Ioannis P Chochliouros
email: ichochliouros@oteresearch.gr

Anastasia S Spiliopoulou

Alexandros Kostopoulos

Eirini Vasilaki

Uwe Herzog

Athanassios Dardamanis

Tao Chen

Latif Ladid

Marinos Agapiou

Testbeds for the Implementation of 5G in the European Union: The Innovative Case of the 5G-DRIVE Project

Keywords: 5G, 5G Action Plan (5GAP), 5G Public Private Partnership (5G-PPP), Horizon 2020, enhanced mobile broadband (eMBB), experimental platforms, Mobile Edge Computing (MEC), network slicing, pilot trials, testing and validation facilities, Vehicle-to-Everything (V2X)

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Introduction

Over the last decade the European Union's (EU) electronic communications policy has been effective in delivering more competition, lower prices and more choices for businesses and consumers [START_REF]Communication of the Commission: A Digital Single Market Strategy for Europe[END_REF]. However, the full economic and social benefits of this sort of broader digital transformation will only be achieved if Europe can ensure extensive deployment and reception of very high capacity networks, in rural as well as urban areas and across all of society that will be able to offer innovative facilities and related services in a broad thematic range. One of the central aims of the European Commission's (EC) Digital Single Market Strategy [START_REF]Communication of the Commission: "Connectivity for a Competitive Digital Single Market: Towards a European Gigabit Society[END_REF] was, therefore, to generate the precise environment and conditions for the deployment of advanced digital -very high-capacity-networks.

While "basic broadband" (i.e., of a speed of at least 2Mbps) is available to every European, mainly enabled by legacy infrastructures, this is no longer good enough for the ongoing digital transformation, strongly promoted by the actual European policies and being aligned to relevant international trends affecting both market and society. Transformative solutions based on Internet connectivity (such as cloud computing, Internet of Things, high performance computing, big data analytics etc.) are expected to modify business processes as well as to influence social interactions. New digital applications -like virtual and augmented reality (VR/AR), increasingly connected and automated driving, artificial intelligence-will necessitate the speed, quality and responsiveness that can only be delivered by very high-capacity broadband networks.

Digital technologies support innovation and competitiveness across private and public sectors and enable scientific progress in all disciplines. The proposed Horizon 2020 (H2020) Work Program (WP) 2018-2020 [START_REF]Work Program[END_REF] covers a great variety of modern Information and Communication Technologies (ICT) thus aiming to modify multiple domains, with particular emphasis given among others, to the development of the 5 th Generation of Mobile Communications (the so called as "5G") ( [START_REF] Andrews | What Will 5G Be?[END_REF], [START_REF]European Commission: 5G: Challenges, Research Priorities, and Recommendations -Joint White Paper[END_REF]). By properly pursuing the change initiated under the previous EU WPs, it is expected that related research activities will continue to promote more innovation-orientation to ensure that the EU industry remains strong in the core technologies, being at the roots of future value chains.

However, a suitable strategic approach is essential for the EU so as to realise a clear and leading position in the international "arena" and to obtain any relevant (early) advantage of the new market opportunities promoted and enhanced by 5G, via appropriate validation of the related 5G services and facilities, for the benefit of the "whole" economy and society.

Actions for Tests-Trials at EU Level towards the 5G Promotion

The promotion of the digitisation process in some "key" industrial sectors based on 5G connectivity, as well as the dawn of modern business models, clearly implicate for closer partnerships between the concerned sectors and the telecommunication sector. While a few markets will naturally lead innovation and attract most of the initial investments, a number of sectors recognise the need to "run" pilot trials to increase predictability, reduce investment risks and validate both the technologies and the concerned business models. Experiments are also needed to "provide input" for the standardisation effort towards establishing validated norms and recommendations.

Among the actual priorities of the EC, working interactively with Member States (MSs) and industrial stakeholders/market actors is the voluntary establishment of a common timetable for the launch of early 5G networks (initially scheduled to be operational by the end of 2018) and followed by the launch of fully commercial 5G services in Europe by the end of 2020. According to the 5G Action Plan (5GAP) [START_REF]Communication of the Commission: "5G for Europe: An Action Plan[END_REF], the relevant EU timetable is actually driven by the following key objectives: (i) Promoting preliminary trials, under the 5G-PPP arrangement [START_REF]The 5G Public Private Partnership (5G-PPP[END_REF], to take place from 2017 onwards, and pre-commercial trials with a clear EU cross-border dimension from 2018; (ii) supporting commercial launch of 5G services in at least one major city in all MSs in 2020, and; (ii) encouraging MSs to develop national 5G deployment roadmaps as part of the national broadband plans, with uninterrupted coverage in all urban areas and along main transport paths in 2025.

Many EU cities are already strongly engaged in 5G development, trials and pilots and first commercial deployment will already start in specific EU cities in 2019. The EC EU 5G Observatory website/online platform [START_REF]The European 5G[END_REF] has been officially launched on late of September 2018 and provides independent monitoring information concerning the main actual and likely market developments, 5G trials and other actions taken by MSs, as well as industry stakeholders regarding 5G market introduction in EU and in a global context. This allows assessing the progress of the EU 5GAP and to take actions to fully implement it. The Observatory focuses primarily on developments in EU, along with major international developments that could impact the EU market.

Most of the actual roadmap implementation is both driven and supported by the industry sector, on a private basis. In fact, the core part of the 5G trials and pilots is -and will be achieved-through private trials (commercial and pre-commercial) between network operators and manufacturers/vendors and is increasingly involving vertical stakeholders. Through the present 5GAP, the EC supports the creation of suitable requirements for the intended 5G deployment in Europe, particularly the identification of the related harmonised spectrum and of the regulatory conditions, while supplementary support for trials and pilots is made available from the EC H2020 program in support of the 5G-PPP implementation, specific national programs of MSs as well as from specific programs (such as the European Space Agency (ESA) Satellite for 5G Initiative).

The EC strongly supports pilots and experiments in the course of the 5G growth, though the coordination of the 5G-PPP, via selected 5G trials. The EC counts on the trial results to be able to identify and address specific sectorial policy issues and seek the active support of MSs to resolve them whenever they constitute a major obstacle to high value applications relying on 5G. Furthermore, there is requirement need to guarantee that hardware, terminals (here the term implicates not only for smart phones but also a full range of Internet of Things (IoT) and connected devices (cars, drones, urban furniture, etc.) and devices based on 5G connectivity are available in due time before 2020, to encourage uptake and demand.

Experimental platforms for 4G/5G in Europe are the results of private and public efforts at national and European level. Accelerating trial capabilities and other pilots, the platforms remain subject to continuous efforts targeting the full 5G picture and future evolutions. As such, actual 5G infrastructure deployment roadmap is highly dependent on the capability to deliver relevant and comprehensive set of platforms addressing remaining gaps & challenges. Thus, one should also consider such platforms as valuable and demonstrated set of 5G enablers, beyond the trial objectives. However, complementarity and efficiency of the efforts deserve coordination among the diversity in the documentation of the existing platforms. As a consequence, it is of tremendous importance to describe the matching elements of each platform compared to the complete 5G landscape. For this, a common classification and documentation for 5G platforms addressing different target groups is mandatory. Consistent data structures and unified meta-information like name of the platform, countries where the platform is deployed and additional information on features and capabilities is fundamental. Collections of data provided by platforms have to support answers of diverse stakeholders' research, public sector or industry.

From a high-level perspective, one way to "view" the 5G ecosystem can be in terms of platforms (hardware (HW) and software (SW)), services and use cases). For new platforms and services to be created, key decisions need to be taken, both business-wise and technically. Business cases have so to be developed, while tests, trials and evaluations conducted to satisfy the various stakeholders that expected outcomes need to be achieved. Considering vertical sectors, these will make use of such innovations and generate use cases for their particular sector(s). Investment and development will be required (implicating that new processes or ways of doing business may need to be considered), key decisions are to be made, business cases are to be developed and evaluations are to be conducted. Usually, some use cases may be more demanding than others and will "drive" the platform and services design and implementation. Some vertical sectors can be earlier/early adopters for the new technology/services and others will follow and make use of it, once it is proven. Within the 5G-PPP context, the Trials Working Group (WG) is elaborating a solid and comprehensive strategy to develop the Pan-EU coordinated trials [START_REF]Trials Roadmap[END_REF] as well as international trials with the participation of non-EU countries. The 5G Pan-EU Trials Roadmap is addressing several of the 5G Action Plan (5GAP) key elements and targets to develop the necessary synergies between these elements. The main objectives of the Roadmap are to: (i) Affirm global European leadership on 5G technology, 5G networks deployment and profitable 5G business models; (ii) demonstrate benefits of 5G to vertical sectors, public sector, businesses and consumers; (iii) show a clear path to successful and timely 5G deployment; (iv) provide robust response to the EC 5GAP, and; (v) complement commercial trials and demonstrations as well as national initiatives [START_REF]5G Pan-European Trials Roadmap Strategy[END_REF].

This roadmap includes several inter-related streams, as depicted in detail in Fig. 1, and these comprise of: (i) Private experimentation and trials; (ii) large-scale 5G events and showcases, including national and pan-EU events; (iii) development and use of Research & Innovation (R&I) platforms for 5G experimentation and trials; (iv) R&I experimentation and trials in the context of vertical applications and use cases; (v) 5G international trials cooperation, and; (vi) 5G and 5GAP Regulation and Policy ensuring acceleration of 5G adoption and deployment (including spectrum, network neutrality, EU de-fragmentation, etc.).

In a "similar" -or somehow "equivalent"approach, the EC in cooperation with the 5G Infrastructure Association (5G IA) has also identified [START_REF]Trials Roadmap[END_REF] the following distinct steps (covering the broader period 2014-2024) as depicted in Fig. 2, below. This figure also incorporates relevance of intended trials to major events, so that to support 5G innovation for the benefit of the involved end-users. In addition, Fig. 2 summarises the overall 5G Pan-EU Trials Roadmap time plan and relevant standardisation, regulatory and ecosystems time plan(s). Before 2018 (i.e., before the first 5G standard release by the 3rd Generation Partnership Project (3GPP) in Release 15, Stage 3) [START_REF]The 3 rd Generation Partnership Project (3GPP): 3GPP TR 21.915 v0.5.0[END_REF] it has been envisaged to have technical trials run by independent trial consortia in various countries, independent of the status of standardisation, to demonstrate and validate the new 5G capabilities as well as foster an ecosystem around the new 5G capabilities. Vertical industries have been involved in this trials' phase, while these trials have demonstrated several key 5G functionalities and technical/technological enablers. During and after 2018 European stakeholders moved to agree on trial specifications (e.g., use-cases, scenarios, interfaces, agreement to transfer use-cases across trial networks) valid for Pan-European trials, based as much as possible on standard-compliant systems. These trials, as performed in a more "extended" framework, aim to demonstrate wider interoperability and support for vertical use-cases in order to claim global public attention. In the following subsections we briefly discuss the various conceptual streams, as appearing in Fig. 1.

5G Private Trials Stream: This has been the case of the early stages when trials have been realised by specific initiatives promoted by some market actors for their own needs and priorities or, exceptionally, within the "limited" framework of some collaborative actions or projects.

5G/5GAP Trials Events Stream:

There are and will be an increasing number of 5G private experimentations and trials done on bilateral (Network Operator (NO) and Manufacturer/Vendor), trilateral (NO, Manufacturer/Vendor and Vertical stakeholder) and multilateral (NO, Manufacturer/Vendor and different stakeholders) basis. Such experimentations and trials shall be considered upon private basis, while information will be either available a posteriori (achieved) or a priori (pre-announcement). The 5G-PPP Trials WG is responsible to collect the major public information related to announced and pre-announced experimentation and trials. Specific added value analysis will be achieved through different perspectives, such as: (i) Industrial stakeholders; (ii) countries, (iii) 5G pillars (eMBB, URLLC (Ultra-Reliable and Low-Latency Communication) and mMTC (massive Machine Type Communication)) and KPIs (e.g., data rate, mobility, latency, density, reliability, positioning accuracy, coverage, etc.); (iv) timing (2018, 2019, 2020 and onwards) and; (v) standardisation phases and releases (e.g.: 3GPP Release15 [START_REF]The 3 rd Generation Partnership Project (3GPP): 3GPP TR 21.915 v0.5.0[END_REF], Release16 [START_REF]The 3 rd Generation Partnership Project: 3GPP Release 16[END_REF] and onwards).

5G Platforms Stream: 5G Platforms are a useful tool to support the 5G European trials. These platforms can support the execution of 5G services and use cases, including for mass market and vertical sectors. Due to the high diversity of platforms, use cases, trials and maturity stages of technology, the 5G Platforms can be classified in three different levels, that is: (i) Research; (ii) End-to-End (E2E) Trials, and; (iii) pre-Commercial. A number of platforms have been designed in Europe (e.g. from EU-funded research projects) that can serve as a "basis" for 5G trials [START_REF] Verdone | White Paper on 5G Experimental Facilities in EU[END_REF]. However, because the standardisation of 5G is an ongoing activity, 5G Platforms will have to evolve from existing and new platforms, requiring further investments. In such scenario, cooperation between platforms is a way to rapidly adapt to changes/improvements to standards, to reduce costs, permit large trials, and even to interconnect with sites in other regions. The 5G-PPP Trials WG is assessing the interest, feasibility conditions and possible solutions to create and maintain a pan-European platform, or a network of federated platforms, to be used for 5G pan-European trials.

5G Vertical Pilots Stream: 5G experimentation and trials will evidently address vertical applications and use cases. The vertical trials may be partly connected to the private trials and directly connected to the 5G available platforms (pre-Commercial and R&I). The selection of vertical pilots should take into account the sectors already mentioned in the 5GAP, including but not limited to, the media and entertainment, public safety, eHealth, automotive, transport and logistic sectors. It will also incorporate the progress already achieved by the 5G Infrastructure PPP on the 5G use by vertical sectors. The 5G-PPP Trials WG's aim is to support the selection of vertical and use cases for trials for a better understanding of the technical and technology requirements. In any case, the pre-definition of the vertical trials will address: (i) Description of vertical sectors; (ii) use cases addressed; (iii) technical requirements, and; (iv) technologies required for the provision of requirements. This step can enable a gap analysis to focus further R&I to overcome any remaining technology gaps.

5G International Trials Cooperation Stream: Multiple European 5G network trials and most of the 5G technologies under trials will be available for commercial deployment by 2020. In any case and beyond the 5G EU-focused developments, the 5G Pan-EU trials are implemented within a global context where several trials, precommercial and even commercial deployment initiatives are already taking place in other countries and regions outside the EU. Since 5G will be deployed worldwide, it is required to work not only towards accelerating global 5G standards, but also on trialing and interoperability with other main regions. EU-"X" joint trials, where "X" includes international counterpart countries like China, Korea, Brazil and US, are "key" to ensure a common understanding of 5G as well as global interoperability. On the private side, the 5G IA has already signed bilateral Memorandums of Understanding (MoUs) with the leading 5G visionary organisations in China (IMT-2020 5G promotion group), Korea (5G Forum), Japan (5G Mobile Communications Promotion Forum) and 5G Americas (North and South America). Specific joint experimentations and trials will be developed in the context of these MoUs.

5G/5GAP Regulation and Policy Stream:

As the validation of 5G services and facilities is to take place via trials and experimentation, it is expected that this will also affect EU policies and relevant regulation. For certain domains like access to and use of selected frequency bands of spectrum, results are to be immediate, while for other cases results may affect further development of technical solutions as well as corresponding market policies.

The ongoing twenty-one (-21-) 5G-PPP Phase 2 projects [START_REF]The 5G Public Private Partnership (5G-PPP[END_REF] (2017-2019) contribute to the prototyping, experimentation and trialing of 5G technologies and components for specific use-cases including vertical use-cases developed with vertical stakeholders. This infrastructure offers the suitable level of openness to allow vertical industries to test their innovative 5G business cases by using ad-hoc network resource control in an E2E interoperability framework. With the application-specific requirements and usage scenarios for 5G already identified, the experiments are investigating 5G functionalities spanning eMBB, uRLLC and mMTC. These requirements stem from an analysis of business aspects and applications driven by the verticals. In the automotive sector, in particular, industry stakeholders within the 5G-PPP are embracing connected and autonomous cars, vehicle-to-infrastructure, entertainment and corresponding media services. The anticipated impact is huge and extensive, as this promotes new ecosystems of commercial and public-sector product and service providers.

In the same scope, the 5G-PPP Phase 3 of pan-EU 5G E2E facilities/platforms (ICT-17) projects [START_REF]The 5G Public Private Partnership (5G-PPP[END_REF] started in July 2018 and cover 20 platforms/nodes in EU (each one in a European city). Simultaneously, the three Phase 3 corridors (under ICT-18) projects started in November 2018, address multiple EU "test corridors" [START_REF]Cross-Border corridors for Connected and Automated Mobility[END_REF]. The so called as the 5G corridors make Europe the biggest experiment area rolling out the 5G technology. This confirms Europe's leadership in large-scale testing and early deployment of 5G infrastructure also enabling connected and automated driving, among others. As of October 2018, 10 digital cross-border corridors have been identified for Connected and Automated Mobility (CAM).

As of October 2018, more than 140 trials and pilots have been reported as "being active" within the EU. In June 2019, 6-7 PPP Phase 3 Vertical Pilots (ICT-19) projects will "target" large scale trials and pilots, including complete E2E 5G systems and leveraging the existing platforms projects. It is expected that additional 5G test corridors projects will be launched in the context of the PPP Phase 3. In addition to the 5G private trials and pilots reported in the EC EU 5G Observatory, the 5G Infrastructure PPP Verticals Cartography, launched in September 2018, tracks citybased 5G trials and pilots from 5G Infrastructure PPP Phase 2 projects across eight vertical clusters: Automotive, Energy, Health, Industry, Media and Entertainment, Public Safety, Smart Cities, Transport and Logistics planned in 2018-2020 [START_REF]Global 5G, Verticals Cartography[END_REF].

The Context of the 5G-DRIVE Project

The 5G-DRIVE project [START_REF]5G-DRIVE ("5G harmoniseD Research and trIals for serVice Evolution between EU and China") 5G-PPP Project[END_REF] aims to perform a "close" collaboration between EU and China to synchronise 5G technologies and spectrum issues before the final roll-out of 5G in order to "address" two most promising 5G deployment scenarios, namely enhanced Mobile Broadband (eMBB) and Vehicle-to-Everything (V2X) communications. The eMBB is one of three primary 5G New Radio (NR) use cases defined by the 3GPP [START_REF]Study on New Services and Markets Technology Enablers -Technical Report (TR)[END_REF] as part of its SMARTER (Study on New Services and Markets Technology Enablers) project [18]. This "addresses" the human-centric use cases for access to multi-media content, services and data. Actual technology trends purely "demonstrate" that the demand for mobile broadband will continue to increase, thus leading to eMBB. The related usage scenario covers a range of cases, including wide-area coverage and hotspot, which have different requirements. On the other hand, V2X communication is the transfer of information from a vehicle to any "entity" that may affect the vehicle, and vice versa. It is a vehicular communication system that integrates other more specific types of communication as V2I (Vehicle-to-Infrastructure), V2N (Vehicle-to-Network), V2V (Vehicle-to-Vehicle), V2P (Vehicleto-Pedestrian), V2D (vehicle-to-Device) and V2G (Vehicle-to-Grid).

The 5G-DRIVE project falls within the context of the ICT-22-2018 Call ("EU China 5G Collaboration") [START_REF]Work Program[END_REF]. The main scope is to conduct 5G trials addressing two specific scenarios: (i) Scenario no 1 -eMBB on the 3.5GHz band, which is a priority band in the two regions for early introduction of very high rate services; and; (ii) Scenario no 2 -Internet of Vehicles (IoV) based on LTE-V2X using the 5.9 GHz band for V2V and the 3.5 GHz band for V2N. The overall goal is to evaluate in real setup innovative end-to-end 5G systems built on the outcomes of the previous phases of the 5G R&I. More specifically, the optimisation of the band usage in multiple scenarios with different coverage is a key target, so as the validation of the geographic interoperability of the 3.5 and 5.9 GHz bands for these use cases. Both scenarios shall be implemented in both regions (EU and China) through testbeds with interoperability forming the core of the R&I work.

The underlying trials' testing facilities shall implement the latest mature and broadly commonly agreed 5G systems, network architectures and technologies spanning from the core/transport networks, the radio access, up to the service, orchestration, management and security components. The trial facility shall not be restricted to innovative 5G radio access technology, but should include and enable the evolution of 5G networks innovations in network slicing, virtualisation, cross-domain orchestration, in view of supporting resource control from multiple tenants. In EU, trials are preferably implemented over the 5G end-to-end platforms developed under ICT-17-2018 [START_REF]Work Program[END_REF]. The 5G trials' infrastructures shall facilitate the testing and validation of innovative applications for each of the defined scenarios, including efficiency solutions in the areas of spectrum usage, energy consumption and costs.

The main objective of the 5G-DRIVE context is to "bridge" current 5G developments in Europe and China through joint trials and research activities in order to facilitate technology convergence, spectrum harmonisation and business innovation before large scale commercial deployments of 5G networks take place. In order to achieve this goal, the 5G-DRIVE develops "key" 5G technologies and precommercial testbeds for eMBB and V2X services in collaboration with the "twinned" Chinese project led by China Mobile. Trials for testing and validating key 5G functionalities, services and network planning will be conducted in eight cities across the EU and China. 5G-DRIVE's overall concept is illustrated in Fig. 3, which shows the three "core" streams and depicts the flow from research, to adaptation into existing testbeds and commercial testbed deployments, to the real-world trials of the 5G radio access network (RAN) and the wider 5G network. The project "brings together" solid research competence, commercial grade testbeds, and some of the stakeholders who will eventually become major customers of 5G systems.

In the 5G-DRIVE context there are partners with rather extensive 5G testbed installations -these are three facilities that have been defined, specified and deployed to "meet" the individual requirements of the involved three research organisations (UoS, VTT and JRC, as discussed in detail in the following sub-sections). In fact, three testbed sites have already been set up with commercial and experimental-grade equipment, supporting capacity provision in very dense deployments, network slicing and V2X, as well as testing of new technologies in any part of the network in a fullycontrolled environment. While all three testbeds are set up with commercial grade equipment, each one has a special focus: the University of Surrey (UoS) testbed can support capacity provision in very dense deployments over a 4 km² area; the Espoo testbed (VTT) demonstrates the use of slicing and V2X; the JRC facility allows the testing of new technologies in any part of the network in a fully-controlled environment. All testbeds are defined in an evolutionary approach and allow the gradual introduction and testing of new equipment, as well as new mechanisms, algorithms and protocols. These characteristics will be exploited in the entire 5G-DRIVE's context. In the research stream, the project will investigate network and RAN slicing, mobile edge computing (MEC), massive MIMO (Multiple Inputs, Multiple Outputs) for the 5G NR, as well as SDN and network function virtualisation (NFV) techniques applied to different traffic and load scenarios. Techniques and mechanisms in the research stream will be integrated into the most appropriate testbed. Wherever possible, 5G-DRIVE will endeavour to deploy such new mechanisms into all three testbeds.

The core objective of the project is to extensively trial eMBB and V2X service delivery under real world conditions. The stringent requirements for the delivery of such services will be defined jointly with the mobile operators in the consortium (Orange, OTE), as well as stakeholders form the automotive and intelligent transports markets (BMW, Vedia, Dynniq, ERT). These partners will be involved in the use case and trial requirements definition, as well as in its subsequent implementation and analysis. The inclusion of these stakeholders is imperative to ensure that the trials and solutions "do meet" the requirements from the vertical domains. As shown in Fig. 4, The UoS trial site and the 5G Innovation Centre [19] includes a Cloud-RAN (C-RAN) test platform [START_REF]The Benefits of Cloud-RAN Architecture in Mobile Network Expansion[END_REF] which supports clusters of remote radio heads (RRH), supported by high performance core processing facilities for experimental research on advanced techniques such as joint transmission coordinated multi-point transmission and reception (CoMP) schemes [START_REF] Singh | Coordinated Multipoint (CoMP) Reception and Transmission for LTE-Advanced/4G[END_REF]. In addition, the test network provides a unique environment to test operation of heterogeneous access networks in a real life environment. In the context of 5G-DRIVE, it will focus on the development and evaluation of the eMBB scenario. The testbed is connected to the Vodafone Core Network, Fujitsu Cloud Computing facilities and covers a 4 km² area for the testing of 5G technologies. The coverage area encompasses a stretch of motorway, rural, urban and dense urban radio environments. The outdoor deployment consists of 44 sites and 65 cells (of which 3 are macro cells, the remainder are small and ultra-dense cells). This end-to-end testbed incorporates a different range of frequency bands (3.5 GHz, 28 GHz and 60 GHz) and allows the testing and trialling of new air-interface solutions. Supported by a mix of wireless and fibre optic backhaul connectivity, trials can be matched to meet industry requirements. Finally, the platform can support interfacing to other testbeds, servers and databases for integration of different components provided by other consortium members and external experiments.

The Espoo trial site (see Fig. 5) provides 5G testing facilities built in several national projects under the 5GTNF (5G Test Network of Finland) framework. In the context of 5G-DRIVE, it will focus on the development and evaluation of both eMBB and V2X scenarios. The current network infrastructure is built on top of Nokia's NetLeap LTE test network [START_REF] Aalto | Mobile test network for research (Netleap)[END_REF]. It will be gradually upgraded to 5G networks when 5G NR and 5G core network components are available. The network contains both indoor and outdoor eNodeBs operating at 2.6 GHz, lamppost integrated small cell networks operating at 3.5 GHz and mm-wave bands at 26 GHz, as well as Wi-Fi networks operating at unlicensed 2.4 GHz and 5 GHz. This site enables creating a virtual mobile network with its own evolved packet core (EPC) and can utilize the edge computing platform for developing localized services. The JRC Ispra site is a fully fenced research campus equipped with high-level safety and security featuresa 167-hectare controlled environment for hands-on experimentation, testing and demonstration purposes. It features 36 km of roads under real-life driving conditions, as well as 9 Vehicle Emissions Laboratories (VELA 1-9) that can be used for calibration, electromagnetic compatibility/interference testing and other experimental activities. In the context of 5G-DRIVE, the JRC Ispra site will focus on the development and evaluation of V2X scenarios, with a particular focus on laboratory and field ITS-G5/LTE-V2X coexistence testing (see Fig. 6). The JRC Ispra campus has been mapped at very high resolution using drones and Laser Imaging Detection and Ranging (LiDAR), with digital maps available in various formats. This information will be used for network planning (i.e. predictions of radio propagation and network coverage), as well as vehicle localisation and intelligent routing. As far as network infrastructure is concerned, the JRC owns various LTE eNodeBs of both commercial and experimental grade, as well as a software-defined LTE core network to provide mobility, session management and user authentication services to internal test users. MEC infrastructure can be connected to the core network using either radio links or low latency fibre. These systems will take the form of small-form factor PCs featuring roadside sensing and fast computing capabilities. Finally, the JRC is also equipped with a production-level Public Key Infrastructure (PKI) which has already been used as the European Root Certification Authority for the EU Digital Tachograph project [23]. The JRC has been directly involved in drafting the Certificate Policy for the European V2X (referred to as the Cooperative Intelligent Transport Systems (C-ITS) Trust Model) [24].

Conclusion

The present paper has discussed the important role of trials and of dedicated platforms at the European level as fundamental "enablers" for the 5G deployment and validation within the EU. The work identified the important role that 5G is expected to perform, as essential part of the EU policy for evolution and growth, offering benefits to the European market and citizens. More specifically, within the context of the actual 5G-PPP initiative, we have assessed the specific measures proposed and the roadmap already applied -or scheduled-towards establishing the necessary framework for the realisation of pilot actions and /or related trials, to serve the expected transition to the 5G. The European Commission, in particular, emphasizes on the establishment of an adequate and well-defined framework, able to support 5G growth via suitable experimental efforts. The proper development of the 5G ecosystem in Europe is strongly dependent on a dedicated roadmap that is harmonised to the present EU research effort, being able to promote and enlarge all potential technological and business benefits. To this aim, we have discussed the distinct and separate streams affecting these actions, in parallel to corresponding standardisation efforts, promotion of research programs and events of global character.

The actual 5G pan-European trials roadmap [24] is covering a broader scope than the 5GAP and the 5G Infrastructure PPP Phase 3 (2018-20+). Most of the roadmap implementation is -and will be-covered by the industry on private basis, with part of this implementation supported by EC through the 5GAP, EC 5G Infrastructure PPP Phase 3, EC 5G Investment Fund and by MSs through specific national programs. More specifically, the 5G Infrastructure PPP Phase 3 platforms projects (2018-2021) provide a pan-European large-scale E2E 5G validation network infrastructure, covering about 20 European sites and nodes on a pan-European basis. This infrastructure will provide the adequate level of openness to make it possible for vertical industries to test their innovative 5G business cases using ad-hoc network resource control in an E2E interoperability framework. The future roadmap of actual 5G infrastructure deployment is highly dependent on the capability to up-date existing or deliver a new relevant and comprehensive set of platforms addressing the remaining gaps and challenges as well as to promote new architectures [START_REF] Redana | Views on 5G Architecture, White Paper of the 5G-PPP Architecture WG[END_REF].

Part of the above PPP Phase 3 context is also the case of the 5G-DRIVE project which intends to realise a collaborative framework between EU and China so that to achieve mutual synchronisation of 5G technologies and spectrum issues before the final roll-out of 5G, around two essential 5G deployment scenarios (namely enhanced Mobile Broadband (eMBB) and Vehicle-to-Everything (V2X) communications). In this scope, we have presented, in detail, the related 5G-DRIVE trial sites that have been proposed by this project, as a characteristic example of the actual EU initiatives for trials and as an indispensable part of the European strategic framework for the 5G validation in certain domains that can be of significant benefit for the European economy and society.

Fig. 1 .

 1 Fig. 1. Pan-European Trials Roadmap 2017-2020 -Strategy and Streams [9].

Fig. 2 .

 2 Fig. 2. Overall 5G Pan-European Trials Roadmap 2014-2024 [9].

Fig. 3 .

 3 Fig. 3. Overall Concept of the 5G-DRIVE Project's Approach.

Fig. 4 .

 4 Fig. 4. High-Level View of the University of Surrey's Test Network in the UK.

Fig. 5 .

 5 Fig. 5. Espoo Trial Site at Otaniemi Espoo in Finland. The design of the test network is such that it is open for experimental EPCs. This enables multi-operator scenarios and testing of network slicing in the project. MEC platforms are currently being installed at the Otaniemi site. With the aid of an artificial delay element (network emulator), the performance of MEC for URLLC use cases can be tested in different latency scenarios. eNodeBs are connected to a 10 Gbps SDN-enabled backhaul and to an OpenStack cloud environment. The testbed in Espoo provides facilities and test environments for SDN/MEC, indoor positioning, latency reduction, reliability and other technology topics targeted by 5G-DRIVE.

Fig. 6 .

 6 Fig. 6. The ITS-G5/LTE-V2X Testbed at JPC Ispra, in Italy.

Acknowledgments. This work has been performed in the scope of the 5G-DRIVE European Research Project and has been supported by the Commission of the European Communities /H2020, Grant Agreement No.814956).


Fotios Kounelis
email: kounelis@ceid.upatras.gr

Andreas Kanavos
email: kanavos@ceid.upatras.gr

Ioannis E Livieris
email: livieris@teiwest.gr

Gerasimos Vonitsanos
email: mvonitsanos@ceid.upatras.gr

Panagiotis Pintelas
email: ppintelas@gmail.com

Predicting Secondary Structure for Human Proteins based on Chou-Fasman Method

Keywords: Protein Structure Prediction, Computational Structural Biology, Human Proteins, Secondary Structure Prediction, Protein Folding

Proteins are constructed by the combination of a different number of amino acids and thus, have a different structure and folding depending on chemical reactions and other aspects. The protein folding prediction can help in many healthcare scenarios to foretell and prevent diseases. The different elements that form a protein give the secondary structure. One of the most common algorithms used for secondary structure prediction constitutes the Chou-Fasman method. This technique divides and in following analyses each amino acid in three different elements, which are a-helices, b-sheets and turns based on already known protein structures. Its aim is to predict the probability for which each of these elements will be formed. In this paper, we have used Chou-Fasman algorithm for extracting the probabilities of a series of amino acids in FASTA format. We make an analysis given all probabilities for any length of a human protein without any restriction as other existing tools.

Introduction

Proteins form one or more determined conformations guided by a number of complicated and reversible non-covalent interactions in order to unfold their their biological functions. Although determining the structure of a protein can be obtained by time-consuming as well as relatively expensive techniques such as crystallography, dual polarization interferometry and nuclear-magnetic resonance spectroscopy, new techniques based on the area of bioinformatics have been developed. These techniques aim to compute and in following predict protein structures based on their amino acid sequences. Amino acids are one of the main components that form proteins [START_REF] Vickery | The history of the discovery of the amino acids ii. a review of amino acids described since 1931 as components of native proteins[END_REF][START_REF] Vickery | The history of the discovery of the amino acids[END_REF]. They are encoded directly from the genetic code through the transcript-RNA. These specific components contain functional groups of amine and carboxyl as well as side chains. Twenty different amino acids are encoded from the genetic code, although a lot more have naturally occurred [START_REF] Wagner | New naturally occurring amino acids[END_REF]. Using different techniques, additional amino acids which generate proteins with newly enhanced properties [START_REF] Xie | Adding amino acids to the genetic repertoire[END_REF] are genetically encoded.

Protein structure can be predicted with the use of structure analysis and prediction tools according to their amino-acid sequences. More specifically, solving the structure of a given protein can be considered as highly important in medicine (another example is the drug design) and biotechnology (an example constitutes the design of novel enzymes). Thus, the area of computational protein structure prediction is constantly evolving, by following the development of intelligent algorithms and more important, by taking advantage of the increase in computational power of machines.

Regarding the protein structure prediction, the primary structure is employed to predict secondary as well as tertiary structures. The primary structure consists of the linear sequence of amino acids [START_REF] Bock | Predicting protein-protein interactions from primary structure[END_REF][START_REF] Sanger | The arrangement of amino acids in proteins[END_REF]. There are twenty different amino acids, that their occurrence in the sequence and the position or positions they occur, will form a different protein.

Furthermore, the different elements that form a protein as well as their 3D structure give the secondary structure [START_REF] Perticaroli | Secondary structure and rigidity in model proteins[END_REF]. a-helices, b-sheets, turns, and omega loops are the elements that occur in the secondary structure of a protein and the ones that our paper predicts.

After the above elements are formed and in following are combined in the 3D space, the tertiary structure of the protein is shaped [START_REF] Biasini | SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information[END_REF]. This structure has one or more secondary structures and may have interactions and bonds with amino acid side chains. The tertiary structure is considered as the highest structure of a protein, where all the protein subunits are arranged and numbered [START_REF] Janin | Protein-protein interaction and quaternary structure[END_REF].

In the present manuscript, we have employed the Chou-Fasman method, an algorithm used for secondary structure prediction. With this method, we aim to divide and analyse each amino acid in three different elements, namely ahelices, b-sheets and turns based on already known protein structures to predict the probability for each one of these elements. There are also other tools to implement the Chou-Fasman method but they lack in different aspects that we overcome.

The first obstacle is the size limit of online tools. Our implementation predicts the secondary structure for any length of sequence. Furthermore, our implementation provides the probabilities for all three different elements that the Chou-Fasman algorithm can predict. This part of our paper overcomes the problem of other tools that don't predict the probabilities for turns. Even if turns have the lowest probabilities, it is yet a very useful part of the secondary structure of a protein.

The second contribution of the proposed work deals with the proteins we study. We aim at the human proteins and their probabilities so that we can primarily understand what is the performance of this method in this specific type of proteins. This will allow us in the future to build a more robust map for human proteins that are produced from different chromosomes and check the performance of different algorithms on this kind of map.

The remainder of the paper is organized as follows. Section 2 presents a brief survey of recent studies concerning protein structure prediction, homology modeling and protein fold recognition. Section 3 presents a reference to the prediction tools and methods and introduces the Chou-Fasman method while in Section 4, we present our experimental results. Finally, Section 5 discusses our concluding remarks, the open problems as well as our future work.

Background Topics

Regarding protein structure prediction, several methods and techniques have been proposed with the aim of attempting to specify the structure and bonds of an amino acid sequence [START_REF] Jiménez-Montaño | The hypercube structure of the genetic code explains conservative and non-conservative aminoacid substitutions in vivo and in vitro[END_REF][START_REF] Siman | Excitatory amino acids activate calpain i and induce structural protein breakdown in vivo[END_REF]. As a result, the knowledge of the protein structure is critical and thus, several determinants can be considered as important; the bond angle stresses, the electrostatic interactions, the hydrogen and covalent bonds, the hydrophobicity and hydrophilicity of residues, the van der Waals interactions, as well as the enthalpy and the entropy.

Regarding protein structure determinants, there are two important aspects as well; initially, the information about them, and as a result, the information regarding the structure of a protein is entirely contained within the sequence (in addition to knowledge of the solvent). In following, these determinants can also be considered as measurements of physical properties. Without the aid protein chaperones, we can assume that proteins can take their native conformation in a solvent; then enough information for predicting protein structures ab initio (from basic principles) can be considered. However, many of the determinants are not precisely known or may be too compute-intensive and computationally non-tractable.

Protein structure prediction has resorted to knowledge-based methods, since there is lack of feasible ab initio methods. Specifically, homology modeling [START_REF] Cavasotto | Homology modeling in drug discovery: current trends and applications[END_REF] as well as protein fold recognition methods [START_REF] Jones | A new approach to protein fold recognition[END_REF] constitute the two major and complementary approaches that were taken.

Homology Modeling

In terms of homology modeling, the amino acid sequence of a protein with unknown structure is aligned against sequences of proteins with known structures. Furthermore, high degrees of homology (very similar sequences across and between the proteins) can be used for determining the global structure of the corresponding protein and in turn for placing it into a certain fold category. On the other hand, lower degrees of homology may be still used in order to determine local structures, with an example being the Chou-Fasman method [START_REF] Chou | Empirical predictions of protein conformation[END_REF][START_REF] Kumar | Cfssp: Chou and fasman secondary structure prediction server[END_REF] for predicting secondary structure. An advantage for homology modeling methods is the lack of dependence on the knowledge of physical determinants.

Every homology modeling process consists of four steps which are always the same [START_REF] Meier | Automatic prediction of protein 3d structures by probabilistic multi-template homology modeling[END_REF]. Firstly, the homologous template proteins of already known proteins should be gathered. Secondly, the best set of templates should be selected. In following, this set will be used in order to optimize the multiple sequence alignment for the query. Finally, the model that will be as close as possible to the structure of the templates, will be built. These steps can be repeated until a satisfying model finally occurs [START_REF] Schwede | SWISS-MODEL: an automated protein homology-modeling server[END_REF].

More to the point, homology modeling constitutes a very important tool for 3D protein structure models. This is the reason why web-based integrated systems have been built. SWISS-MODEL [START_REF] Biasini | SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information[END_REF][START_REF] Bordoli | Protein structure homology modeling using swiss-model workspace[END_REF][START_REF] Schwede | SWISS-MODEL: an automated protein homology-modeling server[END_REF] is considered as such a workspace, where for any queries, a library is searched for suitable templates. This tool is very reliable as it depends on sequence alignment for predicting the structure of the protein.

Homology modeling can be further utilized for the correct prediction of quaternary protein structure [START_REF] Bertoni | Modeling protein quaternary structure of homo-and hetero-oligomers beyond binary interactions by homology[END_REF]. The researchers in this work used homology to fill the gap of non-structural experimental evidence for the 3D multimeric assemblies. Apart from that, homology modeling can be also used for large proteins too. In [START_REF] Shen | Homology modeling of larger proteins guided by chemical shifts[END_REF], an approach for the structure prediction of large proteins is described. In this work, an alignment method, which exploits bioinformatics algorithms to match values from a database to experimental data, is produced.

Protein Fold Recognition

Fold recognition methods take a complementary approach. In these methods, structures, not sequences, are aligned. If the sequence of a protein has an unknown structure, with the method called "threading", it undertakes the conformation of the backbone (protein sans side chains) of a known structure. For each attempt, higher physical determinants produce a more complete score for the alignment. These methods tend to be more computational-intensive than homology modeling methods, but they give more confidence in the physical viability of the results.

One of the major contributions is Phyre2 [START_REF] Kelley | The phyre2 web portal for protein modeling, prediction and analysis[END_REF]; a suite of tools available on the web to predict and analyse protein structure, function and mutations. The focus of Phyre2 is to provide biologists with a simple and intuitive interface to state-of-the-art protein bioinformatics tools.

A recent work [START_REF] Chen | Improved chou-fasman method for protein secondary structure prediction[END_REF] has improved Chou-Fasman method in three aspects; authors replaced the nucleation regions with extreme values of coefficients calculated by the continuous wavelet transform, in following substituted the original secondary structure conformational parameters with folding type-specific secondary structure propensities and finally modified Chou-Fasman rules.

In a more recent work, authors in [START_REF] Rani | Elucidation of structural and functional characteristics of collagenase from pseudomonas aeruginosa[END_REF] present a detailed overview of the molecular, functional, and structural characteristics of collagenase from Pseudomonas aeruginosa which might be helpful for understanding the possible structure and functions of unknown proteins.

Prediction Tools and Methods

A great number of structure prediction software tools are developed for dedicated protein features, some among which are disorder prediction, dynamics prediction, structure conservation prediction, etc. Homology modeling, protein threading, ab initio methods, secondary structure prediction, and transmembrane helix and signal peptide prediction are approaches that help.

Each method uses a different level of the protein structure to predict another one. The primary structure is based on the amino acids linear sequence, which when combined in different places and varying numbers, form a different protein.

In our paper, we deal with the secondary structure prediction. Regarding the tools used for predicting the secondary structure of a protein, they are based on the primary structure. In following, we analyze the secondary structure prediction and especially, the method for predicting the secondary structure on the human proteins in a more detailed way.

Secondary Structure Prediction

The prediction of local secondary structures is facilitated via these tools. These structures are solely based on the amino acid sequence of the protein and their prediction is afterwards compared to DSSP (Define Secondary Structure of Proteins) score [START_REF] Joosten | A series of PDB related databases for everyday needs[END_REF], which is calculated based on the crystallographic structure of the protein [START_REF] Bliven | Automated evaluation of quaternary structures from protein crystals[END_REF][START_REF] Palczewski | Crystal structure of rhodopsin: A g proteincoupled receptor[END_REF]. It is the standard method used to assign secondary structure annotations to a protein structure. In addition, it calculates all DSSP entries from PDB (Protein Data Bank). DSSP utilizes the atomic coordinates of a structure to assign the secondary codes, but is not a secondary structure prediction tool.

Neural nets and support vector machines comprise modern machine learning methods on which prediction methods for secondary structure rely on. Taking into consideration databases of known protein structures along with these methods is an optimal combination.

More specifically, the secondary structure prediction problem for a protein sequence is to predict for each amino acid, whether it appears in a-helix, b-sheets or any other team. There are several prediction algorithms, which provide such probabilities to solve the problem [START_REF] Ito | Prediction of protein secondary structure using the 3d-1d compatibility algorithm[END_REF][START_REF] Yavuz | Prediction of protein secondary structure with clonal selection algorithm and multilayer perceptron[END_REF][START_REF] Zhou | Association classification algorithm based on structure sequence in protein secondary structure prediction[END_REF]. One of the most well-known and pioneer method is the Chou-Fasman technique. This method provides probabilities of amino acids whether they appear in a-helix, b-sheets or turns.

Chou-Fasman Method

The Chou-Fasman method exports the probabilities for a block of amino acids to be shown in each one of the three elements. Based on the frequencies that are calculated from observation for each amino acid, the probability for the whole sequence is predicted. When the method predicts helices and sheets, it works on a similar way; it has a window sliding on the frequency and upon meeting a high probability, it gets extended until the probability is under the predefined threshold.

On the other hand, even if turns have the same length of windows as in helices, the procedure is different due to the fact that many turn regions may occur in both helices and sheets. In order to predict a turn, its probability should be greater than helix or sheet as well as the same probability should be greater than a predefined threshold.

p(t) = p t (j) × p t (j + 1) × p t (j + 2) × p t (j + 3) (1)
In formula [START_REF] Bertoni | Modeling protein quaternary structure of homo-and hetero-oligomers beyond binary interactions by homology[END_REF], we can see this probability which explains why it is lower than the probabilities of helices and turns in all our experiments, where j is the position of each amino acid in the positions of the window with length equal to four.

There have been found that different amino acids have a higher tendency to occur in a certain secondary structure class, for example, alanine is known to appear in a helix form. The Chou-Fasman technique is based on statistical data and rules to provide such probabilities. When the method was introduced not many things were known about protein structure and as a result, the accuracy of the method was lower than it is nowadays.

Lab experiments can clearly prove which of the predicted probabilities of this technique are true and which are not. This allows the methods that produce their probabilities from already known structures, such as Chou-Fasman, to improve. As a result, we examine this method on the latest prediction updates. This will allow us to compare these results in later research, with probabilities that either have been collected in the past with the same method or by other modern methods. In the following section, we will analyze the proteins that were used and their form, as well as explain the methodically exported data.

Experimental Methodology

In order to gain a deeper insight on the elements that are analyzed, we present the following results that portray the distributions for secondary structure prediction.

The Chou-Fasman algorithm was implemented and run for ten different human proteins. These ten proteins as depicted in Tables 1 and2 as Apolipoprotein C-I, Casein Kinase 2, Disulphide Isomerase, Glutaredoxin, Insuline like 3, In-tereleukin6, Lysozyme C, Major prion Protein, Prion like Protein Dropel and S100-B. Each protein was split in five continuous amino acid blocks. For each block, the three different probabilities for each structure that will be formed, is produced. In order to understand and explain these data in a more rigorous way, we have Figure 1 to illustrate them. The three different lines represent the structures; while in y axis, the percentage is represented and in x axis, the different blocks in a consecutive order for each protein, are represented.

In following, we analyze the results of the ten proteins that were used in the Chou-Fasman technique. All these proteins belong to the human organism (Homo Sapiens). The human organism has 23 pairs of chromosomes and in the first row, we can see the number of the chromosomes in which each protein is deployed. The proteins are constructed from several amino acids. In the second row, we can see their number of amino acids. We can observe diversity in this specific characteristic as for example, the number of amino acids in protein S100-B is 92, whereas in Disulphide isomerase, the number of amino acids is 525. This diversity gives us a better insight to check the probability spectrum of the Chou-Fasman technique.

In the third, fourth and fifth lines, the most appeared amino acids as well as their percentage in the whole sequence are presented. We notice that Leucine (Leu) is the most common amino acid, which exists in almost all the proteins; specifically, this happens in seven out of the total ten proteins. However, even if Leucine is the most common amino acid in Casein Kinase 2 protein, the percentage remains low; this makes the deduction that all of the amino acids which occur share the same percentage, but this one is slightly larger.

Furthermore, the sixth line presents the instability index. This metric informs us whether a protein will be stable in a protein tube. In order to be stable, a protein should have an instability index of less than 40. As we can see, the four out of the total ten proteins are stable where Prion-like Protein Dropel is the most stable of all and the Casein Kinase 2 is the most unstable. Finally, in all of these proteins, two amino acids never occur inside the sequence, namely Pyrrolysine (Pyl) and Selenocysteine (Sec).

As we can see in Figure 1, both a-helices and b-sheets have for each block a greater probability than turns. Concretely, a-helices have in more than half the blocks a greater probability than b-sheets. Our first remark from the figure is that for all the blocks in these ten human proteins, these amino acids are more possible to form an a-helix structure.

A second observation that follows the previous one, is that there are whole proteins, like the third (Casein Kinase 2 ) and the sixth (Prion like Protein Dropel ) for the amino acid blocks 11-15 and 26-30 respectively, that have bigger probability of forming an a-helix. This means that these two proteins, in their whole sequence, according to the Chou-Fasman algorithm, will have a structure of an a-helix. This is not usual regarding the corresponding proteins, as only the two out of ten have this probability. In this paper, we analysed the problem of the secondary structure prediction for human proteins. Chou-Fasman is a method which solves this problem by predicting the structure in which a block of amino acids will be formed. Depending on the probability of each amino acid that will appear in a-helices, b-sheets, or turns, this method produces the output. We used this method for ten human proteins to predict their secondary structure form.

Chou-Fasman method achieves success rate equal to 50%-60%, while other more modern secondary structure prediction methods, like GOR or Porter 5 [START_REF] Torrisi | Porter 5: fast, state-of-the-art ab initio prediction of protein secondary structure in 3 and 8 classes[END_REF] achieve better results. As an open problem for research will be to implement other modern methods and make a comparative study between these methods and thus, discuss their differences in human proteins. Furthermore, it would be of great interest to implement those methods for proteins that are coded from different human chromosomes. This could potentially create a hybrid model for the use of different prediction methods depending on the chromosome from which a protein is derived of.

Fig. 1 .

 1 Fig. 1. The different probabilities for the three structures in all amino acid blocks

Table 1 .

 1 Human Proteins Details 1/2

		Apolipo-	Casein-	Disulphide	Glutare-	Insuline
		protein C-I	Kinase 2	Isomerase	doxin	like 3
	Chromosome	19	20	16	5	19
	Number of
	amino acids	83	391	525	106	131
	Most frequently
	appearing				Leu(L),
	amino acids	Leu(L)	Leu(L)	Leu(L)	Gln(Q) Leu(L)
	Number of most
	frequently appearing
	amino acids	12	33	63	12	22
	Percentage of most
	frequently appearing
	amino acids	14.5%	8.4%	12%	11.3%	16.8%
	Instability
	index	30.79	48.79	46.79	43.72	53.10
	Stable/Unstable	Stable	Unstable Unstable Unstable Unstable
	Non appearing	Pyl(P),	Pyl(P),	Pyl(P),	Pyl(P),	Pyl(P),
	amino acids	Sec(S)	Sec(S)	Sec(S)	Sec(S)	Sec(S)

Table 2 .

 2 Human Proteins Details 2/2

		Intere-	Lyso-	Major prion	Prion like
		leukin 6	zyme C	Protein	Protein Dropel S100-B
	Chromosome	7	12	20	20	21
	Number of
	amino acids	211	148	253	176	92
	Most frequently
	appearing
	amino acids	Leu(L) Ala(A)	Gly(G)	Leu(L)	Glu(E)
	Number of most
	frequently appearing
	amino acids	28	15	45	20	16
	Percentage of most
	frequently appearing
	amino acids	13.3% 10.11%	17.8%	11.4%	17.4%
	Instability
	index	37.38	27.71	43.11	23.06	36.45
	Stable/Unstable Unstable Stable	Unstable	Stable	Stable
	Non appearing	Pyl(P),	Pyl(P),	Pyl(P),	Pyl(P),	Pyl(P),
	amino acids	Sec(S)	Sec(S)	Sec(S)	Sec(S)	Sec(S)


Christos Tsirakis
email: ctsirakis@oteacademy.gr

Mariana Goldhamer
email: marianna001@gmail.com

Panagiotis Matzoros
email: pmatzoros@oteresearch.gr

George Agapiou
email: gagapiou@oteresearch.gr

Dimitris Varoutas
email: d.varoutas@di.uoa.gr

Marinos Agapiou

A Techno-Economic Analysis of Employing a central Coordinator Entity in 5G Networks

Keywords: 5G, C3 entity, techno-economic analysis, producer, operator

This research work describes the role of the Central Controller and Coordinator (C3) entity and its potential techno-economic gain when implemented in the upcoming 5G networks. We investigate how viable could be for a C3 Producer and for a cellular network Operator to produce and implement respectively the C3 entity in its network. The performance of techno-economic analysis is estimated by considering various key parameters and some useful conclusions are drawn.

Introduction

"Mobile data traffic will grow at a compound annual growth rate (CAGR) of 47 percent from 2016 to 2021", according to [START_REF]Cisco Visual Networking Index: Global mobile data traffic Forecast[END_REF]. So mobile network operators are facing massive data growth, but their revenues are usually not following this trend directly. This means that the operators need to provide the required capacity in a more efficient way. Moreover, mobile operators need to re-evaluate their network architecture due to advanced requirements of various applications. So it is crucial to identify the most flexible and cost-effective infrastructure model for next-generation services. In particular, the new mobile network needs to provide capacity exactly when and where it is required. Another challenge is that despite the fact that LTE and next generations of mobile networks offer lower price per bit, operators cannot switch off their legacy 2G / 3G networks. Thus operational costs need to be optimized by outsourcing or by shared operations.

There are also high expectations for new markets toward which the 5G technology is evolving [START_REF] Andrews | What will 5G be?[END_REF][START_REF]A Survey of Resource Management Toward 5G Radio Access Networks[END_REF]. These markets are associated with Machine-to-Machine (M2M) type communication and Ultra-Reliable Communication (URLLC), but they are not yet well understood by operators. Therefore, flexibility of configuration and operation are probably the most profound features expected from future 5G networks in order to provide these new types of services. A new sharable architecture is needed which should be flexible and easily manageable to satisfy the needs of the end users. The network controller and its clients at the Radio Access Network (RAN) level have to be highly adjustable to accommodate changing traffic characteristics such as bandwidth and latency requirements.

The COHERENT project [START_REF]Coordinated control and spectrum management for 5G heterogeneous radio access networks (COHERENT)[END_REF] focuses on developing a next generation unified programmable control and coordination framework for various heterogeneous radio access networks, with demonstrators based on LTE and Wi-Fi. It adopts the concept of resource and service virtualization across technology domains. The COHERENT architecture includes the C3 entity that exploits the software-defined networking (SDN) and virtualization concepts [START_REF] Arslan | Software-defined Networking in Cellular Radio Access Networks: Potential and Challenges[END_REF][START_REF] Granelli | Software Defined and Virtualized Wireless Access in Future Wireless Networks: Scenarios and Standards[END_REF][START_REF] Trivisonno | SDN-based 5G Mobile Networks: Architecture, Functions, Procedures and Backward Compatibility[END_REF], and converts the existing wireless mobile networks to contemporary, flexible, scalable, efficient and interoperable 5G networks.

The main contribution of our research work is the techno-economic analysis of employing the C3 entity in the 5G networks that highlights the potential financial gain value from two different perspectives, namely of the C3 producer and of the cellular network Operator.

In this research work, we perform a techno-economic analysis of how employing the C3 entity in 5G networks could provide profit for producer and operator companies. In Section 2, the C3 entity and the architecture, where it is integrated, are indicated, whereas in Section 3, a detailed techno-economic analysis is performed from two different business perspectives. Finally, in Section 4, the results of this technoeconomic analysis are presented, while interesting conclusions are drawn in Section 5.

COHERENT Architecture and C3 Entity

The COHERENT architecture [START_REF] Coherent | Deliverable 2.2 "System Architecture and Abstractions for Mobile Networks[END_REF] provides a programmable control and coordination that offers fine grain, real-time control. Scalability and timeliness for control and coordination are achieved by introducing two control mechanisms, namely Central Controller and Coordinator (C3) and Real-Time Controller (RTC), as shown in Fig. 1. By receiving status reports from low layer entities, C3 maintains a centralised network view of the governed entities, e.g., transport nodes (TNs) and Radio Transceivers (RTs) in the RAN. For example, an RT could be a legacy LTE eNodeB or a legacy WiFi Access Point (AP) or a New Radio Base Station (NR BS).

Based on the Centralised Network View (CNV), the SDN principles are applied in the design of the C3. For overcoming the delay limitation between the C3 and the individual access network elements, latency-sensitive control functionalities are offloaded from the C3 to RTCs. The network entities connect to C3 and RTCs through the southbound interface (SBi).

As mentioned before, C3 is a logically centralised control entity because the C3 instances share the network graphs with each other. The communication between controllers (RTC-C3, C3-C3) for sharing network graphs and offloading control functions is through east-west interface (EWi).

The service plane is defined as a collection of specific network applications and Radio Access Technology (RAT) configurations. Different network slices contain different network applications and configuration settings as shown in Fig. 1. Through northbound interface (NBi), the C3 and/or RTCs provide the required network view, namely slice-specific network view (SNV), for the network service slices so that network service slices could express desired network behaviours (by programming) without being responsible themselves for implementing that behaviour (with hardware). Some application modules in network slices may be latency-sensitive. For such a slice, these modules are located in the RTC. Additionally, monitoring modules which are latency-sensitive may need to operate close to the data source for reducing overhead and observe the network at high information granularity. The need for such monitoring modules may be service specific or operation specific. The examples of latencysensitive network applications are flexible RAN function splitting in Cloud-RAN, MAC scheduling, handover decision, cell reconfiguration. In addition, most of the Mobile Edge Computing application areas [START_REF] Patel | Mobile-Edge Computing -Introductory Technical White Paper[END_REF] are also relevant to RTC, e.g., localisation, augmented reality, low latency IP service, etc. In general, we could have different views inside the same slice, according to what the application wants to do. While the COHERENT control and coordination plane makes control decisions for RAN functions and sends the decisions to the network entities for executing the decisions, the management plane usually focuses on monitoring, configuring and maintaining the long-term decisions for network entities in the infrastructure, e.g., queries the databases managed by the National Regulation Agency (NRA) for the spectrum usage rules in the spectrum manager [START_REF] Lembo | Hierarchical Network-level Coordination for 5G Radio Access Networks[END_REF]. The entities in management plane are connected to the C3 through NBi.

Techno-economic Analysis

This section presents a detailed financial analysis of employing the C3 entity from two different business actors' point of view [START_REF]COHERENT, Deliverable 7.4 "Exploitation and Techno-Economic Model[END_REF]: I. C3 Producer's point of view II. Operator's point of view

Financial Model

A key step in the process of planning a transition path towards COHERENT network deployment is the evaluation of the network economics. Specifically, C3 producers and network operators should consider choosing the evolution path that yields the most economic value, including revenues, capital expenditures (CapEx) and operating expenditures (OpEx) over the economic life of the system. Note that economic evaluation may have to be based on assumptions about the evolution of demand and service penetration.

To implement a financial model where all of the described aspects are properly taken into account, specially designed tools are normally used. This implies a sequence of steps to go through to associate values to the input parameters. Running the model generates the technical and financial outputs driven by geographical data, service demand and network costs.

Furthermore, the main financial parameter that reflects the success of an investment is the Net Present Value (NPV). The NPV of an investment is the present (discounted) value of future cash inflows minus the present value of the investment and any associated future cash outflows. NPV is important because it gives more accurate solutions in the out years. This metric recognizes that money has different real value over time and makes the values of money constant by discounting costs and benefits over a specific period of time-an asset's life cycle or any selected period of analysis. On a less formal level, this metric can be indicative of the profitability of a business over the years. Given the (period, cash flow) pairs (t, Rt) where N is the total number of periods, the NPV is given by: where, tthe time of the cash flow; cash flow equals to the revenues minus the costs.

ithe rate of return WACC; the opportunity cost of capital. Rtthe cash flow at time t. Finally, there is one more input parameter that is needed, namely the Weighted Average Cost of Capital (WACC) of the company for this investment. The WACC is the average rate of return that the company expects to compensate all its different investors. The weights are the fraction of each financing source in the company's target capital structure. The WACC can serve as a useful reality check for investors. In this analysis, the WACC for the C3 Producer and Operator Company is 10%.

C3 Producer

This subsection describes a techno-economic analysis of employing the COHERENT architecture and specifically the C3 entity from the C3 Producer's point of view.

The viability of the C3 Producer (i.e. Start-up Company) is going to be evaluated over a period of seven years. The C3 Producer is going to invest on this architecture (i.e. developing the product and selling its services to multiple operators) because it would like to gain some revenues. The results of this analysis will show the economic benefits of employing the COHERENT architecture from the C3 Producer's point of view. The costs and revenues of the C3 Producer are categorized into the following general groups, as shown in Table 1.

More specifically, each group is further explained below, after making the necessary realistic assumptions for business related works with similar cost-revenues methodology [START_REF] Kantor | General framework for techno-economic analysis of next generation access networks[END_REF]: I. Cost of business-includes the sum of the personnel-related cost, i.e. head count (15-25) and cost per employee (80 k€ -84 k€), the fixed assets (10 k€ -50 k€), the production cost (60 k€ -150 k€) and the marketing cost (50 k€ -100 k€), according to the COHERENT original approach. It is also taken into consideration a company overhead factor (e.g. accounting, depreciation, insurance, license fees, taxes, rent), which is usually 0,3-0,5. The approach is that there is a basic functionality, such as mobility and load balancing, which can be enhanced by additional features. For each additional feature the producer revenue is defined as percentage of the infrastructure served by one C3 and a penetration factor. The producer will have revenues on both C3 and agents: II. Basic SW C3 + agent revenues-the revenues from the C3 are assumed to be the total C3 server cost multiplied by the number of C3 servers. Additionally, the revenues from the SW agent are assumed to be 5% of the total infrastructure cost served by one C3 multiplied by the penetration percentage of SW agents. In this case, the penetration percentage is 100%, since the SW agents are installed at each micro/macro DU.

III. Spectrum sharing revenues, infrastructure sharing revenues, RRM revenues,

network slicing revenues-include the added value multiplied by the basic SW C3 + agent revenues. Added value is the price that the product or service is sold at. The added value is assumed to increase from the initial 20% up to 50% for spectrum sharing, 30% for infrastructure sharing, 30% for RRM and network slicing. Also, the penetration percentage waves from 10% to 70% over the years for spectrum sharing and infrastructure sharing, from 20% to 40% for RRM, up to 50% for network slicing revenues. Note that, added value and penetration percentages follow the original COHERENT approach. IV. Maintenance revenues-it is usual practice to assume that maintenance is the 10% over the sum of revenues (i.e. SW C3 + agent revenues plus infrastructure sharing revenues plus spectrum sharing revenues plus RRM revenues plus network slicing revenues).

Operator

This subsection describes a techno-economic analysis of employing the COHERENT architecture and specifically the C3 entity from the Operator's point of view. The viability of the Operator is going to be evaluated through a 7-year analysis. The Operator is going to invest on this architecture (i.e. buying the product and services from the C3 Producer) because it would like to improve the performance of its network and at the same time gain some revenues. The results of this analysis will show the economic benefits of employing the COHERENT architecture from the Operator's point of view.

The costs and revenues of the Operator are categorized into the following general groups, as shown in Table 2.

More specifically, each group is further explained below, after making the necessary realistic assumptions for business related works with similar cost-revenues methodology [START_REF] Kantor | General framework for techno-economic analysis of next generation access networks[END_REF]: I. Infrastructure cost-Infrastructure includes the micro DUs and macro DUs.

Specifically, DU is a hardware box with MAC and PHY layers, radios and antennas.

The micro DU and macro DU cost 600 € and 4.500 €, respectively, according to data provided by OTE S.A, Greece. The macro DU/micro DU ratio is considered to be 1/10.

II. Cost of business-It is actually the price that the Operator will pay to the C3

Producer in order to employ the COHERENT architecture in his existing network. A C3 producer can sell to multiple Operators. III. C3 + agent related ARPU-First of all, it is assumed that the Operator deploys the new technology for providing coverage to customers; the increase in infrastructure coverage is reflected in the yearly increase of the customer number. Each year the customer base increases 10%. Also, the indicative average revenue per user (ARPU) is 25 €, which is reduced by 3% every year, according to data provided by OTE S.A, Greece. The income generated by ARPU is partially used (starting with 20% and increasing in time to 30%) for paying the infrastructure, C3 and agents costs. The SW cost is covered by the proportional part of the ARPU.

This section demonstrates the results of the techno-economic analysis provided in Section 3. These results give the opportunity to a future C3 Producer or Operator to examine the case of investing on the C3 entity and COHERENT architecture in general. At first, Fig. 2 presents the costs and revenues per year for the C3 Producer. It is observed that only after Year 3 the revenues-to-cost ratio is more than 1.5. The first two years, i.e. Year 1 and Year 2, the C3 Producer needs to cover this damage, i.e. difference between cost and revenue, with alternative ways, e.g. by taking a bank loan for the required capital. It is also shown that Year 4 and Year 5 have the highest performance, since these years the C3 production and relevant sales reach their peak. In fact, according to [START_REF]COHERENT, Deliverable 7.4 "Exploitation and Techno-Economic Model[END_REF], in Year 4 and Year 5, the number of C3 servers has more than tenfold increase compared with the number of C3 servers in Year 1. Also, Fig. 3, which shows the net revenues per year for the C3 Producer, proves the peak performance of the Year 4 regarding the revenues. Therefore, from the C3 Producer's point of view, it seems that it is beneficial to employ the COHERENT architecture since the techno-economic analysis results reflect the success of this investment. Then, Fig. 4 demonstrates the costs and revenues per year for the Operator. It is observed that the revenues exceed the costs from Year 3 onwards. Only the starting years, i.e. Year 1 and Year 2, both costs and revenues follow a similar trend, but then the revenues grow extremely during the whole next period analysis. The Operator starts with limited amount of money from the customer ARPU and invests strongly in the technology; in time the investments are reduced and the customer base increases significantly, such that the Operator gets significant earnings and has limited costs. Also, Fig. 5, that shows the net revenues per year for the Operator, proves again this almost exponential rise regarding the revenues. Therefore, from the Operator's point of view, it results that is beneficial to employ the COHERENT architecture since the techno-economic analysis results reflect the success of this investment. Finally, in Fig. 6, it is observed that the gradient of the NPV cumulative is higher for the Operator than for the C3 Producer. Essentially, after Year 4 the revenues for the C3 Producer slows down since the market starts to reach a saturated level of this technology and the competition is stronger. On the contrary, the Operator can impressively increase its revenues and continuously acquire new customers by providing new attractive services on the top of the COHERENT architecture.

To summarize, through this 7-year detailed financial analysis, it was proved that the adoption of COHERENT architecture could be profitable in economic terms and a viable solution for the wireless ecosystem, for both C3 Producers and Operators but with different attributes. It is also important to note that the assumptions made were modest, neither pessimistic nor optimistic. Thus, the results of the techno-economic analysis reflect the success of the COHERENT investment from both C3 Producers' and Operators' point of view and can drive to great development for future wireless networks.

Conclusion

This research work described the role of C3 entity and its potential technical gain value if implemented in the existing wireless communications network. Additionally, the performed techno-economic analysis showed that it could be viable for both C3 producer and network operator to implement the C3 entity in their own network.

The C3 producer is expected to make a platform that provides a lot of opportunities in increasing the financial base. In the near future, when 5G will come into the arena of network operators, there will be the need for the operators to have a platform such as C3 that controls and orchestrates the infrastructure, slicing aspects and resources.

Regarding the Operator, the C3 solution will bring benefits to him in terms of providing less CapEx and OpEx since most of the EPC-based equipment will be replaced by C3. However, the high initial cost or the time period it will take to replace all the access infrastructures may be threats to the operator.

Fig. 1 .

 1 Fig. 1. COHERENT Architecture [8].

Figure 2 .

 2 Figure 2. Costs and revenues per year for the C3 Producer.

Figure 3 .

 3 Figure 3. Net revenues per year for the C3 Producer.

Figure 4 .

 4 Figure 4. Costs and revenues per year for the Operator.

Figure 5 .

 5 Figure 5. Net revenues per year for the Operator.

Figure 6 .

 6 Figure 6. Net present value cumulative for both C3 Producer and Operator.

Acknowledgments. This work was conducted within the framework of the 5G-PPP COHERENT project, which is funded by the Commission of the European Union (Grant Agreement No. 671639). Also, this work has received funding from the European Union Horizon 2020-MCSA-ITN-2015 Innovative Training Networks (ITN) under grant agreement No. 675806 (5G-Aura).