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# Techno-economic Analysis Highlighting Aspects of 5G Network Deployments at Railway Environments

Ioanna Mesogiti¹(✉) , Eleni Theodoropoulou¹, Fotini Setaki¹,

George Lyberopoulos¹, Konstantinos Stamatis², Panteleimon Konstantinos Chartsias², Nikos Makris³, Paris Flegkas³, Jesús Gutiérrez⁴, Christina Politi⁵, Christos Tranoris⁵, Markos Anastasopoulos⁶, and Anna Tzanakaki⁶

¹ COSMOTE Mobile Telecommunications S.A., Athens, Greece
`imesogiti@cosmote.gr`

² Intracom Telecom S.A., Peania, Greece

³ Department of Electrical and Computer Engineering, University of Thessaly, Volos, Greece

⁴ IHP - Leibniz-Institut Für Innovative Mikroelektronik, Frankfurt, Germany

⁵ University of Patras, Patras, Greece

⁶ National and Kapodistrian University of Athens, Athens, Greece

**Abstract.** 5G and beyond networks will comprise versatile infrastructures consisting of multiple disaggregated pools of network, compute and storage resources, while network deployments are expected to appear in various (physical/vertical) environments as access network extensions of public networks or as Non-Public-Networks. In many physical/vertical environments, the necessary network deployments may be very dissimilar to the wide area public network ones, raising new deployment challenges. Such cases can be the railway environment or specific deployments along rivers or roads. In these cases, the various network deployment alternatives, in terms of technologies and topologies, shall take into account various factors such as area specifics, technologies' deployment feasibility, traffic/usage forecasts considering long-term services roadmaps and, certainly, the associated costs. In this context, network planning and dimensioning shall be tightly accompanied by the techno-economic analysis of the various deployment alternatives. This paper provides insights of 5G network deployments at railway environments as retrieved through macroscopic techno-economic analysis and demonstrates their applicability on the architectural concepts of the 5G-PPP 5G-VICTORI project.

**Keywords:** 5G · Railway Vertical · Transport Network · Wireless-Optical Convergence · Techno-economic · Fronthaul · Backhaul

## 1 Introduction

5G and beyond access network deployments are expected to coexist with physical environments that belong to vertical industries as network extensions of public networks or as Non-Public-Networks (NPNs). In many cases, these upgraded environments may
be very dissimilar to the wide area coverage deployments of existing public networks, raising new challenges in the deployment of the networks.

At the same time, it is a fact that 5G and beyond network services will pose stringent coverage and data rate requirements to access network segments. For transport network segments it will be necessary to support high-capacity Macro Sites (MS), dense layers of high capacity Small Cells (SCs), or/and versatile disaggregated Radio Access Network (Cloud/Distributed/Open RAN) setups. For this purpose, the transport network needs to be equipped with mechanisms to support flexible and scalable RAN deployments, and to converge fronthaul (FH) and backhaul (BH) traffic of various functional splits (FS). This is usually carried out using a common infrastructure that comprises various wireless and optical technologies [1].

A number of research projects have addressed the development of next generation transport network technologies. In this context, the 5G-XHaul [2] and 5G-PICTURE [3] projects proposed converged optical-wireless network solutions capable of supporting flexible splitting options, while the 5G-PICTURE project [3] further aimed at delivering a paradigm shift, by “disaggregating” hardware and software components across domains wirelessly or/and optically interconnected. 5G-VICTORI [4] aims at showcasing and evaluating the applicability of disaggregated 5G network deployments on versatile vertical environments, namely railway and smart cities.

Delivering 5G network deployments to meet the service requirements of railway and smart city environments is not a straightforward task. In practice, many factors need to be considered, such as area specifics, deployment feasibility, long-term service roadmaps, traffic demand and growth patterns/forecasts, as well as infrastructure availability, scaling capabilities and the associated costs. The scaling capabilities and the identification of these costs need to also consider the various deployment phases (over time), in order to estimate the critical, high-cost factors and to extract deployment guidelines at early network planning stages. Performing techno-economic analyses is an equally complex task, the underlying reasons being many and versatile depending on the scope, scale, system, technologies in focus, etc. An overview of the versatility of techno-economic analyses in the 5G era is provided in [5].

This paper proposes a methodology for the techno-economic evaluation of large-scale 5G and beyond network deployments, aiming to identify cost optimizations and to investigate various technological aspects and critical parameters at early stages towards delivering economically viable and sustainable deployments. The methodology is based on previous studies' fully parameterized expandable Techno-Economic analysis tools [6, 7, 14] that reflect the concepts of 5G deployments. The tool has been applied in the case of real network deployments at a railway environment, and the results confirm their capabilities and usage potential.

The paper is organized as follows: Sect. 2 presents a methodology and a tool for performing techno-economic analyses in the context of 5G and beyond networks. In Sect. 3, the beyond 5G architectural principles are applied to the railway vertical environment. The next section presents an application of the tool in modeling and dimensioning an indicative (though realistic) railway vertical facility deployment, followed by cost assessment of various deployment scenarios. A number of network deployment
alternative scenarios are identified and evaluated from a techno-economic perspective.
Finally, conclusions are drawn.

## 2 Techno-economic Analysis Methodology and Tool

In the context of 5G-VICTORI, a methodology for performing technoeconomic analysis of 5G deployments has been implemented [11] (leveraging [6]) as a fully parameterized tool tailored for vertical network deployments. These allow the estimation of the critical, high-cost factors and the extraction of deployment guidelines at early network planning stages through comparative analysis of multiple deployment scenarios. The innovative nature of the methodology and the tool lies in the modeling of versatile 5G network technologies and architectural/ deployment options while considering the railway vertical premises specificities, as well as rules and assumptions resembling usage/operation conditions in the most realistic way. The methodology that was implemented with the techno-economic analysis tool (see Fig. 1) comprises the following steps:

1. Definition of the vertical premises: coverage area (as distance or as surface) and modelling of the vertical premises environment specifics, in this case considering the deployment at a railway area along the tracks and on platforms.

2. Definition of the service scenarios in terms of coverage area/routes, traffic demand to be served, services to be provided etc. and their scaling over years.

3. Definition of the access and transport deployment blueprint, providing a general model of alternative RAN/BH/FH/MEC (Mobile Edge Computing)/5G Core Network (5G CN or CN) options that can co-exist in a deployment.

4. Definition of the scaling rules for each technology and the dimensioning rules for each segment. In practice this includes:

a. RAN dimensioning: as access network nodes' elements calculation based on capacity and coverage increase over years; considering access radio units capacity, gNB (5G nodeB) disaggregation model, etc.

b. Transport segments dimensioning: as capacity and required links calculation considering various FS, various transport aggregation levels, various transport technologies link capacity/range/hops, etc.

c. Edge segment dimensioning: as compute resources calculation considering the application services and Network Functions requirements, loading factors, etc.

d. Core segment placement and dimensioning of UPFs (User Plane Function) to serve the Edges.

5. Definition of the cost information and cost scaling rules for all elements that are modelled/analyzed.

6. Definition of deployment scenarios in terms of technologies, including usage of various FS, wireless/optical technologies, MEC, RAN disaggregation, etc.

7. Application of common Cash Flow Model, which leads to automatically calculated Capital Expenses/Operational Expenses (CAPEX/OPEX) breakdown, as well as the Total Cost of Ownership (TCO) estimation, on per technology/equipment/segment basis. In practice, CAPEX/OPEX breakdown and TCO are calculated, taking into account, equipment- and fiber deployment- specific costs, labor costs, operations and maintenance, as well as additional financial figures such as Weighted Average Cost of
Capital (WACC) and Tax Deduction (%equipment cost). The Yearly Total Cost (YTC) has been calculated by harmonizing the sum - each CAPEX has to be annualized, splitting the investment by the appropriate amortization period (AP):

$$YTC = \sum_{i=1}^{N} \frac{CAPEX_{i}}{AP_{i}} + \sum_{j=1}^{M} OPEX_{j}$$

where CAPEXᵢ and OPEXⱼ are the i-th component and j-th component of CAPEX and OPEX respectively.

8 Iteration of all steps for the various scenarios and collection of analytical, comparative cost results, in order to identify key factors influencing cost and extract deployment recommendations.

<figure><img src="image_3.png" /><figcaption>Fig. 1. 5G-VICTORI Techno-economic Analysis Inputs and Outputs</figcaption></figure>

# 3 5G Vertical Deployment Aspects

## 3.1 5G-VICTORI Deployment Blueprint

5G-VICTORI has adopted the infrastructure disaggregation concepts and the baseline principles of the physical and logical/functional architecture of 5G networks. More specifically, the 5G architecture considers an integrated optical and wireless network topology and infrastructure to support jointly backhauling of SCs/MSs and fronthauling of various FSs (as defined in [8]) of Remote Radio Units (RRUs). The access transport links are aggregated at various levels using optical network technologies and the traffic is forwarded to the backbone optical transport network. In particular, we distinguish the following transport network segments:

* Transport access, providing connectivity from the access network nodes to the 1st level aggregation sites;
* Transport aggregation, aggregating transport access links at COs (Central Offices);

* Transport core, providing connectivity between COs to the CN.

Compute resources are present at various physical and logical locations of the network for hosting applications and/or network services such as Virtual Base Band Units (vBBUs) [12]. These are indicated as edge (close to the location of or collocated with a MS), (at COs), or central clouds (at CN).

Leveraging these concepts, the project has proposed three deployment options for the case of the railway vertical network [9]:

* Option 1: NPN (with autonomous edge): that considers a silo 5G CN deployed at central premises of the vertical facility.

* Option 2: NPN with distributed data plane – multiple edges: including a 5GCN deployed at central premises of the vertical and several UPFs and application servers deployed at a number of specific sites of the vertical facility.

* Option 3: Fully disaggregated 5G architecture: including a 5G CN deployed at central premises of the vertical, UPFs and application servers deployed at a number of sites of the vertical facility, and disaggregated RAN segment.

The principles and these deployment options are reflected in a general deployment blueprint that is modelled with the techno-economic tool. The blueprint includes also the support of multiple hierarchical transport network comprising the aforementioned three segments, i.e. the access, the aggregation and the core transport segment.

To model the case of the extension of access network nodes on-board the trains [10], the blueprint considers the option of last-mile transport segment. The overall blueprint that has been used in 5G-VICTORI techno-economic analysis is depicted in Fig. 2.

<figure><img src="image_4.png" /><figcaption>Fig. 2. 5G-VICTORI Physical Architecture Blueprint</figcaption></figure>
## 3.2 Network Modeling, Dimensioning and Deployment Aspects

**Access Network Deployment Dimensioning.** The RAN deployment at railway vertical premises, will comprise (1) a dense layer of small range access network nodes (RRUs and Small Cell gNBs) to serve the high traffic demand at specific hotspot areas of the vertical premises, and (2) a macro layer of access network nodes for maximizing coverage over the wider vertical premises area. The RRU and gNBs are modelled by the cell capacity, the cell data rates expected at the cell-edge (deriving from communication service-driven radio network planning), and the expected loading factor. For the RRUs and gNBs various FS options are considered. The access network deployment dimensioning is based on service driven radio network planning and forecasts of traffic demand density – especially over a hotspot area.

**5G Transport Network Deployment Modeling.** The 5G data plane architecture considers an integrated optical and wireless network topology and infrastructure to support jointly BH of gNBs, and FH of various FSs of RRUs to BBUs. The 5G-VICTORI transport access deployment options are related to the usage of different FSs (ranging from eCPRI (evolved Common Public Radio Interface) A to E options) [13], to the usage of various wireless and optical technologies and other deployment specificities.

In particular, mmWave (millimeter Wave) and Sub-6 links are considered for BH and/or FH providing ~1 Gbps (Gigabits/sec) average data rates per link; depending highly on the spectrum (frequency, bandwidth) and the clutter. MmWave links are implemented by pairs of transceivers, while links' capacity scales by adding new pairs of transceivers and/or by adding capacity software keys up to the 10Gbps per transceiver depending on the equipment specifications. Sub-6 links scale by adding new pairs of transceivers. The wireless transport connections may consist of one or multiple hops.

P2P (Point-to-Point) Optical Ethernet connections for BH/FH links at 10 Gbps are also included as an alternative technology in the model. Scaling of these links is performed by adding interfaces and connecting them to Optical Ethernet switches.

These access transport links are aggregated at 1st level at COs (edge sites), and then at 2nd level at a Main Vertical Premises site where the CN resides. At 1st aggregation level we consider mmWave hubs aggregating up to 10 links, Elastic Time-Shared Optical Network (TSON) [9] components aggregating up to 10 links and Optical Ethernet switches aggregating up to 48 P2P Optical Interfaces with the limitation of 100 Gbps aggregate capacity in two 100 Gbps ports. We consider TSON components to be used at transport core segments to aggregate, at 2nd level, (Optical) Ethernet interfaces with flexible degrees of aggregation (reaching aggregation of 12 × 10 Gbps interfaces to 100 Gbps optical Ethernet) [9].

For all the transport segments, other deployment specifics are modelled, such as the maximum link utilization, the loading factor for the aggregation nodes interfaces, the existence of fiber deployment – Greenfield (GF)/Brownfield (BF) scenarios, etc.

**Usage and Placement of vBBUs and MEC.** Compute resources are considered at various physical and logical locations of the network for hosting applications and/or network services such as vBBUs and UPFs. These are indicated as Edge Compute located at COs. Similarly, to large scale deployments' common practice, it is assumed that COs host the transport aggregation equipment.
By modifying the deployment options and the relevant model parameters, different access network, access transport and aggregation transport deployment scenarios can be defined, dimensioned and evaluated.

# 4 Techno-economic Evaluation of 5G Network Deployments

The stepwise methodology was used to perform a techno-economic assessment for a 5-year timeframe of the various network deployments at a railway vertical premises in the Athens-Patras railway area. As example deployment area we consider the total distance of the railway tracks between the two main stations (ends in Athens and Patras), and the surface of all platforms between these two ends. Traffic and service demand is facilitated by additional information such as the number of platforms, the number of trains that are simultaneously in operation, the number of passengers per train, etc. The vertical facility characteristics are summarized in Fig. 3.

In the railway area under study, we consider 100% coverage along the tracks and at the platforms from the 1st year, and a cell overlapping factor of 10%, given the criticality of train automation services. The traffic demand is considered initially as 1.5 Gbps peak on train reaching 4 Gbps peak on train the 5th year, and initially 3 Gbps peak data rate at each platform reaching 10 Gbps at each platform the 5th year.

<table><caption>Fig. 3. Railway Vertical Facility Assumptions</caption><thead><tr><th colspan="2">Area under study</th><th>Railway tracks</th></tr></thead><tbody><tr><td rowspan="2">Railway Area</td><td>Km of tracks</td><td>208000</td></tr><tr><td>#Platforms</td><td>15</td></tr><tr><td rowspan="2">Platforms</td><td>Platform Area m²</td><td>12000</td></tr><tr><td>Total Platform surface</td><td>180000</td></tr><tr><td rowspan="7">Trains</td><td># Trains in operation per day</td><td>10</td></tr><tr><td>Increase of #trains</td><td>5%</td></tr><tr><td>#Trains Operating/ Total #Trains</td><td>90%</td></tr><tr><td># Passengers per Train</td><td>300</td></tr><tr><td>Total Passengers = Users *2</td><td>16000</td></tr><tr><td>Average Users per train</td><td>417</td></tr><tr><td>Average MS radius (m)</td><td>1000</td></tr><tr><td rowspan="3">Deployment related</td><td>Average SC radius (m)</td><td>70</td></tr><tr><td>Average distance to cabinet (m)</td><td>13900</td></tr><tr><td>#COs</td><td>15</td></tr></tbody></table>

## 4.1 Techno-economic Analysis Scenarios

For the techno-economic analysis, multiple network deployment scenarios were analyzed along the lines of the afore-described network modeling capabilities (summarized in Table 1). Scenario Set 1 considers the level of existing fiber deployment and usage of
various FS (Set 1). We consider that fiber deployment is already existing at platforms towards ISPs. However, not all platforms are directly interconnected via fiber. Scenario set 1 BF options refer to the existence of fiber deployment along the tracks for 100% to 50% of the MSs deployed along tracks. This set assumes GF vs BF scenarios in pure optical deployments. Scenario Set 2 analyses the level of usage of optical vs wireless technologies at various transport aggregation segments and for various FS.

<table><caption>**Table 1.** Deployment Scenario Sets under evaluation.</caption><thead><tr><th colspan="4">Optical Transport Deployment (Set 1 Scenarios)</th></tr></thead><tbody><tr><td rowspan="3">BF - 100</td><td rowspan="3">Existing fiber deployment @ Platforms: 100%<br>Existing fiber deployment along tracks: 100%</td><td>eCPRI A</td></tr><tr><td>eCPRI IID/IU</td></tr><tr><td>eCPRI E</td></tr><tr><td rowspan="3">BF - 50</td><td rowspan="3">Existing fiber deployment @ Platforms: 100%<br>Existing fiber deployment along tracks: 50%</td><td>eCPRI A</td></tr><tr><td>eCPRI IID/IU</td></tr><tr><td>eCPRI E</td></tr><tr><td rowspan="3">GF</td><td rowspan="3">Existing fiber deployment @ Platforms: 100%<br>Existing fiber deployment along tracks: 0%</td><td>eCPRI A</td></tr><tr><td>eCPRI IID/IU</td></tr><tr><td>eCPRI E</td></tr></tbody></table><table><thead><tr><th colspan="4">Transport Network Technologies Mix (Set 2 Scenarios)</th></tr></thead><tbody><tr><td rowspan="3">Only Optical</td><td rowspan="3">Access Transport: P2P optical<br>Aggregation Transport: Optical Ethernet Switching 50% &amp;<br>TSON 50%<br>Optical Scenarios BF - 100%, BF - 50%, and GF correspond to<br>Scenarios of Set 0</td><td>eCPRI A</td></tr><tr><td>eCPRI IID/IU</td></tr><tr><td>eCPRI E</td></tr><tr><td rowspan="3">Only Wireless</td><td rowspan="3">Access Transport: Wireless:<br>Along Tracks: 30%Sub-6, 20% + 50% mmWave,<br>At Platforms: 80%Sub6, 20%mmWave<br>Aggregation Transport: mmWave 50% &amp; TSON 50%</td><td>eCPRI A</td></tr><tr><td>eCPRI IID/IU</td></tr><tr><td>eCPRI E</td></tr><tr><td rowspan="3">Optical-Wireless</td><td rowspan="3">Access Transport Wireless:<br>Along Tracks: 20%Sub-6, 30% + 10% mmWave, 40% P2P<br>optical<br>At Platforms: 40%Sub6, 20%mmWave, 40% P2P optical<br>Aggregation Transport: mmWave 40%, Optical Switching 20%,<br>TSON 40%</td><td>eCPRI A</td></tr><tr><td>eCPRI IID/IU</td></tr><tr><td>eCPRI E</td></tr></tbody></table><table><thead><tr><th colspan="4">Edge Offloading Options (Set 3 Scenarios)</th></tr></thead><tbody><tr><td rowspan="3">Decentralised</td><td rowspan="3">vBBU Processing at Edge: 100%<br>Application offloading at Edge: 70%</td><td>eCPRI A</td></tr><tr><td>eCPRI IID/IU</td></tr><tr><td>eCPRI E</td></tr><tr><td rowspan="3">Completely Centralised</td><td rowspan="3">Completely Centralised Deployment (vBBUs and App.<br>Processing at Main site)</td><td>eCPRI A</td></tr><tr><td>eCPRI IID/IU</td></tr><tr><td>eCPRI E</td></tr><tr><td rowspan="3">Only vBBU Processing</td><td rowspan="3">vBBU Processing at Edge: 100%<br>App. Offloading at Edge: 0%</td><td>eCPRI A</td></tr><tr><td>eCPRI IID/IU</td></tr><tr><td>eCPRI E</td></tr></tbody></table>
Scenario Set 3 deals with the level of decentralization of traffic and Network Functions (Set 3). We consider edge deployment at the railway platforms, with scenarios ranging from: (1) completely decentralized (vBBU processing and application offloading is performed at Edge – only internet traffic is routed via a main UPF node); to (2) partially decentralized (only vBBU processing at Edge while all traffic is served by a central UPF); to (3) completely centralized (all vBBU and application traffic processed centrally).

Finally, a 4th scenario analyses the deployment of gNBs or RRUs on poles along the tracks vs the deployment of access network nodes on-board trains; in which case practically, we consider wireless access transport nodes as an additional hop to deployment option 1 (evaluated with Scenario Set 2). We consider FS A/B (following eCPRI split naming). Essentially, we evaluate the cost benefits incurred by the decrease of the access network nodes number for the on-board deployment, and the cost overheads incurred by densification and extension of the access transport along the tracks compared to the trackside deployment; i.e. scenario 4 compared to FS A/B scenarios of Set 2.

## 4.2 Techno-economic Analysis Results

Through the comparative evaluation of costs, broken down per network segment, critical network deployment factors were revealed. The comparative results for Scenario Sets 1–3 are presented in Fig. 4. In particular, the 1st set of scenarios focus on the evaluation of a GF optical network deployment compared to various BF options. The results revealed that the fiber deployment cost is an extremely critical factor of the total cost, although the criticality is very location/deployment dependent, e.g. digging/trenching costs, etc.

Considering the selection of optical-wireless technologies (Scenario Set 2 results), the comparative analysis results revealed that the cost of deployment is highly influenced by the selection of the transport network technologies, by the existence of fiber deployment and by the selection of the FS at gNBs. In practice, in areas where fiber deployment is already existing, using wireless technologies increases the cost significantly from 50% to even 145% depending on the level of wireless technologies usage and on the FS scheme, due to the current capacity vs cost relation of these technologies. However, considering the cases of no fiber deployment, it could be cost efficient to use wireless technologies even with low level FS. In the case of partial existing fiber deployment, the scenarios shall be carefully analyzed, since the cost efficiency of wireless technologies depends highly on the FS scheme.

Considering the BBU processing and application offloading at Edge (Set 3), the results indicated that the centralized or decentralized deployment of vBBU processing have no impact on the TCO for CPRI schemes A to IID, while the TCO increases by 80% for eCPRI E when vBBU deployment is centralized compared to the decentralized case. The impact of application offloading at the edge is also marginal in terms of cost, however this will depend on the estimated traffic generated by the specific applications.

Last but not least, the Track-to-Train network deployment versus that on-board (5G) is evaluated from the techno-economic perspective (Scenario 4). In the Athens-Patras railway case, the cost of the RAN decreases by 76% in the case of the on-board deployment compared to the trackside one, while the cost of the access transport is almost 10 times greater in the case of the on-board deployment compared to that of the trackside.
Scenario Set 1

Cost Overheads

<figure><img src="image_5.png" /></figure>

Scenario Set 2

Comparative Cost Analysis - various Technologies Deployment Scenarios (Transport)

<figure><img src="image_6.png" /></figure>

Scenario Set 3

Comparative Cost Analysis between various Offloading Scenarios

<figure><img src="image_7.png" /><figcaption>Fig. 4. Techno-economic analysis results</figcaption></figure>
Considering the total costs of the Track-to-Train network deployment versus that on-board (5G) (Scenario 4 versus Scenario set 2), CAPEX is almost doubled, and OPEX is also increased significantly in scenario 4. However, eventually, the TCO in the pure on-board deployment is increased only by 50% compared to the trackside deployment. The access transport cost overhead depends highly on the radio emission characteristics of the equipment and the clutter along the tracks. Thus, in real network deployments, this analysis shall be carefully performed for the different wireless access transport equipment alternatives.

## 4.3 Overview of Results

A Techno-economic analysis is useful for quick and coarse evaluation of a large number of scenarios of network dimensioning and rollout. Analysis of longer-term scenarios can also provide insights related to future-proofness of deployment and to securing investments. The more accurate the pricing information, the more accurate and valuable the results of the analysis. To this end, the cost and cost models of the equipment are critical factors, making analysis highly sensitive to the cost assumptions, which are highly influenced by the Business-to-Business agreements, the target equipment volumes, the maturity level of the product, etc. However, unitary equipment costs cannot provide safe conclusions about network deployment. Instead, equipment cost and scaling information shall be associated also with 5G network deployment options such as the usage of MEC, the use of various FS and the placement of associated vBBUs. Apparently, the deployment environment and restrictions should also be taken under consideration. For this purpose, techno-economic tools capable of modelling such technical aspects prove to be useful.

In the indicative railway vertical case under study, the exemplary cost analysis has revealed the following:

* The fiber deployment along the tracks is a big cost share, so that premises with existing fiber deployments, are commercially advantaged.

* Wireless systems is a valid transport network alternative to optical transport, with the performance and cost efficiency of these technologies increasing over the years – compared to past deployment analyses [6].

* The selection of eCPRI scheme shall come hand in hand with the deployment distribution and technologies selection decision.

## 5 Conclusions

In this paper, we propose a methodology framework and a tool for the techno-economic analysis of 5G and beyond network deployments at vertical facilities. The methodology enables the cost evaluation of various network technologies and deployment options, by modeling, dimensioning and cost-analyzing individually of the various network segments while taking into account a plethora of critical technology-related parameters. Results from the applicability of the tools in indicative railway vertical deployment scenarios provide interesting conclusions on the potential selection of alternative 5G technologies and deployments achieving higher cost efficiency. Flexible by nature, the
tool can be further expanded to include additional technologies, more complex deploy-
ment options as well as business factors, to enable the investigation of cost-efficient high
performance network deployments as well as deeper understanding and familiarization
with the disaggregated network architectures.

**Acknowledgements.** The research leading to these results has received funding from the European Union's Framework Programme Horizon 2020 under grant agreement No. 857201 and project name "5G-VICTORI - VertIcal demos over Common large scale field Trials fOr Rail, energy and media Industries".

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