2004, when most of this research was conducted.

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2. Background 2.1. Different types of Wi-Fi deployment There are various types of Wi-Fi3 deployments which can be summarized as follows: The first type is the deployment at home and some businesses where the service is offered without charge. However, the user theoretically could open up his Wi-Fi router generating revenue (Mussachio and Walrand, 2004). The second type is offered by micro carriers, e.g., Starbucks coffee-shops and Border bookstores in the United States, where they set up their own access points and maintain customer-billing relationship with subscribers (Friedman and Parkes, 2003). Though revenues are not high, the model is still profitable for small business owners (Camponovo et al., 2003). The third type is offered by the Wi-Fi startups,4 e.g., Boingo and Wayport. These Wi-Fi operators aggregate the networks provided by micro carriers and provide a single access to the end user. The fourth type applies to Wi-Fi services offered by cellular operators (which is our recommendation in this paper). The cellular operators can partner with micro-carriers or aggregators or they can implement their own Wi-Fi networks. A profitable business strategy for cellular operators would be to provide their own Wi-Fi networks integrated with the already existing cellular networks. Cellular networks integrated with Wi-Fi have advantages over Wi-Fi deployed by micro carriers and aggregators, since cellular operators already have an established customer base to which they can integrate Wi-Fi and offer 3G-like services (Ahmavaara et al., 2003; Oliver and Poiraud, 2002). Apart from that, cellular operators have the advantage of using the existing OSS (Operational Support System) and the BSS (Business Support

www.wi-fi.org (URL accessed on September 2005) Wi-Fi, or Wireless Fidelity, allows connectivity to the Internet from virtually anywhere at speeds of up to 54 Mbps. Wi-Fi-enabled computers and handsets use radio technologies based on the IEEE 802.11 standard to send and receive data anywhere within the range of a base station. The Wi-Fi Alliance, formerly known as WECA, is a global Wi-Fi organization that created the Wi-Fi brand. A nonprofit organization, the Alliance was formed in 1999 to certify interoperability of IEEE 802.11 products and to promote them as the global, wireless LAN standard across all market segments. The Wi-Fi Alliance comprises of over 200 members from the world’s leading companies. These companies offer over 1500 Wi-Fi certified products. 4 www.boingo.com and www.wayport.com. 3

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System) for the deployment of the new Wi-Fi systems. Cellular operators could provide a common bill to customers in the integrated network, attracting their subscribers to experience the benefits of Wi-Fi connectivity. Although the users could pay separately to use the Wi-Fi services, the operators hope that bundled service promotions will create more demand for their regular service and reduce churn. Certain cellular operators have already provided price bundling5 as an option to their subscribers. If integrated networks are deployed, then cellular operators can realize additional revenue from the existing data users by offloading certain portion of the cellular traffic-GPRS/EDGE traffic in this case – to the hotspots. The effect of integration on the revenue stream will be further discussed in Section 3. 2.2. Architectural overview of integration: Wi-Fi with GPRS Integrating GPRS and Wi-Fi offers both ubiquitous coverage and supports high data rates in strategic locations (campuses, office, airports, hotels, coffee shops etc.). When Wi-Fi and GPRS networks are integrated, the cellular operators are able to meet some of the requirements for 3G services. This would allow them to provide high quality data services which can be perceived as 3G-like services. The following are two types of network architectures that are proposed for network integration of GPRS and Wi-Fi: tight coupling architecture and loose coupling architecture. 2.2.1. Tight coupling architecture In tight coupling architecture, a Wi-Fi network is connected to a GPRS network as an alternative Radio Access Network (Ahmavaara et al., 2003). It is connected directly to the operator’s core network as illustrated in Fig. 1. The hotspot can reuse the GPRS infrastructure, such as core network resources, subscriber databases and billing systems. The mobile users can either select their network preferences or choose to get connected at the best available network speed. This process, which is all performed in software, will automatically connect the users to the network of their choice.

5

www.t-mobile.com.

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Fig. 1. Tight coupling architecture of Wi-Fi with GPRS.

Fig. 2. Loose coupling architecture of Wi-Fi with GPRS.

2.2.2. Loose coupling architecture In loose coupling architecture, the hotspots can be coupled with the GPRS network through the operator’s IP network (see Fig. 2). The Wi-Fi data traffic goes directly to the operator’s IP network, instead of going through the GPRS core network. Though the Wi-Fi and cellular networks remain separate, there is a common platform for authentication, accounting and authorization for users to access the network. The hotspots may be owned by any third-party carriers, with roaming enabled via a dedicated connection (between the cellular operators and Wi-Fi providers) or over an existing Internet connection. 2.3. Pricing structures of GPRS and Wi-Fi GPRS is a packet-based data service offered over an existing GSM network, which promises to provide instant connection of data services. With a theoretical maximum speed of up to 171.2 kbps – in contrast to 9.6 kbps of circuit-switched data connection over GSM network-GPRS seems to be the ideal solution for mobile operators to provide wireless Internet services such as Web browsing, FTP, multimedia, etc. (Yang et al., 2003). However, in reality, the achievable transmission rate of GPRS is a lot

lower than expected (approximately 50 kbps). When the system experiences high volume of aggregated traffic, the performance of a GPRS system is seriously suffered from its best-effort architecture, causing reduction in transmission rate. Due to its limited capacity and cost structure, data transmission over GPRS networks is charged based on simple usagebased pricing, which is relatively high compared to the charge of data transmission over most fixed wireless and wired networks (i.e. approximately $5 to $10 per megabyte.6) However, certain GPRS operators also offer pricing plans based on flat-rate pricing. The flat-rate pricing plans are deployed by the network operators largely because of the business concern to fight customer churn. Though this type of pricing plans are attractive for customers due to their simple billing and accounting system; flat rate pricing plans do not provide economic incentives to influence efficient network usage, contributing to increasing congestion in the GPRS networks. On the other hand, Wi-Fi has become both a competitive and complementary technology in relation to cellular technology (Ahmavaara et al., 2003; Alleman, 2002; Doufexi et al., 2003; Salkintzis et al., 2002). It offers fast connectivity and relatively much cheaper services due to the lower cost of Wi-Fi access points and Wi-Fi compatible devices. Wi-Fi service providers offer several payment options, such as a monthly subscription fee, a one-time charge per connection, or usage-based pricing. Pricing schemes vary among different service providers. Since the network resource of Wi-Fi networks is relatively high, most Wi-Fi charges are based on flat pricing, such as a one-time connection fee, which is usually a service charge per single connection for a user in one location7 (Camponovo et al., 2003). In addition, Wi-Fi networks have recently started offering a fairly popular connection charge that allows users to have unlimited connections at a certain location for 24 h. As described above, data access over Wi-Fi is cost effective compared to cellular networks. In fact, a cellular base station costs over 30 times more than a Wi-Fi hotspot (Oliver and Poiraud, 2002). By integrating these technologies, network operators can enjoy significant cost saving while offering broadband wireless services that are comparable to 3G,

6 7

www.cingular.com (URL accessed on Nov. 2004). www.boingo.com.

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namely, 3G-like services. With the integration of both networks, network users are able to choose alternate high bandwidth Wi-Fi networks, which offer cost-effective services and high transmission rate, but limited coverage. 3. Proposed pricing model Let us assume that the user is initially in GPRS coverage and she is ready to perform a large file transfer. The user has two options: she can either perform the file transfer right away over the GPRS network paying a GPRS charge, or she can search for Wi-Fi hotspots, assuming she occasionally crosses Wi-Fi networks. If the user decides to search for Wi-Fi hotspots, she could program her mobile terminal to detect Wi-Fi hot spots and perform file transfer whenever she is in the coverage. There is one common Accounting, Authentication, and Authorization (AAA) server and billing system maintained by the cellular operators for all integrated hotspots. Once users are in hotspot coverage, they are charged a connection fee. The connection fee can be determined from Wi-Fi operators based on certain criteria, i.e., users could be charged for the actual number of connections that they make at a certain location, or they could be charged a single fee that allows them to have unlimited number of connections for a certain period of time. In our scenario, the connection fee is referred to the latter case. Furthermore, different types of hotspots would determine if users need to pay a roaming fee (third party hotspot case) or not (operator-owned hotspot case). We disregard the roaming fee at this point since that fee is not significant enough to contribute to the Wi-Fi charges when compared to charges from GPRS services. Fig. 3 illustrates a blocking diagram for the scenario we just described. As mentioned earlier, the cost of transmitting data over a GPRS network is higher than that of a Wi-Fi network, especially when the data volume is large. The significant price difference between the two networks can influence the way users use these networks. Some users may be willing to search for a Wi-Fi network, if they need to perform a large file transfer. Hence, price incentive can influence users to use either GPRS services with high charging rate or Wi-Fi services with a connection fee. We argue that the user demand represented by the certain percentage of mobile users (D), who would transfer their data over the GPRS integrated networks, depends heavily on the ratio between the

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User in GPRS Coverage Area

Is the user willing to pay GPRS price Pg

No

Yes

GPRS Price Pg

Delay

User in Wi-Fi Coverage Area

Wi-Fi Price Pw

Common Billing

Fig. 3. Pricing Block for GPRS networks with Wi-Fi integration.

charges of these two networks (Hou et al., 2002). The user demand (D) can be mostly influenced by the demand function which is a function that characterizes the reaction of users to changes in price. In this paper we use the demand function that appears in (Fishburn and Oldyzko, 1998) since it is used for different classes of users, which fit our model. The demand function is as follows: pg

Dint ¼ e½pw 1

2

0 6 Dint 6 1;

pg > pw ;

ð1Þ

where pg is the GPRS charge and pw is the Wi-Fi connection charge for each user (pw could be either operator owned or third party owned hotspot charge). We are interested in the case when price incentives can influence users’ decision. The session volume charged by the GPRS network must be large enough to allow pg to be greater than pw. GPRS pricing is based on usage-based charges, which do not depend on holding time but on session volume. Therefore, pg is a linear function of the number of megabytes transmitted over the network. Regarding the Wi-Fi pricing scheme, the charge is based on flat pricing, which is basically a connection fee. Therefore, pg and pw can be described as follows: pg ¼ rg  v; pw ¼ rw ;

ð2Þ

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where rg is the charging rate per megabyte for the GPRS network, rw is the connection fee of the Wi-Fi network (in US dollars in this paper), and v is the session volume of user data that is transmitted over the GPRS network (in megabytes (MB)). The lower curve in Fig. 4 illustrates the demand function in (1). The horizontal axis represents the price ratio between GPRS and Wi-Fi charges ((pg/ pw)  1). This function works quite well in our model which can be explained as follows: First, the demand function starts high for small price ratios, representing the situation where users have small volume of data to transmit. The price charged by GPRS networks at that point is not much different from the Wi-Fi charge. The users have little incentive to seek Wi-Fi hotspots, resulting in high demand for GPRS usage. Then, the demand decreases rapidly as the curve gets into a mid-range and has very narrow tail. This part of the curve represents the increase in GPRS charges due to the increasing session volume from users. There is enough incentive for some users to start migrating to Wi-Fi, resulting in reduction of GPRS usage. For example, when the price ratio at the horizontal axis equals to one, the GPRS charge is double the Wi-Fi charge, resulting in a significant drop of GPRS demand of about 36%.

Revenue due to the integration can be determined by the weighted sum of the revenue created by the GPRS network and Wi-Fi network respectively, based on their corresponding demand. Therefore, from (2), the average revenue of the integrated network can be determined as follows: Rint ¼ Dint  ðrg  vÞ þ ð1  Dint Þ  rw :

ð3Þ

Regarding the revenue gained from GPRS network without Wi-Fi integration, the GPRS users do not have an alternative to migrate their traffic. Therefore, the demand of users using non-integrated GPRS networks would be higher than in (1). Since the distribution of session volume in GPRS networks is not available to us, we have to produce a meaningful demand function for this particular scenario. This function needs to be well above the demand function in (1), to represent the higher demand of GPRS networks in the absence of Wi-Fi networks. The demand function can be shown as follows (Fishburn and Oldyzko, 1998) Dw:o:int ¼

1þ

1 p g K

1

4

0 6 Dw:o:int 6 1;

ð4Þ

where K is a constant. In our case, we set K equal to pw for the purpose of fitting our demand curve according to our assumptions. We do not imply that

1 GPRS with Wi-Fi integration GPRS without Wi-Fi integration

User demand of GPRS networks (D)

0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

0

0.5

1

1.5

2

2.5

3

Price ratio between GPRS and Wi-Fi ((Pg/Pw)-1)

Fig. 4. Demand functions of (a) GPRS networks with Wi-Fi integration and (b) GPRS networks without Wi-Fi integration (with K = pw) (The demand function for GPRS without Wi-Fi integration is plotted based on the assumption that the function has to be over the demand function of the GPRS with Wi-Fi integration due to the lack of alternative networks).

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Dw.o.int depends on the Wi-Fi charge. The demand function represents only the percentage of data users who are willing to pay the GPRS charge (pg). The rest of the users simply do not use the service. By comparing the revenue generated in both cases, we can gain insight regarding the effect of integration on the revenue stream. Fig. 4 illustrates both demand functions for the integrated networks and non-integrated networks. Based on the user demand for a non-integrated GPRS network, the revenue gained from GPRS network without integration is as follows: Rw:o:int ¼ Dw:o:int  ðrg  vÞ:

ð5Þ

Hence, the additional (incremental or extra) revenue gained from the integrated networks is basically the difference between (3) and (5), i.e. Rextra ¼ Rint  Rw:o:int ¼ ðDint  Dw:o:int Þ  rg  v þ ð1  Dint Þ  rw :

ð6Þ

In terms of traffic offload from GPRS networks with Wi-Fi integration, we focus on the portion of users’ demand that we can migrate to the Wi-Fi network. Each GPRS user generates a certain amount of session volume based on her preference. By assuming an average session volume from data users, we are able to find the prices for both GPRS and Wi-Fi networks that attract users and reduce their demand down to certain target demand Dt. To be specific, for certain average session volume (v), we are able to set up an optimization model to find the optimal rg and rw based on a target demand D t. We first set up the objective function for our optimization problem. The most suitable objective function would be the maximization of (3), which represents the revenue from the integrated networks. The objective function is subjected to certain constraints on rg, rw and Dt. Constraints for rg and rw are based on competitive market prices. Regarding the target demand Dt, this represents the remaining user demand in GPRS networks required by the network operator post integration. The target demand Dt basically implies the operating point of the GPRS network. For a certain Dt(rg, rw), the operator maximizes revenue, calculating the charging rates rg and rw. Hence, from (1)–(3), we can set up the objective function and its constraints as follows:

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f ðrg ; rw Þ ¼ MaxDt ðRint Þ ¼ MaxDt ðDt  ðrg  vÞ þ ð1  Dt Þ  rw Þ;

ð7Þ

subject to the following constraints: a 6 rg 6 b; c 6 rw 6 d; 2

rg v Dt ¼ e½ rw 1 ; v ¼ constant;

where [a, b] and [c, d] are the constraint bounds for rg and rw respectively and Dt is the target demand of the GPRS Network. Regarding the data volume (v), typically, distribution of the data volume (v) in GPRS networks integrated with Wi-Fi is needed to determine the optimal charges in both networks. Kilpi (2003) illustrates cumulative session volume of a large number of GPRS sessions, giving an overview of typical GPRS sessions. The result shows that the session volume for very ‘‘big’’ sessions of GPRS services is not more than 5 MB, due to slow user speeds and structural delays of GPRS networks. Therefore, we consider sessions less than 5 MB as typical in the investigation of our optimization model. Regarding the users who transfer more than 5 MB, we assume that they are highly likely to migrate their traffic to Wi-Fi networks. Since larger sessions from network users are migrated to Wi-Fi networks, we expect that the performance of GPRS networks will improve significantly (However, the performance improvement rigorous proof is beyond the scope of this paper.). 4. Numerical results In this section, we present numerical results based on the proposed pricing model. We illustrate the revenue gained from network integration and how the change in the Wi-Fi connection fee affects the average revenue of the integrated network. Then, we investigate the optimal GPRS charging rate and Wi-Fi connection fee, which influence GPRS users to migrate traffic load to Wi-Fi networks yielding maximum revenue. 4.1. Assumptions and parameters We consider the case where users want to transmit fairly large files. The cost of a large data volume creates pricing incentives to seek and transmit traffic onto less expensive Wi-Fi networks.

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Average Revenue per user ($/user/connection)

6

5

4

3 Integrated GPRS network Non-integrated GPRS network

2

rw is $4 per 24 hr. connection rg is $6 per MB 1

0 0.5

1

1.5

2 2.5 3 3.5 Usage (MB/connection)

4

4.5

5

Fig. 5. Revenue per user from GPRS networks with and without Wi-Fi integration, as a function of data usage. As data usage increases, the revenue for the integrated networks approaches the Wi-Fi connection fee, i.e. $4, due to the migration to the less expensive Wi-Fi network. For example, when the (average) data volume is 3 MB, the additional revenue due to integration is $3.8.

Smaller data volume (<500 kb) will not create enough incentive for users to look for hotspots. We assume that pricing in GPRS networks is usage-based at a rate of $6 per megabyte8 (fixed in this paper). The charge at the Wi-Fi hotspots is flat, where users are charged per connection. Since a Wi-Fi connection lasts for 24 h, users can transmit or receive as much traffic as they want during a connection session. In addition, we assume that users have access to Wi-Fi hotspots, when they seek for them. The only incentive that drives them to hotspots is pricing. We do not include the incentives based on coverage or location of hotspots into our study. 4.2. Effect of charges on revenue and traffic migration of the integrated network Fig. 5 shows the revenue gained from the integration of networks determined by (3), as a function of the average session volume. The Wi-Fi connection fee is $4 per connection (a typical market price for a mobile user), regardless of network usage. We can see that when usage increases, users are influ-

8

http://www.mobileinfo.com/3G/pricingplans.htm (URL accessed on September 2004) and www.cingular.com.

enced by price incentives to transmit their traffic over Wi-Fi hotspots, resulting in additional network revenue as determined by (6). However, the revenue starts dropping when average data volume is equal to 1.2 MB, since a large number of users migrate to inexpensive Wi-Fi hotspots, causing reduction in revenue. For example, when the average data volume is equal to 3 MB, we can gain additional revenue up to $3.80 (The example will be used later in the case study.). Fig. 6 shows the average revenue gained from integrated GPRS networks and non-integrated GPRS networks as a function of the Wi-Fi connection fee (pw). The curves illustrate the effect of Wi-Fi connection fee on the revenue for both cases. We assume that each user transmits an average of 3 MB per connection. The result shows that as pw increases, the revenue generated from non-integrated GPRS networks is closer to that of integrated GPRS networks; network users would continue using the GPRS network due to the high Wi-Fi connection fee; there is no significant price incentive for them to migrate their traffic to Wi-Fi. According to Fig. 6, we can identify the Wi-Fi connection fee (pw) that yields the largest difference between those two revenue curves (maximum value of Rextra). Fig. 7 illustrates the extra (additional) revenue as shown in (6) versus pw and v in a three-

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Fig. 6. Revenue gained from the GPRS network with and without integration, as a function of the Wi-Fi connection fee. As the Wi-Fi connection fee increases, the two curves are getting closer, i.e. there is only little incentive for users to migrate the Wi-Fi networks.

Fig. 7. Additional (extra) Revenue of GPRS due to integration. The plot shows the incremental revenue, i.e. the difference of revenue between a GPRS network with and without Wi-Fi integration. The contour of the three dimensional plane represents the region, which yields the largest difference in revenue and their corresponding Wi-Fi connection fee (pw) and data volume (v).

dimensional plane. The contour of the plane shows the region (the peak region of the plane), which

yields the largest difference in revenue and their corresponding pw and v. For example, when v = 3 MB,

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the maximum value of incremental revenue (Rextra) is achieved at pw = $6.5. To offload the traffic from GPRS networks to Wi-Fi, the GPRS charging rate (rg) and the Wi-Fi connection fee (rw) must be set appropriately so that

the system yields maximum revenue and encourages GPRS users to migrate to Wi-Fi networks. Based on our assumption of the demand function and the proposed optimization model, Fig. 8 illustrates charging rates for GPRS and Wi-Fi networks as

Charging rate or Revenue per connection ($)

8 GPRS charging rate (rg) 7.5

Wi-fi Connection fee (rw) Revenue (Rint)

7 6.5

Session Volume = 2MB

6 5.5 5 4.5 4 3.5 3 0.1

0.2

0.3

0.4 0.5 0.6 User Demand (D)

0.7

0.8

0.9

Charging rate or Revenue per connection ($)

16

14

12

10

8 Session Volume = 5MB GPRS charging rate (rg)

6

Wi-Fi Connection fee (rw) Revenue (Rint)

4

2 0.1

0.2

0.3

0.4 0.5 0.6 User Demand (D)

0.7

0.8

0.9

Fig. 8. Revenue (in US dollars) generated for optimal charging sets (GPRS charging rate and Wi-Fi connection fee) (calculated via optimization), as a function of user demand for two cases (a) Session volume = 2 MB and (b) Session volume = 5 MB. The constraints used in the optimization is as follows: 1 6 rg 6 4 and 3 6 rw 6 10, based on prevailing market prices.

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well as the revenue generated from those charges. The constraints used in the optimization are as follows: 1 6 rg 6 4 and 3 6 rw 6 10. The constraints are according to the pricing information from commercial GPRS operators and Wi-Fi aggregators. We can see that, when the average session volume is equal to 2 MB, the optimal value of rg is equal to $4 per MB for any target user demand (Dt). The maximum revenue generated from integrated networks is driven by the Wi-Fi connection fee (rw) as shown in Fig. 8a. For larger session volume (i.e., 5 MB), when D is equal to 0.5, the connection fee (rw) reaches the maximum market price ($10 per connection). The optimization model suggests that the GPRS operator should reduce the GPRS charge (rg) in order to meet certain user demand and maximum revenue. Hence, based on our assumption and the optimization model, we can obtain sets of charging rates for both GPRS and Wi-Fi networks which yield maximum revenue. The results from pricing analysis show that, for certain users with relatively high data volume, significant revenue can be obtained by offering Wi-Fi services as an alternative for enhancing data capability of GPRS networks. The additional revenue of the integrated network depends heavily on the Wi-Fi connection fee. We have shown the effect of the Wi-Fi connection fee on the overall revenue of the integrated network. However, revenue can answer only half of the questions in the decision for deploying integrated networks. An investment analysis is needed to determine the actual profit from network integration. In the next section, we apply the proposed pricing model in a case study. Specifically, the additional revenue from the pricing model will be used in the cash flow analysis. We will determine the cost of implementing the integrated networks according to certain assumptions. Then, certain investment decision parameters, such as net present value, will be calculated for the economic justification of the investment of integrated networks. 5. Case study We consider for analysis an area of 23 square miles, which is roughly equal to the size of Manhattan (analysis can be generalized to any other city where the operator offers cellular services). We assume that the subscriber base in that geographic area is 120 000 users, for both voice and data. We also assume an annual growth rate of 6% for the

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customer base for the next four years, a projection which is typical for the US market. Currently, the operator provides voice and data service through its existing GSM/GPRS network. Since there is a growing demand for broadband access, the operator can consider deploying a Wi-Fi network, i.e., integrate the GSM/GPRS with Wi-Fi, instead of deploying 3G networks. The company wants to complete the integration of networks within the next four years; this is the life of the project. Since it is not necessary to cover the entire city with Access Points (APs) to provide Wi-Fi services, APs can be deployed in locations where groups of people need high-speed wireless access, e.g., coffeehouses, malls, subways, university campuses, airports, convention centers, and other strategic locations. Each access point in a WLAN system forms a Basic Service Set (BSS); two or more access points form an Extended Service Set (ESS). The access points in the ESS are connected by distribution systems. Therefore, there will be one ESS serving each GSM/GPRS site. A typical urban cell site covers a radius of 1.212 square miles (approximately). A Wi-Fi access point covers 0.0102 square miles (approximately). The company currently operates 19 GSM/GPRS cell sites in the considered geographic area, to serve its existing subscribers. They will be expanding their GSM network in the proceeding years, as they anticipate an increase of their customer base. At the end of the project’s life, they target to provide WLAN coverage equivalent to 1/5th of the cell site coverage area of 23 square miles. They are planning to expand their network gradually, year by year. The first year will be covering 1/8th of the whole coverage area, the second 1/7th and so on. Table 1 shows the annual deployment for both GSM cells and WLAN APs, i.e., hotspots. Details about the calculations can be found in Appendix A. Currently, out of the 120,000 subscribers, 85% are voice users and the rest 15% are data users (the breakdown is typical for US wireless operators.9) It is very difficult for the operator to make all the existing data subscribers to migrate to the integrated network’s service (at least in the very near future, due to high customer acquisition cost). However, according to industry forecasts, by the year 2007, the number of Wi-Fi users in the US will

9

www.attwireless.com (Cingular Wireless after merger).

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Table 1 Number of GSM cell sites and hotspots per year

Table 2 Cash flow breakdown

Year

1

2

3

4

GSM cells Hotspots

19a 281

20 322

21 376

22 451

Operator’s objective to provide WLAN coverage equivalent to 1/ 5th of the cell site coverage area of 23 square miles, at the end of the forth year. a Existing GSM/GPRS cells.

almost equal or surpass 2.5G/3G data users.10 For our case, we have assumed that 10% of the data service users will be using the integrated service, i.e. 1.5% of the total customer base for the first year.

Capital expenditure

Wi-Fi AP’s cost Integration cost to GSM/GPRS cells

Operating expenditure

Backhaul Maintenance Depreciation cost

Revenue due to Wi-Fi deployment

Revenue from data subscribers Revenue from Wi-Fi only service

assumed an additional $5000 for integrating an Extended Service Set to the GSM/GPRS cell sites. Figures are shown in Table 3.

5.1. Cash flow breakdown In order to calculate the cash flows for the project, we consider the following: (Additional, i.e. due to integration) Capital Expenditure (CapEx) and (Additional) Operating Expenditure (OpEx) in the cost side, and total Average Revenue Per User (ARPU) due to the integrated service and the WiFi only service. We ignore taxes and interest expenses here. For the CapEx calculation, we consider the investment cost of the Wi-Fi system: cost for deployment of Access Points and the integration cost to GSM/GPRS cells. The additional cost for integration, may be due to tight coupling or loose coupling integration. Backhaul cost, maintenance and equipment depreciation cost are included in the OpEx calculation. The revenue considered comes only from the data users who are using the integrated service, i.e. both GPRS and Wi-Fi. Table 2 summarizes the cash flows considered in the paper, while Appendix B provides more detailed calculations. 5.1.1. Capital expenditure The capital expenditure for a GSM/GPRS cell site is very high, compared to a WLAN access point. A GSM base station costs up to $150,000/site, with an additional Coax/Antenna cost of $50,000. While a new cell site construction will cost $150,000, WLAN equipment and access point will cost only $500–$1000/site. The additional investment cost due to Wi-Fi deployment is minimal, compared to the company’s typical projects. In the paper, we 10

http://www.pyramidresearch.com/ (URL accessed on Oct 2004).

5.1.2. Operational expenditure From Table 4, we see that the operating expenses of the WLAN systems are significantly higher than the CapEx figures. Hotspots, regardless of the shape, size, or business model, need a backhaul connection to transmit data traffic to the Internet. Hotspot systems support a variety of backhaul methods that include business class DSL, cable, T1, etc. For hotspot deployment to be profitable, the operators should find a balance between backhaul performance and cost. For small hotspots, operators can Table 3 Capital Expenditure (CapEx) per year Year

1

2

3

4

Total GSM cells Total hotspots CapEx

19 281 $235,000

20 322 $25,500

21 376 $32,000

22 451 $42,500

Table shows total numbers of GSM/GPRS cells and Wi-Fi hotspots at the end of each year. Expenses are based on incremental, i.e. new cells and spots. The capital expenditure for a cellular system is very high compared to a WLAN access point. Therefore, the first year operator experiences significant cash out-flow.

Table 4 Operating Expenditure (OpEx) per year Year Total GSM cells Total hotspots OpEx

1

2

3

4

19

20

21

22

281

322

376

451

$893,538

$1,023,960

$1,195,680

$1,434,180

Table shows total numbers of GSM/GPRS cells and Wi-Fi hotspots at the end of each year. Expenses based on incremental, i.e. new cells and spots. Depreciation cost of the WLAN equipment is also included in the OpEx figures.

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go for a business class DSL that will cost them approximately $100 per month, instead of going for T1, so they can save some money in the operating cost. After all, wireless networks require less maintenance than the wired ones. The maintenance and depreciation cost of the WLAN equipment is estimated at $50–$70 per month. 5.1.3. Average revenue per hotspot The operator has two sources of revenue coming from each Wi-Fi hotspot: (a) users of the integrated service, and (b) users of the Wi-Fi service only. For the integrated service we consider only the additional revenue from the mobile data users, to match the revenue side to the cost side. We apply our proposed pricing model from Section 3 to the case study. We assume that the Wi-Fi connection fee (rw) equals $4 per connection and the GPRS charging rate (rg) equals $6 per MB. For example, if each data user transmits on average 3 MB data volume through a 24 h connection then from Fig. 5, additional revenue of $3.8 would be obtained from each customer. Let us assume that, on average, each user uses the Wi-Fi network at least twelve days per month, which is simply 12 connections per month, i.e. once every two business days (this is a parameter that will vary later in the analysis). This assumption results in an additional monthly revenue of $3.8 * 12 = $46. Additional revenue comes from the Wi-Fi only service.11 The service allows users to access only Wi-Fi networks. The users do not take advantage of integrated networks, therefore, discount from the price bundling cannot be applied to this type of users. Hence, the Wi-Fi connection fee is higher than that of the service in the integrated network. Let us assume that the charge for Wi-Fi only user is as high as $8 per connection. Assuming 30 connections per month per location12 (i.e. one connection per day), the service provider can generate $8 * 30 = $240 per month from each hotspot location. The GPRS charge is usage based: the user pays $6 for each megabyte of transmission. But with a Wi-Fi connection fee (rw) of just $4 in the integrated service model, flat per day, users can transmit 11

http://www.boingo.com/wi-fi_industry_basics3_3.html (URL accessed on April 2005). 12 Our assumption is conservative as we assume only 30 connection per month per location. According to the Wi-Fi industry, there are at least 90 connections per month in a given hotspot.

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unlimited volume of traffic. By using Wi-Fi, users can connect to any hotspot and gain the most value out of the money they pay. If they are outside of the Wi-Fi coverage, they have to pay an extra 6$ for every megabyte. We have calculated the total additional revenue due to integration and Wi-Fi only service and presented the results in Tables 5a and 5b. Table 6 shows the net profit for each year in the project life. Table 5a Additional revenue due to integration per year Year

1

2

3

4

Users of integrated service Total additional revenue

1800

1908

2023

2144

$984,960

$1,044,058

$1,106,986

$1,173,197

Modest growth is assumed for the number of users of the integrated service. Each user uses the Wi-Fi network once every two business days for the next four years, i.e., 12 connections per month. The connection charge is $3.8, according to our pricing model (constant for the next four years) (see Fig. 5 for assumptions on that charge calculation)

Table 5b Revenue due to Wi-Fi only service (in hotspots) per year Year

1

2

3

4

Number of hotspots Number of connections

281

322

376

451

101,160

115,920

135,360

162,360

$809,280

$927,360

$1,082,880

$1,298,880

Total Wi-Fi revenue

For the Wi-Fi only service, we assume $8 charge per connection, and 30 of those connections per month per hotspot location, i.e. one connection per day. Assumptions kept constant for the life time of the project.

Table 6 Net profit (loss) for operator per year Year

1

2

3

4

Total revenue $1,794,240 $1,971,418 $2,189,866 $2 ,472, 077 (Integrated and Wi-Fi only) Total cost $1,129,080 $1,049,460 $1,227,680 $1,476,680 (CapEx and OpEx) Net profit $665,160 $921,958 $962,186 $995,397 Project is profitable for each year (EBIT figures; we ignore taxes and interest expenses here).

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To achieve a better Return on Investment (ROI) for the WLAN deployment, cellular operators can add extra customers for Wi-Fi only service to their already existing subscriber base. Wi-Fi bundled with GPRS price may well attract subscribers from other operators and help reduce the churn. 5.2. Base case and sensitivity analysis Discount Cash Flow (DCF) analysis provides the net profit or loss of an investment in present value terms, assuming that future cash flows are known and discounted at a risk-adjusted factor, e.g., weighted average cost of capital (WACC) (Domodaran, 2002). The Net Present Value (NPV) of the project in our case study is given by 4 X NPV ¼ F n ðP =F ; i%; N Þ; ð8Þ N ¼1

where Fn is the net profit, i.e. revenue minus cost, at the end of Nth period, and i is the discount rate per period. The discount rate remains constant during the life of the project and there is no salvage value. The project should be started if NPV > 0 i.e., project has a positive net present value and should be abandoned if NPV < 0. For the NPV calculations, we assume a WACC of 10%, which corresponds to the average company for the US wireless industry.13 For the base case analysis we assumed that: (a) the average number of monthly connections per user in the integrated service would be 12; and (b) the number of connections in the WiFi only service would be 30 per month per hotspot. Since these two variables are key to cost/benefit analysis, we performed sensitivity analysis to show by how much the ARPU, and therefore the NPV, will be affected. The cash flow estimates are based only on the additional capital expenditure and operating expenditure due to WLAN deployment. From Table 6, we see that operating profit increases in the beginning year; however, it increases with a slower rate after the second year due to increased operational expenditures. Based on the WACC of 10%, we find the project as a whole has a net present value of $2.79 million. In the present case we consider only 23 square miles area for integration. We have used WACC as the risk-adjusted factor for discounting the investment, since the capital allocated to Wi-Fi 13

http://www.boozallen.de/content/downloads/Prepaid_Wireless_US.pdf.

hotspots is very little compared to the investment in 2.5G or 3G infrastructure and spectrum, which are in billions of dollars. Moreover, Wi-Fi investment is part of the service provider’s portfolio and it has a strategic value. We consider the risk-adjusted factor as a variable and perform sensitivity analysis. As shown in Table 7, even if we use a risk adjusted factor of 30% or 40% for discounting cash flows, which is comparable to the required rate of return for individual projects or Venture Capital funded investments, the investment in Wi-Fi is justifiable, i.e., positive NPV. There are a lot of new players in the market. This leads to high uncertainty regarding who will survive and who will exit the market. Additionally, there are many places, e.g., hotels and cafe´’s, offering Wi-Fi free of charge. Therefore, it is likely that there will be a severe price war among the Wi-Fi service providers. This will affect the average number of connection per user and the total revenue. Table 8 shows how the present worth of the project is affected if we change the average connections per user in the base case scenario for the integrated service, all other variables constant. For the integrated Table 7 Effect of (risk-adjusted) discounting factor to the Net Present Value (NPV) of the project Weighted Average Cost of Capital (%)

Net Present Value (in millions)

10% 20% 30% 40% 50%

$2.77 $2.23 $1.84 $1.56 $1.33

The investment is justifiable even with higher risk-adjusted discounting factors. Table 8 Effect of number of (monthly) connections per user of integrated service to the Net Present Value (NPV) of the project (Average) Number of Wi-Fi connections in integrated service

Net Present Value (in millions)

2 3 4 6 8 10 12

$0.06 $0.23 $0.51 $1.07 $1.64 $2.20 $2.77

The operator needs only 3 connections from each user per month in the integrated service so that the project has a positive NPV, even without considering any revenue from the Wi-Fi only service.

S. Yaiparoj et al. / European Journal of Operational Research 187 (2008) 1459–1475 Table 9 Effect of number of (monthly) connections per hotspot with WiFi only service to the Net Present Value of the project (Average) Number of Wi-Fi connections per location

Net Present Value (in millions)

3 4 5 10 15 20 25 30

$0.11 $0.01 $0.10 $0.63 $1.17 $1.70 $2.24 $2.77

Even without considering the Wi-Fi revenue that comes from the integrated service, the operator needs only 4 connections in a single hotspot location per month to get the positive NPV for the project.

service the operator needs at least 3 monthly connections per subscriber to have a positive NPV. Apart from the average number of connections per user in the integrated service model, the other key variable is the number of connections in the Wi-Fi only service. This also plays an important role in justifying the investment in Wi-Fi systems. The pricing for Wi-Fi only service is $8 per connection, which is double that of the connection fee compared to the integrated service model. The discount due to the price bundling could attract more users to subscribe for the integrated service. As seen in Table 9 it is necessary for the operators to have at least 5 connections in a single hotspot location per month from the Wi-Fi only service to have a positive NPV. The more the number of connections per month is, the higher the revenue. However, even with a smaller number of connections, the operator might be able to justify the Wi-Fi deployment considering other benefits, e.g., performance improvement of GPRS service and avoiding customer churn. 6. Conclusions In this paper we present a simple, yet effective, pricing model for GPRS networks integrated with Wi-Fi. We propose the use of demand functions to describe the response of users based on the charges in each network. By integrating the two technologies together, operators can attract new customers with value-added services provided by Wi-Fi networks, thereby, reducing the churn. Furthermore, the GSM/GPRS cellular operators can delay their 3G deployments as the integration could offer 3G-like services. The integration is beneficial to the cellular

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operator not only in terms of additional revenue, but also in terms of overall performance of GPRS services, due to traffic offload from the resource-limited GPRS networks to the Wi-Fi networks. The traffic offload occurs as users take advantage of the price difference between GPRS and Wi-Fi networks; users who are not willing to pay high GPRS prices, will eventually start utilizing the Wi-Fi network. Thus, from the users’ perspective, integration provides an alternative to transfer data based on pricing preferences. According to our pricing model, the integration of GPRS with Wi-Fi networks provides additional revenue for the cellular operators, for a certain level of data volume. To verify if such integration yields a profitable strategy for cellular operators, a case study with cost/benefit analysis is conducted. By making certain assumptions on the number of mobile users and Wi-Fi only users as well as on the expenditures due to integration, the cost analysis indicates that integration is a significant factor towards profitable data services for GPRS network operators. In order to see the real benefits of integration, the pricing schemes applied are very important. We believe that cellular carriers must re-examine their business strategies and move forward with WiFi integration; otherwise, they can ignore Wi-Fi in favor of their 2.5G/3G data service at their own risk. To our opinion, the most successful carriers in data services are the ones who integrate Wi-Fi with their existing cellular systems. The present paper provides evidence towards that direction. Acknowledgements We are thankful to Mr. Clint Smith, with AT&T Wireless (now Cingular), for his valuable comments in budgeting of GSM/GPRS projects and the benefits of Wi-Fi integration with cellular systems. Appendix A. Network dimensioning and customer base GSM cells Total coverage area = 23 square miles (island of Manhattan) Area covered by one GSM/GPRS urban cell site = 1.212 square miles Number of GSM/GPRS cell sites in Year 1 = 19 Annual growth in total (voice & data) subscriber base = 6% Number of GSM/GPRS cell sites in Year 4 = 22

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Wi-Fi Hotspots or Access Points (APs)

Assumptions:

Area covered by one Wi-Fi Access Points (AP) = .0102 square miles If the company wants to cover the entire coverage area, they would need to deploy 23/0.0101, i.e. 2255 APs. Corporate decision to cover 1/8th of total Manhattan area in Year 1 and 1/5th by Year 4. Therefore;

Cost of Business T1 Line = $200/month Maintenance Cost for AP = $50/month Depreciation Cost of AP = $15/month

• Total number of Wi-Fi APs at the end of Year 4 = (Total coverage area/5)/Area covered by one AP = 451 • Total number of ESS systems in Year 4 (one GSM/GPRS base station cell with multiple APs connected to it) = 22, i.e. equal to the number of GSM cells. Customer base and subscribers calculation Total (voice & data) subscriber base in Year 1 = 120,000 Annual growth in total (voice & data) subscriber base = 6% Data subscribers = 15% of the total subscriber base (constant for all years) Data users using integrated (Wi-Fi & GPRS) service = 10% of data subscribers, i.e. 1.5% of total subscribers. Appendix B. Cash flow analysis Capital expenditure (Table 3) Total Capital Expenditure = Deployment Cost (for Hotspots) + Integration Cost (for ESS systems) = Access Point Cost * (Number of APs/ Year) + Integration Cost * (Number of cells/year). Assumptions: Deployment Cost for Hotspot = $500 Integration Cost = $5000 For example, for Year 1: Total CapEx for Year 1 = $500 * 281 + $5,000 * 19 = $140,500 + $95,000 = $235,500 Operating expenditure (Table 4) Total Operational Expenditure = (Number of APs/Year) * (Cost of T1 Line + Maintenance Cost for AP + Depreciation cost of AP).

For example, for Year 1: Total OpEx for Year 1 = 281 * ($200 * 12 + $50 * 12 + $15 * 12) = $893 580 Revenue projections (Table 5) Additional Revenue due to Integration = (Number of users of the integrated service) * (Average Number of Connections per user per month) * (Charge per Connection from our Pricing Model) * (12 months per year). The Charge per Connection from our Pricing Model is determined as the Additional (extra) revenue from (6). Making assumptions as presented in Fig. 5, for an (average) data connection of 3 MB and $4 fee per Wi-Fi connection, the resulting charge is $3.80 (constant through out the life of the project) Revenue due to Wi-Fi only service = (Number of Wi-Fi hotspot) * (Number of Connections per location per month) * (Per Connection Fee) * (12 months per year) Per Connection Fee for Wi-Fi only Users = $8 (constant though out the life of the project) Total revenue = Additional Revenue due to Integration + Revenue due to Wi-Fi only service For example, for Year 1: Additional Revenue due to Integration for Year 1 = 1,800 * 12 * $3.8 * 12 = $984,960 Revenue due to Wi-Fi only service for Year 1 = 281 * 30 * $8 *12 = $809,280 Total Revenue for Year 1 = $984,960 + $809,280 = $1,794,240 References Ahmavaara, K., Haverinen, H., Pichna, R., 2003. Interworking architecture between 3GPP and WLAN systems. IEEE Communications Magazine 41 (11), 74–81. Alleman, J., 2002. A new view of Telecommunications Economics. Telecommunications Policy 26, 87–92.

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