Electronic copy available at: http://ssrn.com/abstract=2588010

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Technology Broadband Roadmap for Rural Areas in the Andes and Amazon Regions in

Peru By

David Espinoza1 and David Reed2

1. Introduction

For several decades, the landscape in South America (the Andes range and the Amazon Rainforest) has posed one of the main barriers to carrying out large-scale infrastructure deployments (i.e. road, power grid, or telecommunication deployments) in rural areas. In the last 5 years, several governments in South America, recognizing the importance of broadband for economic and social development, have been proactive to design and implement national broadband plans [1]. In 2010, the Peruvian government launched the “National Plan for the Development of Broadband” [2], which aimed to increase broadband penetration by increasing capacity in urban areas and reaching rural areas across the country.

The broadband penetration in Peru (population 30 Million) was 7% in 2012, up from 3.46% in 2010 [3]. Fixed and mobile broadband services expansion in major urban areas explains the growth in broadband penetration. In 2012, there were 1,563,217 fixed broadband households and 702,315 mobile broadband users. For combined fixed and mobile broadband users, fixed broadband was 69% and mobile broadband 31% of the broadband market [4]. The fixed broadband service was provided mainly using ADSL, cable, WiMAX, and VSAT. Mobile broadband service was provided using 3G technology. The dominant technology is ADSL which had 86% of the fixed broadband market and 60% of the total (fixed and mobile) broadband market. This indicates most of the broadband service is provided using the legacy telephone networks (PSTN) that are mainly deployed in urban areas. The broadband penetration in Peru can also be presented based on geographical location of the 24 states of Peru grouped in the three main regions: Coastal, Andes and Amazon. About 90% of the broadband users are located in the Coastal region, while only the remaining 8% and 2% are located in the Andes and Amazon regions, respectively [4]. In these underserved regions, the penetration is equal or lower than 2% in each state; in most cases even lower than 1%.

As part of the Peruvian National Broadband plan, the Ministry of Transport and Communications of Peru and Telecommunications Investment Fund (FITEL) (the government agency that promotes and funds deployment of broadband systems in the rural areas) have

1 PhD Candidate, Interdisciplinary Telecom Program, University of Colorado at Boulder 2 Faculty Director, Interdisciplinary Telecom Program, University of Colorado at Boulder

Electronic copy available at: http://ssrn.com/abstract=2588010

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designed, planned and set a bid for the construction of the national optic fiber backbone to interconnect and increase capacity in main urban areas and reach rural areas across the country (22 states and 180 provinces) [5], especially in most states in the Andes and Amazon regions. In 2013, the company TV Azteca won the government bid (USD 323 Million) to deploy the backbone network with completion scheduled by the end of 2015. The provision of broadband services to reach final users will be carried out through access networks connected to this fiber backbone. By the end of 2014, the Peruvian government set public bids for the deployment of broadband access networks (fiber optic and wireless) in four states (each project or bid corresponds to one state) [6], where broadband service providers will compete for a government subsidy to provide services to government institutions (mandatory or primary customers), and business and residential customers (secondary customers).

In this scenario, there is a high level of uncertainty as to the best wireless access technology to deploy and operate. The study [7] attempts to provide solutions for this question by describing a broadband deployment framework identifying the most promising technologies to connect underserved areas in emerging countries to the Internet. The study describes how to develop broadband roadmaps, representing the best local access network technology fit, considering technology alternatives which have the potential to provide the most economical combination of network coverage and broadband speed for a given population density in underserved areas. While [7] provides a qualitative framework for developing a broadband roadmap for underserved areas, beyond concluding that early solutions will be wireless, it does not provide any concrete recommendations for the best technical wireless options to deploy over time.

This study attempts taking this next step addressing the research question: “What is the technology roadmap for introducing broadband services to underserved and unserved areas in the Andes and Amazon regions of Peru?” Our study identifies and compares current (WiFi, WiMAX, LTE and TVWS) wireless broadband technology candidates for access networks in the Andes and Amazon regions of Peru. The research focuses on the following key issues:

1. Analysis of the access network deployment cost for these wireless options based on different coverage and speed scenarios.

2. Analysis of operating and maintenance costs for these options after the initial deployment. Once again coverage and speed will play an important role in the analysis. This look at costs over time will allow the development of a broadband roadmap for the region that will describe the forecasted deployment of network capacity over a time period of 10 years.

3. Spectrum management. In the last decade in Peru, most licensed bands used to provide broadband services have been granted to operators on a nationwide and regional basis. This paper will examine current available spectrum and alternatives to enable more spectrum for new access network deployments in underserved or unserved areas in the Andes and Amazon regions.

To address these points, this study will determine the technical performance and cost of the wireless options using both specialized propagation software to carry out wireless network simulations in this remote environment and a detailed engineering cost model to quantify the cost of the access networks (based on coverage and speed) in this geographic setting (the

Electronic copy available at: http://ssrn.com/abstract=2588010

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Andes and Amazon regions). We will employ a quantitative research methodology that will utilize geographic coverage and demand data provided by Peruvian government agencies (i.e., the Supervisory Agency of Private Investment in Telecommunications (OSIPTEL) (regulatory agency), Telecommunications Investment Fund (FITEL) (universal service fund agency), Ministry of Transport and Communications (MINTEL), etc. Additionally, the paper will use access network elements and wireless equipment cost data from vendors in Peru and regional vendors, and telecom network deployment contractors.

2. Study Areas and Broadband Demand Estimation

2.1 . Study Area Selection

This study analyzes two geographic settings in Peru, the Andes (mountains) and Amazon (rainforest jungle) regions. Our case study area selection criteria targeted the following: 1) low broadband penetration areas, 2) rural remote locations, and 3) harsh landscape settings that will help to analyze wireless technology performance in extreme conditions. We reviewed geographical and population data provided by FITEL in the projects or government bids “Broadband Deployment for Integrated Connectivity and Social Development” [6] for the deployment of broadband networks in four underserved states, where broadband service providers will compete for a government subsidy to provide services to government institutions (mandatory clients), and residential customers (secondary clients). For these bids, the government made available data of primary interest areas (list of mandatory towns, government institutions, coordinates, altitude, etc.) of all provinces and counties in each of the four states. In Peru, states divide into provinces and provinces divide into counties (also called districts). We used these data to plot potential study areas into digital satellite maps, and applied our selection criteria.

The selected area in the Andes region was the Yauli County, in the State of Huancavelica (0.32% broadband penetration in 2010 [2]), in the central Andes of Peru. The capital of the county, Yauli, is located at 3,400 meters (11,155 feet) above sea level. The landscape in this area consists of mountains, hills and moderate or minimum vegetation. There are tens of agrarian towns located around the county capital. Most towns consist of a main square surrounded by houses and government buildings, and other houses spread in open land closer to agricultural land.

The selected area in the Amazon region was the Samugari County, in the State of Ayacucho (1.06% broadband penetration in 2010 [2]), in the central Andes and western Amazon rainforest of Peru. The capital of the county, Palmapampa, is located at 810 meters (2,660 feet) above sea level. The landscape in this area consists of hills and dense vegetation (rainforest jungle). There are agrarian towns in the district mostly located along the Apurimac River. In this area, most towns consist of a main square surrounded by houses and government buildings with only a small percentage of houses in open land due to the dense jungle vegetation. Figure 1 shows the Yauli and Palmapampa towns in the two selected study areas.

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Figure 1. (Left) Yauli town, capital of the Yauli County in the State of Huancavelica in the central Andes of Peru. (Right) Palmapampa, capital of the Samugari County in the State of Ayacucho in the central

Andes and western Amazon of Peru.

2.2 Service Coverage

The geographical service coverage criteria included the following: 1) coverage defined by the FITEL projects, and, 2) additional coverage based on estimated expansion of the wireless access network to other towns in a 10-year period.

Each FITEL project defines mandatory coverage that consists of lists of towns, elementary/high schools, health posts, police stations, etc., for each state [6]. The FITEL coverage selection criteria includes towns which meet the following criteria: population of at least 300 people before deployment, student population of more than 100, or having at least one health post, one police station or any other government institution. Additionally, towns that meet these criteria must not have available broadband service or have a low quality service, and must have public electricity grid service. In the selected Yauli area, the FITEL coverage includes 16 towns, 22 schools, 11 health posts, 1 police station and 2 government buildings. In the selected Samugari area, the FITEL coverage includes 19 towns, 28 schools and 5 health posts. The number of towns remains static for the deployment and following 10 years, while the number of government clients slightly increases around 10%. The FITEL coverage requirement includes only government clients in towns and it is up to broadband service providers to estimate and plan for potential residential customers.

Our additional service coverage criteria includes towns which will reach at least 300 people within the next 10 years after deployment, or have currently broadband service, as the new wireless broadband network can bring another competitor to the market for the benefit of customers.

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Combining the FITEL and our additional service coverage criteria, the total service coverage for the analysis is as follows:

Yauli area: 27 towns with a total estimated population of 17,338 and 3,511 households in 2015, located in a 62.71 square mile area.

Samugari area: 29 towns with a total estimated population of 16,909 and 3,884 households in 2015, located in a 662.52 square mile area.

The service coverage in both areas includes both government and residential customers. The broadband service speed and overbooking factor was defined based on the service requirements in the FITEL projects [6]. The fixed broadband speed offered to customers for this analysis is 2Mbps and an overbooking factor of 10:4 for government customers and 10:1 for residential customers.

2.3 Broadband Demand Estimation

After defining the service coverage for the two study areas, we estimated the broadband demand for residential customers. The government demand was defined in the FITEL coverage requirement for government customers. To calculate network costs, we estimated the residential broadband demand at deployment and for the next 10 years in the two study areas. For this estimation we used data of fixed Internet service penetration [8], fixed monthly Internet service price [9] and average monthly income from the 24 states and three regions (Coastal, Andes and Amazon) of Peru [10].

We use Internet service penetration data as a proxy for broadband service penetration. This Internet service penetration includes both narrowband and broadband; however, after 2004, there was a decreasing trend of narrowband (dial-up) customers and, in 2009, narrowband penetration was only 2.17% of the total fixed Internet penetration. We estimated the broadband penetration in Huancavelica and Ayacucho (the two study areas are located in these states) for 2015 based on historical fixed Internet service penetration (from 2002 to 2013) [8] using the yearly average Internet service penetration growth. We estimated broadband penetration for 2015 (as percentage of households) of 2.9% in Huancavelica (0.2% yearly average growth) and 5.7% in Ayacucho (0.4% yearly average growth). The Internet service demand for 2015 provides the estimated demand at deployment in the study areas (previously unserved or underserved). The next step was to calculate the average demand growth for the next 10 years.

After the deployment and during the operation of the wireless broadband access networks in the two study areas, we expect an increase in the average yearly demand growth (from 0.2% and 0.4%) mainly due to two factors: 1) new availability of broadband service in previously underserved or unserved areas, and 2) a low initial Internet service price set by the government that includes subsidy. To estimate the increased yearly demand growth, we generated a fixed Internet service demand curve, shown in Figure 2, having in the x-axis the percentage of the monthly Internet service price out of the monthly average income, and, in the y-axis, the fixed Internet service penetration for all states of Peru in 2013. This Figure shows an exponential

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trend curve. Then using the new regulated Internet price for the Huancavelica and Ayacucho States, we calculated the new percentage of monthly Internet price out of average monthly income for these states. We introduced these percentages in the demand curve and identified two states that are already operating in those conditions. We estimated that Huancavelica and Ayacucho’s Internet penetration will follow the Internet penetration of the States of Tumbes and Lima (other areas than the city of Lima). Then the estimated yearly Internet growth for Huancavelica and Ayacucho will be 2.7% and 3.7%.

Figure 2.The Internet service price as percentage of monthly income determines the Internet service penetration or Internet demand curve

The fixed broadband demand estimation for the states of Huancavelica and Ayacucho, where the study areas are located, is 2.9% and 5.7% of the households after the deployment, and the yearly average growth is 2.7% and 3.7% respectively. This demand estimation applies for all selected towns except for the county capitals. For the case of the towns of Yauli and Palmapampa (capitals of the selected counties), we assumed there will be an ADSL incumbent and new broadband provider that will have a 50% of the customers after deployment and the ensuing next 10 years.

3. Wireless Broadband Access Network Design

In this section, we identify and compare current (WiFi, WiMAX, LTE and TVWS) wireless technology candidates for broadband access networks, and design efficient state-of-the-art access networks with optimal topology for the two study areas, in the Andes and Amazon regions of Peru. Inputs for the network design are the estimated service demand, coverage and speed requirements (see Section 2: Study Areas and Demand Estimation), technical specifications of network equipment for the different technologies and available frequency resources. The EDX Signal Pro software is used for the network design.

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Different studies state that wireless technologies are the first viable options for new deployments to provide broadband services in underserved and unserved areas in rural areas in developing countries [7][11][12][13]. Table 1 shows the wireless technology candidate analyzed in this study to provide fixed wireless broadband service to the study areas.

Table 1. Wireless broadband technology candidates for access network deployments in rural areas.

Technology Operation Band (Peru) Max. Data Rate WiFi

(IEEE 802.11a/b/g/n) 2.4GHz 5.8GHz

a/g: 54Mbps n: 600Mbps

WiMAX (IEEE 802.16d)

2.5GHz 3.5GHz

350Mbps

TV White Spaces (IEEE 802.22 & IEEE 802.11af)

540MHz-746MHz 22Mbps

LTE 1,900MHz 2,100MHz

1Gbps

In rural areas, TVWS [14], WiMAX [15][16][17][18] and LTE [19][20][21] have reported deployments with cell site coverage ranging from a few to tens of miles. WiFi has also reported deployments in rural areas in long distance radio links and access networks using equipment 802.11a, b and g [22][23]. Even though WiFi was designed for a local area network range (a few to hundreds of feet), these cases show that WiFi can also work in longer distances in rural settings. For our study, for the WiFi network design, we used the 802.11n standard in order to benefit from the higher data rates provided by this standard in comparison to 802.11a/g (maximum data rate of 54Mbps); however there are not current reports of outdoor rural deployments using this standard. The study [24] conducts simulations and deployment testing of the 802.11n standard in long distance outdoor radio links and concludes that 802.11n, in point-to-point links, shows higher performance than 802.11a when used with frame aggregation, 40MHz bandwidth and MIMO antennas, and that 802.11n shows at least a data rate 50% higher than 802.11a in distances under 35km (for point-to-point links). The performance of WiFi point-to-multipoint links (between the access point and client stations), is set by the WiFi channel access mechanism Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA), which has a maximum allowable distance to avoid collisions of 10.2km for 802.11a and 8.4km for 802.11g [22]. Based on these studies, for WiFi point-to-point and point-to-multipoint performance, and to keep WiFi from reaching its limits, we assume maximum distances for 802.11n for backhaul radio links and access links to be 15km and 6km, respectively.

3.1 Wireless Access Network Design Simulations

For the wireless access network design and propagation modeling, we used the EDX Signal Pro software. EDX allows using different propagation models such as the free space, empirical (i.e., Extended Hata or Cost 231-Hata), stochastic (i.e., Cost 231 Walfisch-Ikegami), and physical (Anderson-2D) models. We selected the Anderson-2D model for the analysis for the reasons

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stated in [25]: 1) supports the use of terrain, land use and land coverage (LULC) and building data, 2) this model was selected by the Telecommunications Industry Association (TIA) as the definitive model for TSB-88A/B for predicting coverage and interference for mobile radio systems in the United States, and 3) the model was found to outperform (average standard deviation of the prediction error) the Longley-Rice (ITM), TIREM and other physical and empirical models when compared against measurements.

For the propagation analysis in EDX, we used the Aster digital terrain data [26], which generates 30-meter resolution global maps. We also used Digital satellite images from Google Earth and Bing to identify different land use and coverage areas. For Yauli, the following areas and layer heights were identified and set in EDX: forest (15 meters), grassland (1 meter) and towns (4 meters). For Samugari, the following areas and layer heights were identified and set: rainforest (15 meters) and towns (4 meters). These settings allow for the best accuracy for the propagation modeling needed for the efficient design of the wireless access networks in the settings established by the case study areas.

The technical specifications of the RF equipment were obtained from datasheets and manuals available on vendor websites or made available by vendor representatives. For the analyzed technologies (WiFi, WiMAX, TVWS and LTE), we reviewed and selected equipment from a broad range of options in the market. The methodology used to benchmark the different options (base stations and CPE) used the Key Performance Indicators (KPI) defined in [27] such as maximum range and throughput (output power, sensitivity, antenna gain, and data rates), next generation capabilities (clients load, high speed ports), quality of service, market status (market availability), innovation potential, and upfront and operating expenses of the equipment.

For spectrum resources, we reviewed current spectrum allocation in Peru. WiFi operates in the following unlicensed bands: 900MHz, 2.4GHz, 5.4GHz and 5.8GHz. WiMAX operates in the 2.5GHz (mobile services) and 3.5GHz (fixed services) bands. LTE operates in the 1,900MHz and 2.1GHz bands. Digital Television Broadcast services operate in the 470-608MHz and 614-698MHz bands [28]. For the analyzed technologies, we set our simulations in these bands with exception of LTE, which we simulated in the 2.5GHz band instead of the 2.1GHz band due to availability of technical and cost data of LTE equipment in this band. We also reviewed current spectrum assignment to determine maximum spectrum available per operator or service provider. WiFi does not have any limitation in this matter due to the use of unlicensed spectrum. WiMAX using the 3.5GHz band has a spectrum cap of 50MHz, and LTE using the 1.7/2.1GHz band has a spectrum cap of 40MHz. For TVWS there is not any defined regulatory framework yet.

Another important factor considered for the access network design to avoid interference was the frequency reuse pattern. We defined this pattern based on the maximum spectrum available and the channel width. We set the following channel widths in order to maximize users per base station and achieve an optimal reuse pattern: WiFi, 20MHz, WiMAX, 10MHz, LTE, 20MHz, and TVWS, 6MHz. The resulting reuse patterns were WiFi (2.4GHz), 3, WiMAX, 5, and LTE, 2.

Finally, the wireless broadband access network design for each of the technologies consisted of an initial design able to handle the initial demand but also the demand growth for the following

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10 years. Then, each design increases capacity following the demand growth for this period. As a result, the access network increases capacity in the backhaul and access equipment, energy, and infrastructure.

Figure 3. EDX Signal Pro simulation of the WiFi access network and service coverage (yellow area) in the Yauli County in the central Andes of Peru.

3.2 Simulation Results

The designed wireless broadband access networks consist of wireless backhaul radio links and base stations or outdoor access points (including the energy system and infrastructure for each node) placed in strategic locations both to achieve an optimal number of radio links and a minimum number of base stations to meet coverage and data rate requirements at the lowest cost. Figure 3 shows an EDX simulation of an access network in the Yauli County. Table 2 and Table 3 show the result of the network design for the WiFi, WiMAX, TVWS and LTE technologies at the end of the 10-year period. The WiFi analysis for each study area consists of two network designs. The first WiFi design is the design recommended by FITEL, which uses one node per town (conservative approach). For the second WiFi design, we stretched the WiFi coverage and use one node to provide coverage to more than one town. We called this second WiFi design Extended WiFi.

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Table 2. Network design results for the Yauli study area.

Yauli Study Area Nodes Backhaul

Links Base

Stations CPE

21 to 30 m.

towers

45 to 60 m.

towers

WiFi 8 7 14 1,205 8 0

Extended WiFi 5 4 12 1,205 5 0

TVWS 3 2 37 1,205 3 0

WiMAX 4 3 13 1,205 4 0

LTE 3 2 5 1,205 3 0

In Table 2, we can observe that, in the Yauli area, TVWS and LTE require the least number of nodes, 3. This is due to the TVWS operation band (470-698MHz), which allows propagation through or around obstacles (moderate vegetation and mountains), and the enhanced sensitivity levels of LTE base stations (lower than -100dBm). However, TVWS requires the highest number of base stations due to a smaller average channel capacity and data rates in comparison to the other technologies resulting in a lower client load. WiMAX requires 4 nodes and can operate in near line-of-sight (LOS) or non-LOS conditions; LTE can also operate in these conditions. However, WiMAX requires more base stations than LTE due to a smaller average channel capacity, resulting in a smaller client load, and also operates at a higher frequency than LTE, resulting in smaller area coverage. The WiFi design (conservative) requires 8 nodes and Extended WiFi design requires 5 nodes. Both WiFi designs require the two highest number of nodes due to the limited channel access mechanism to serve a large number of clients (requires to activate additional collision avoidance features which increases the overhead leading to a decrease in average channel capacity), and the requirement of LOS or near-LOS between access points and client devices.

In this study area in the Andes region, towers range from 21 to 30 meters (69 to 98 feet) and are placed in elevated locations to enable LOS over mountainous terrain for backhaul radio links. Vegetation does not pose a major obstruction or attenuation source.

Table 3 shows that in the Samugari area, the TVWS, LTE and WiMAX technologies require 9, 10 and 11 nodes respectively. In this area, dense vegetation between towns (separated from 2km to 8km) prevents these technologies from covering larger areas and several towns with one base station; resulting in an increased number of required nodes in comparison to the Andes scenario. The dense vegetation is a major obstacle for these broadband technologies to make full use of their technical features. The WiFi and Extended WiFi designs require the most infrastructure, 18 and 14 nodes, respectively.

In this area, due to the rainforest landscape, the access networks required use of taller towers, from 30 to 60 meters (98 to 197 feet) in order to propagate above the tree canopy (15 to 20 meters height) for both the backhaul radio links and base stations. The rainforest poses as a major attenuation source.

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Table 3. Network design results for the Samugari study area

Samugari Study Area Nodes Backhaul

Links Base

Stations CPE

21 to 30 m.

towers

45 to 60 m.

towers

WiFi 18 17 27 2,175 13 5

Extended WiFi 14 13 24 2,175 9 5

TVWS 9 8 72 2,175 6 3

WiMAX 11 10 29 2,175 8 3

LTE 10 9 13 2,175 7 3

4. Engineering Cost Analysis

The engineering cost analysis in our study uses a bottom-up approach to estimate the deployment and operation cost of an efficient state-of-the-art wireless broadband access network with an optimal topology in the two study areas for a 10-year period. In the previous section, we designed and simulated these access networks for the analyzed technologies. We used these technical designs as input for the engineering cost analysis. To conduct this analysis, we also developed an engineering cost model and used cost data provided by different sources. We used Analytica to develop the engineering cost model and conduct the analysis. In this section, we discuss the cost model, cost data sources, and present the results of the engineering cost analysis.

4.1 Engineering Cost Model

The engineering cost model developed in this study has two main components: capital

expenditures (CAPEX) and operating expenditures (OPEX).

Table 4 shows the CAPEX main categories, which includes tower, civil engineering, energy system, network equipment and customer premise equipment (CPE). Table 5 shows the OPEX main categories, which includes network operation, preventive maintenance and reactive maintenance. The input for the cost model comes from the access network design for the 10-year period. This cost model will reflect the deployment and operation costs of the access networks for the analyzed technologies, and as the network expands or increases its capacity, in the backhaul or base stations, to handle demand growth over the 10 year period, the model will translate the upgraded network design into costs.

In Table 6, we present the main cost data sources for the different categories of the cost model. We reviewed, compared and analyzed the cost data provided from different sources and selected the best option (balance between low cost and high performance) for the elements of the different cost categories in order to reduce biases or distortions that any single source might generate.

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Table 4. CAPEX categories of the engineering cost model.

Cost Category Description

Tower Infrastructure to install the base stations and includes installation, lightning protection, grounding system and pre-installation studies per site.

Civil engineering Node facilities to host equipment, cabinets, energy system, and includes installation, and pre-installation studies per site.

Energy system The energy system connects to the public grid and also has a backup system in case the grid fails.

Network equipment RF equipment (active and passive) for the backhaul radio links and base stations and includes installation.

Customer premise Equipment (CPE)

RF equipment (indoor and outdoor) for government and residential clients and includes installation.

Table 5. OPEX categories of the engineering cost model.

Cost Category Description

Operation Electricity consumption per node, technical headquarters operation, personnel salaries, local operation fees and fiber optic backhaul interconnection fees.

Preventive maintenance

Replacement of network equipment, CPE and energy system elements before equipment reach their lifespan.

Reactive maintenance Replacement of network equipment and CPE when failure or malfunction occurs.

Table 6. Cost data sources for the CAPEX and OPEX.

Cost Data Source Description Investment Fund in Telecommunications (FITEL)

FITEL provided cost data in the categories of towers, civil engineering, energy system, and network equipment. It also provided operation and maintenance cost data.

RF equipment vendors Regional vendor and local representatives in Peru provided quotations of wireless equipment. For equipment quoted abroad (U.S., China, etc.), import and sales taxes were added.

Wireless Access Network Deployment Contractors

Cost data were requested from contractors and research groups that have carried out deployments in the Andes and Amazon regions. They provided cost data in the categories of towers, civil engineering, energy system, and network equipment. They also provided operation and maintenance cost data.

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4.2 Engineering Cost Analysis Results

This subsection presents the results of the engineering cost analysis for the technology candidates in the two study areas and the general findings of this analysis. We present the total cost of deployment and operation, net present value (NPV), cost per user and cost per Mbps. Total Deployment and Operation Cost

Total cost includes the combined CAPEX and OPEX for the deployment cost (year 0), and the operation and maintenance cost for a 10-year period (year 1 to 10). The five CAPEX categories (tower, civil engineering, energy system, network equipment and CPE) are present in the deployment cost (year 0). The CAPEX is also present during the 10-year period as the broadband demand increases and the network expands capacity to handle this demand growth. Adding customers every year will be reflected as costs in the CPE category (it includes installation cost) and the network equipment category; as additional base stations, or access points, are required to connect these additional customers. In the case that one of the network nodes or cell sites reaches its maximum capacity (spectrum constraint due to the channel reuse factor or non-interference channels), then the network will require an additional cell site (tower, civil engineering, energy equipment and network equipment categories) and proper spectrum management of the channels in this area. The OPEX is present at deployment and during the 10-year period in the operation and maintenance categories.

Figure 4 shows the annual deployment and operation cost of the wireless broadband access network for the Yauli County. LTE presents the lowest deployment (year 0) cost followed by WiMAX, Extended WiFi, TVWS, and WiFi. For the remainder of the 10-year period, Extended WiFi has the lowest cost per year followed by WiFi, LTE, WiMAX and TVWS. In year 5, WiMAX presents a peak due to adding a new node to the network, and, in year 7, LTE presents a peak due to adding two base stations. The other technologies also increase the number of access points or base stations during the 10-year period; however the lower cost of WiFi or TVWS base stations do not generate peaks in the total cost curves.

Figure 5 shows the deployment and operation cost of the wireless broadband access networks for the Samugari County (Amazon region). We can observe that the total cost in the Samugari area is higher than in the Yauli area. As mentioned in the 3.2 Simulation Results section, the longer distances and the dense vegetation between towns prevented, in some cases, coverage to several towns from one base station. As a result, networks in Samugari require from 2 to 3 times the number of nodes than networks in Yauli, even when networks in Samugari provide service to 29 towns and in Yauli to 27 towns, almost the same number of towns. In Figure 5, we can observe that LTE presents the lowest deployment cost, followed by TVWS, Extended WiFi, WiMAX and WiFi. For the operation and maintenance cost over the 10-year period after deployment (year 1 to year 10), Extended WiFi presents the lowest cost per year followed by WiFi, LTE, WiMAX and TVWS. There are not additional nodes added to the network for any of the five technologies in this study area, as a result, there are no peaks in the curves.

The total cost results show the expenses at deployment and for the next 10-years. These results show which technology has the lowest deployment cost, and which one roughly has the lowest operation cost over this period. However, in the two study areas, none of the technologies have

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both the lowest deployment and operation cost, which in addition to operation cost fluctuations, it is unclear yet to determine which technology presents the lowest cost. However, this total deployment and operation cost per year provide input to calculate the Net Present Value (NPV) of each technology option.

Figure 4. Deployment and operation cost of wireless broadband access networks in the Yauli County for a 10-year period using the WiFi, Extended WiFi, TVWS, WiMAX and LTE technologies.

Figure 5. Deployment and operation cost of wireless broadband access networks in the Samugari County for a 10-year period using the WiFi, Extended WiFi, TVWS, WiMAX and LTE technologies.

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Net Present Value (NPV) of the Total Deployment and Operation Cost

To estimate the NPV of the total cost, we used input of the total deployment and operation cost per year and the yield of 10-year Peruvian government bonds. The NPV allows comparing in the present the cost of deploying and operating the access networks over the 10-year period. Figure 6 shows the NPV for Yauli and Samugari. We can observe that in Yauli, Extended WiFi presents the lowest NPV; closely followed by LTE, and then WiFi, WiMAX and TVWS. In Samugari, Extended WiFi also presents the lowest NPV, followed by LTE, WiFi, WiMAX and TVWS. Both study areas present the same NPV ranking where Extended WiFi has the lowest cost and TVWS the highest cost.

Figure 6. Net Present Value (NPV) of the cost of wireless access network options in the Yauli (left) and Samugari (right) areas.

Figure 7. CAPEX NPV breakdown in the main categories.Figure 7 shows the NPV breakdown of the CAPEX by cost category to identify the main cost drivers for the CAPEX for each of the technologies. In Yauli, LTE presents the lowest total CAPEX, closely followed by Extended WiFi, and at the higher end WiFi, WiMAX, and TVWS. In the aggregated categories of towers, civil engineering and energy system (infrastructure and node elements), TVWS and LTE have the lowest cost, followed by WiMAX, Extended WiFi and WiFi. In the network equipment category (backhaul and access base stations), Extended WiFi and WiFi present the lowest cost, followed by LTE, WiMAX and TVWS. Finally, in the CPE category, WiFi and Extended WiFi also present the lowest cost, followed by LTE, WiMAX and TVWS. This CAPEX NPV breakdown shows the combined cost efficiencies that each of the technologies presents due to their technical features, operation frequency and resulting capacity and coverage. TVWS and LTE require the lowest number of nodes due to technical features (low frequency operation for TVWS and enhanced sensitivity for LTE) which allow to provide a larger coverage per base station resulting in the lowest infrastructure cost; however, combined network equipment and CPE cost of TVWS is 250% more expensive than LTE and 470% more expensive than Extended WiFi (the lowest network equipment and CPE cost). WiMAX requires one more node to provide the same coverage due to operation at a higher frequency resulting in higher infrastructure cost than TVWS and LTE. Additionally, the combined network equipment and CPE cost of WiMAX is 220% more expensive than LTE and 490% more expensive than Extended WiFi. WiFi and Extended WiFi have the lowest combined network equipment and CPE cost due to the low cost

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WiFi equipment, however, a limited reach of this technology (in terms of distance) in comparison to the other technologies results in requiring a higher number of nodes to provide the same coverage. As a result, WiFi infrastructure cost is 220% more expensive than TVWS infrastructure.

Figure 7. CAPEX NPV breakdown in the main categories.

In Figure 7, we can also observe the CAPEX NPV breakdown for the Samugari area. LTE has the lowest CAPEX, closely followed by Extended WiFi, and then WiFi, WiMAX, and TVWS. In the aggregated categories of towers, civil engineering and energy system (infrastructure node elements), TVWS has the lowest cost due to requiring the lowest number of nodes. LTE and WiMAX follow TVWS. The WiFi and Extended WiFi technologies have the highest cost in this aggregated category. In the network equipment category (backhaul and access base stations), Extended WiFi and WiFi are the lowest cost, followed by LTE, WiMAX and TVWS. Finally, in the CPE category, WiFi and Extended WiFi are the lowest cost, followed by LTE, WiMAX and TVWS. The CAPEX breakdown in this scenario also highlights the cost efficiencies that each technology achieves due to the technical standard, operation frequency and resulting capacity and coverage. TVWS is the lowest infrastructure cost; however, the higher cost of network equipment and CPE results in the highest CAPEX. LTE provides the second lowest infrastructure cost with lower network equipment and CPE cost than TVWS. LTE equipment cost is only more expensive than WiFi and Extended WiFi equipment. WiMAX infrastructure cost and network and CPE equipment cost are higher than LTE. Extended WiFi and WiFi have the highest infrastructure cost; however, these technologies provide the lowest cost of network and CPE equipment.

Figure 8 shows the OPEX breakdown for the Yauli and Samugari areas. In Yauli, Extended WiFi is the lowest cost, closely followed by LTE and WiFi. WiMAX and TVWS have the highest cost. In Samugari, Extended WiFi and WiFi have the lowest cost followed by LTE, TVWS and WiMAX. In both study areas, we can observe that preventive maintenance is the main cost driver for the OPEX. Preventive maintenance is directly related to the amount of network equipment, CPE and energy system equipment. Preventive maintenance replaces equipment before it reaches the end of its economic lifetime. We can also observe that reactive maintenance cost varies depending on the technology. Since reactive maintenance is directly related to the amount of network equipment and CPE, it has the same cost ranking than the CAPEX of this equipment. Extended WiFi and WiFi are the lowest cost, followed by LTE, WiMAX, and TVWS. The rest of

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OPEX categories are similar for the different technologies; except for the electricity cost which varies depending on the amount of base stations required for each technology in the access networks.

Figure 8. OPEX NPV breakdown in the main categories.

Annuitized Upfront Cost

For calculating the cost per user per month and cost per Mbps per month, we applied an annuity factor to the upfront cost of the network deployment (CAPEX year 0) in order to distribute this cost in the 10-year period. Figure 9 shows the CAPEX and OPEX for the access network deployments in the Yauli County. The CAPEX and OPEX together are the components of the network deployment and operation cost showed in Figure 4. Figure 10 shows the updated CAPEX including the annuitized upfront cost of the network deployment and the resulting total deployment and operation cost of the networks for the Yauli County. Figure 11 shows the CAPEX and OPEX for the access network deployments in the Samugari County, which also are the components of the network deployment and operation cost showed in Figure 5. Figure 12 shows the updated CAPEX including the annuitized upfront cost of the network deployment and the resulting total deployment and operation cost of the networks for the Samugari County.

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Figure 9. CAPEX (Left) and OPEX (Right) of wireless broadband access networks in the Yauli County for a 10-year period using the WiFi, Extended WiFi, TVWS, WiMAX and LTE technologies.

Figure 10. (Left) CAPEX including annuitized deployment upfront cost. (Right) Deployment and operation cost using the annuitized upfront cost of wireless broadband access networks in the Yauli

County for a 10-year period.

Figure 11. CAPEX (Left) and OPEX (Right) of wireless broadband access networks in the Samugari County for a 10-year period using the WiFi, Extended WiFi, TVWS, WiMAX and LTE technologies.

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Figure 12. (Left) CAPEX including annuitized deployment upfront cost. (Right) Deployment and operation cost using the annuitized upfront cost of wireless broadband access networks in the

Samugari County for a 10-year period.

Cost per User per Month

This metric is used to estimate the deployment and operation cost per month of the access networks per each customer or household that the network provides broadband service. We conducted the network design and engineering cost analysis based on our broadband demand and yearly demand growth estimation (see section 3.2 Demand Estimation). For the Yauli area, at deployment, the network provides fixed broadband service to 150 customers in 27 towns. At the end of the 10-year period after deployment, the service extends to 1205 customers. For the Samugari area, at deployment, the network provides service to 301 customers in 29 towns. At the end of the 10-year period, the network serves 2175 customers. Figure 13Error! Reference source not found. and Figure 14 show the cost per user per month for the Yauli and Samugari areas, respectively. In Yauli, at deployment and along the 10-year period after deployment, Extended WiFi provides the lowest cost; closely followed by LTE, and then WiFi, WiMAX and TVWS. In the FITEL projects, the government sets an initial regulated Internet service price of around USD 12. Assuming if the government allows a rate increase to USD 16 in the next few years after deployment, by year 5 an operator using the Extended WiFi technology can cover the operation cost of the network from the revenue. In Samugari, at deployment and along the 10-year period after deployment, Extended WiFi provides the lowest cost; closely followed by LTE, and then WiFi, WiMAX and TVWS. Under the same regulated rate, USD 12, and assuming the same rate increase to USD 16, in Samugari, by year 6 an operator using the Extended WiFi technology can cover the operation cost from the revenue.

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Figure 13. Cost per User in the Yauli area.

Figure 14. Cost per User in the Samugari area.

Net Present Value of the Cost per User per Month

Figure 15 shows the NPV of the cost per user per month for the two case study areas that helps to compare and identify the most economical option based on this cost indicator.

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Figure 15. NPV of the cost per user per month for the Yauli (Left) and Samugari (Right) case study areas.

Cost per Mbps per Month

This metric is used to estimate the deployment and operation cost per month of the access networks per Mbps (capacity) that the network provides. We estimated the capacity that each technology provides in the access network using the average channel capacity for the base stations, or access points, and the number of them in the network. Our capacity estimation includes data throughput excluding overhead for each of the technologies. Figure 16 and Figure 17 show the cost per Mbps per months for the Yauli and Samugari areas, respectively. In Yauli, LTE is the lowest cost at deployment, closely followed by Extended WiFi and WiFi. TVWS and WiMAX are the highest cost. In the 10-year period after deployment, WiFi, Extended WiFi and LTE present the lowest cost per Mbps in different years. In Samugari, LTE also presents the lowest cost at deployment and for the 10-year period after deployment. WiFi and Extended WiFi follow LTE at deployment and during the 10-year period after it. WiMAX and TVWS are the most expensive technologies at deployment and during the 10-year period after deployment.

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Figure 16. Cost per Mbps per Month in Yauli.

Figure 17. Cost per Mbps per Month in Samugari.

Net Present Value of the Cost per Mbps per Month

Figure 18 shows the NPV of the cost per Mbps per month for the two case study areas that helps to compare and identify the most economical option based on this cost indicator.

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Figure 18. NPV of the cost per Mbps per month for the Yauli (Left) and Samugari (Right) case study

areas.

5. Summary

The first section of the paper introduces the current broadband environment and Peruvian government initiatives towards the “National Plan for the Development of Broadband”. This plan is currently undergoing the deployment of a national fiber optic backbone, which will reach 22 states and 180 provinces in Peru and is expected to be completed by the end of 2015. The next step is to plan and deploy access networks to reach final customers. Towards this next step, the government set bids for the deployment of transport and access networks initially in four states; in the coming years, bids will be launched for the other 18 states. In meetings held in Peru with government agencies and private broadband service providers at the end of 2013, officers and executives stated that there was still uncertainty about the best technology fit to carry out broadband deployments, especially in rural areas in the Andes and Amazon Regions. In this context, we introduced our research question: “What is the technology roadmap for introducing broadband services to underserved and unserved areas in the Andes and Amazon regions of Peru?” To address this research question, we conduct a technical and engineering cost analysis using a quantitative approach. We analyze current wireless broadband technology candidates for access networks such as WiFi, WiMAX, TVWS and LTE.

In the second section we present the case study areas and demand estimation. We selected two case study areas that had low broadband penetration, were located in rural remote areas and harsh landscape settings. These conditions help to analyze and compare the different technologies in challenging environments. We selected the Yauli County in the Central Andes of Peru in the State of Huancavelica and the Samugari County in the Western Amazon of Peru in the State of Ayacucho. The service coverage criteria included, first, coverage defined by FITEL in the projects or government bids “Broadband Deployment for Integrated Connectivity and Social Development”, and second, additional coverage based on our analysis of other potential markets in the case study areas. The service coverage includes providing broadband service to 27 towns in Yauli with a total estimated population of 17,338 and 3,511 households. In Samugari, the coverage includes 29 towns with a total estimated population of 16,909 and 3,884 households. Based on the number of towns, population and households, both study areas seem to be similar; however, the Samugari study areas is 10 times larger than the Yauli study area. The technical and engineering cost analysis show the impact of the geography

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(Mountainous region in Yauli and Rainforest region in Samugari) and terrain on the network design and cost analysis. For the demand estimation, we used fixed Internet penetration data for all states and regions of Peru. We used the fixed Internet (broadband and narrowband) penetration as a proxy for fixed broadband penetration based on the reduced number of narrowband users in recent years in Peru. In addition to the fixed Internet penetration, we used the fixed Internet service price and average monthly income to generate a fixed Internet demand curve. Based on this curve, we estimate that the initial fixed broadband demand in Yauli will be 2.9%, and in Samugari will be 2.7% in 2015. We also estimated a yearly growth of 2.7% and 3.7% for Yauli and Samugari, respectively. The broadband service speed to be provided to government and residential customers is 2Mbps with an overbooking rate of 10:4 for government customers and 10:1 for residential customers.

In section 3, we present the wireless broadband access network design that is the technical analysis for the deployment and operation of the access networks using the technology candidates (WiFi, WiMAX, LTE and TVWS). The WiFi technology analysis includes a conservative design approach (WiFi), and an extended design approach (Extended WiFi). For the network design and propagation analysis we used the software EDX Signal Pro. Inputs for the network design were the service coverage, estimated yearly demand, broadband speed, technical specifications of network (backhaul and base stations or outdoor access points) equipment and CPE, and spectrum regulation in Peru. The wireless access network design includes an initial deployment stage and network upgrades and expansion to handle the demand growth in a 10-year period after deployment.

Section 4 presents the engineering cost analysis, which uses a bottom-up approach. We introduce our engineering cost model and the main categories, and cost data sources. We used the software Analytica to develop the engineering cost model and to conduct the engineering cost analysis. Then we present the engineering cost results and findings:

Yauli County – Andes study area:

Extended WiFi has the lowest NPV of the total cost of the deployment and operation for a 10-year period.

Extended WiFi has the lowest NPV of the cost per user per month.

LTE has the lowest NPV of the cost per Mbps per month.

Samugari County – Amazon study area:

Extended WiFi has the lowest NPV of the total cost of the deployment and operation for a 10-year period.

Extended WiFi has the lowest NPV of the cost per user per month.

LTE has the lowest NPV of the cost per Mbps per month.

6. Conclusions

Lowest Cost Option Overall for Wireless Access Network Deployments

The NPV of the deployment and operation and maintenance costs indicate that Extended WiFi is the lowest overall cost option for the two case study areas. LTE follows closely with a NPV cost that is 6.3% more expensive in Yauli, and 6.1% more expensive in Samugari. The low cost of

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WiFi network equipment (outdoor access points) and CPE provides significant cost efficiencies for the rollout and operation of broadband networks. In the Extended WiFi design, we stretched the traditional WiFi transmission distance limit based on reports and studies of long distance WiFi deployments (commercial and private networks) in rural areas [22][23][24]. However, we designed the Extended WiFi networks far from reaching reported maximum distance limits. Given this, government agencies (i.e., FITEL) and broadband service providers seeking for the lowest overall cost option to deploy and operate wireless broadband access networks should consider the Extended WiFi technology to deliver broadband service in the range of 2Mbps per user. As we discuss below, whether or not this near-term step represents the best long-term step in the broadband roadmap remains an open question for our future work.

Total Cost of Deployment and Operation Over Time

LTE has the lowest upfront deployment cost in the two case study areas. LTE achieves the lowest deployment cost due to a combination of lower infrastructure, network equipment, and CPE costs in comparison to the other options. The lower infrastructure cost arises due to LTE base station equipment that allows comparatively wider coverage per cell site than WiMAX base stations or WiFi outdoor access points, and as a result, requires a smaller number of nodes or sites. Regarding LTE network equipment cost, in recent years LTE vendors introduced micro-, pico- and femto-cell base stations at competitive prices to WiMAX, leading LTE deployments in more rural markets. Additionally, increasing adoption and penetration of the LTE technology in urban areas in developed and developing countries led to a decrease in LTE end user equipment prices. This balance between wider coverage per base station and lower network equipment and CPE costs allowed LTE to provide the lowest overall deployment cost.

Extended WiFi has the lowest operation and maintenance cost in the two case study areas over the 10-year study period after initial network deployment. The main cost components within this category are preventive and reactive maintenance, which vary with the cost of the deployed network and the CPE (as established by the annual broadband demand growth). Extended WiFi achieves the lowest operations and maintenance cost due to the reduced amount of network infrastructure (due to the larger cell sites as compared to regular WiFI) and the low prices of high-volume outdoor access points and CPE equipment.

TVWS is similar to WiFi and WiMAX in terms of network deployment cost (TVWS is number 4 in Yauli and number 2 in Samugari) due to the low number of sites in comparison to the other technologies; however higher network equipment and CPE prices reduce the advantage of the comparatively larger TVWS cell sizes. The low-volume TVWS equipment prices also drive up the operation and maintenance cost, positioning this technology with the highest cost. According to equipment vendors, TVWS CPE prices may significantly drop in the coming years, even perhaps following the price drop trend of WiFi as this technology became popular over the past decade. WiMAX deployment cost is comparable with WiFi and TVWS (WiMAX is in mid ranking in Yauli and number 4 in Samugari) due to a combination of coverage and competitive base station price; however, the higher price of WiMAX CPE equipment drives up the operation and maintenance cost.

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Lowest Bandwidth Cost Option

Based on a cost per Mbps per month metric, LTE has the lowest bandwidth cost in Yauli and Samugari. For this calculation, we used the average channel capacity that depends on the data rate (which also depends on the distance between base station and CPE, modulation, channel width and number of streams) and the probability that such data rates occur in the coverage area. Technologies that have high data rates (such as WiFi, WiMAX and LTE) mostly provide the highest average channel capacity. Then, dividing the total deployment and operation costs by the total average channel capacity in Mbps provides the bandwidth cost. The high data rates and competitive deployment and operation costs of LTE made it the lowest bandwidth option for the two case study areas.

In our analysis, we assume a static broadband speed over the 10-year period based on the FITEL’s broadband service requirement of 2Mbps for this period. However, given historical increase in broadband speeds over time in currently served areas, a static speed is not enough to characterize broadband service demand over this period of time. Future work will include changing the network design and cost analyses to reflect incremental growth in broadband speed from 2Mbps up to 10Mbps per household for each of the wireless options. This analysis will identify how well each of the wireless technologies can evolve to increase broadband speed over time and also identify the incremental costs for adding speed to the network over time.

Implications for Feasibility

The cost per user per month helps broadband service providers to determine the economic feasibility of successfully building and operating a wireless broadband access network. Extended WiFi has the lowest cost per user in our two case study areas using the NPV of the cost per user per month at deployment and after a 10-year period. Assuming a broadband service price of USD 16 in the two study areas after a few years from the network deployment (FITEL sets an initial regulated Internet service price of around USD 12), service providers using Extended WiFi are able to cover operation and maintenance costs in year 5 in Yauli and in year 6 in Samugari. In Yauli, other technology options such as LTE and WiFi allow operators to cover operation and maintenance cost one and two years later than Extended WiFi, respectively; TVWS and WiMAX allow operators to cover operation and maintenance costs at year 9 and 10, respectively. In Samugari, other technology options such as LTE and WiFi allow operators to cover operation and maintenance cost one year later than Extended WiFi; TVWS and WiMAX allow operators to cover operation and maintenance costs at year 10.

Then, in the case of a broadband service provider deploying and operating an Extended WiFi network, government subsidies should focus mainly on the initial deployment and first years of the network operation; 5 years in Yauli and 6 years in Samugari.

Broadband Technology Roadmap

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The results of the technical and engineering cost analysis show that Extended WiFi has the lowest NPV for the deployment and operation cost for a 10-year period of a wireless access network to introduce broadband services at 2Mbps in the two case study areas in the Andes and Amazon regions. LTE has the second lowest cost on this metric. Using other metrics, Extended WiFi has also the lowest cost per user per month in the two areas (closely followed by LTE); and LTE (followed by WiFi) have the lowest cost per Mbps per month in the Andes and Amazon areas. Based on these results, for a technology roadmap that targets to minimize wireless access network cost (since government bids are set to minimize cost), Extended WiFi and LTE are the best fit to introduce broadband services at 2Mbps to unserved and underserved rural areas in the Andes and Amazon regions of Peru. However, given historical increase in broadband speeds over time in currently served areas, we need to conduct further analysis in additional scenarios which include increasing speed, demand, and cost of different CAPEX and OPEX categories, among others. Our initial analysis for introducing a 2Mbps broadband service provides the best fit for the initial years but further analysis will help to determine the technology roadmap for the 10-year period.

For the initial deployment and operation of wireless access networks, since one of the main differentiators for carrying out deployments using either Extended WiFi or LTE is the use of unlicensed or licensed spectrum; advantages and disadvantages, as well as availability, of using this spectrum need also to be addressed to determine the best fit.

In 2008, the Ministry of Transport and Communications (MTC) of Peru allocated the 900MHz, 2.4GHz, 5.4GHz and 5.8GHz to public telecom services in rural or underserved areas [28]. This allocation aimed to promote deployment of Internet networks in rural areas; in this way broadband service providers can serve these areas without requiring any spectrum license or authorization. In our analysis, we use Extended WiFi in one of these bands, the 2.4GHz band (which has 3 non-interference channels), to connect the outdoor access points to customers or households. A constraint that using this unlicensed band might face is saturation and interference from other WiFi equipment. In the case of having only one service provider there will not be interference from network equipment from another provider; however due to the introduction of residential broadband services, customers are likely to install home indoor WiFi equipment to extend service to different home users and devices, which might become a source of interference. It is difficult to estimate when interference will become a problem for the service provider and customers; however, service providers must plan for alternatives to mitigate the negative effect of this interference.

Because LTE operates in licensed bands, access networks using LTE are not likely to face interference neither from other service providers nor from home devices. In 2013, the Peruvian government auctioned and granted 2 blocks (40MHz each block) of the 1.7/2.1GHz band to provide 4G LTE services for USD 105.51 Million and USD 152.23 Million [29]. These were nation-wide licenses with a regulation that stated that service providers cannot acquire more than one LTE block. Other operators have also announced launching LTE services in other bands such as 1900MHz. Additionally, in the near future, the government is planning to auction more LTE bands including frequencies bellow 1GHz. Even though most of the LTE licenses are planned for providing mobile broadband services in main urban areas using macro cell sites, our study

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shows that LTE is also a low cost option to introduce fixed broadband services in rural areas, which are markets usually underestimated by operators.

Based on the cost metrics and spectrum considerations, a recommended solution for introducing broadband services in underserved and unserved rural areas in the Andes and Amazon regions of Peru is to initially deploy and operate wireless access networks using the Extended WiFi option which might not face interference in the first years. The service provider must implement inexpensive software mechanisms to monitor interference and saturation in the sites. Then, if interference starts becoming an issue, the service provider can start deploying in some sites the LTE option for new users reusing the already deployed infrastructure. Since LTE was the second lowest total overall cost and second cost per user per month after Extended WiFi, carrying out an initial Extended WiFi deployment followed by LTE, reusing the infrastructure, will ensure the lowest cost for the introduction of broadband services in these rural areas.

7. Future Work

Future work consists of conducting sensitivity analyses on the engineering cost model for the two case study areas and wireless technologies. The analysis will address performance and cost impact (incremental cost) of increasing speed, coverage, demand, cost of different capex and opex categories, among others. These analyses will help to determine the broadband technology roadmap for the 10-year period. Future work also includes analyzing new technologies such as millimetric-wave, gigabit satellite, balloons and drones.

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