Ioannis P Chochliouros
email: ichochliouros@oteresearch.gr

Na Yi

Anastasia S Spiliopoulou

Alexandros Kostopoulos

Nathan Gomes

Uwe Herzog

Tao Chen

Athanassios Dardamanis

Olga Segou

Philippos Assimakopoulos

Klaus Moessner

Juha Jidbeck

Rahim Tafazolli

Latif Ladid

Enhanced Mobile Broadband as Enabler for 5G: Actions from the Framework of the 5G-DRIVE Project

Keywords: The Third Generation Partnership Project (3GPP), 5G, 5G New Radio (5G NR), enhanced Mobile Broadband (eMBB), massive Machine Type Communication (mMTC), Ultra-Reliable and Low-Latency Communication (URLLC)

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Introduction

Both the European and the global economy have faced multiple challenges for growth due to the immense evolution of the Internet and the expansion of the broader electronic communications sector in a fully converged environment, promoting competition and wider service offerings for the involved market players [START_REF]Communication of the Commission: "A Digital Single Market Strategy for Europe[END_REF]. Within the forthcoming years it is also expected that the underlying (usually heterogeneous) network infrastructure will be able of "connecting everything" according to an extended multiplicity of application-specific requirements (thus including users, things, goods, computing centres, content, knowledge, information and processes), in a purely flexible, mobile, and quite powerful way.

In order to "face" these major challenges, such innovative aspects not only necessitate but also imply for the proper establishment and the effective operation and management of a relevant novel kind of infrastructure being able to deliver network attributes and performance characteristics to assure progress and growth in all corresponding domains (i.e., technical, business, financial, regulatory, social, etc.). Major network operators are strongly involved in such processes, whilst creating new opportunities for novelty and investments. These chances are so expected to take place within the forthcoming "fifth generation" -or 5G-of telecoms systems, that will be the most critical building block of our "digital society" in the following years [START_REF]European Commission: 5G: Challenges, Research Priorities, and Recommendations -Joint White Paper[END_REF]; in particular, 5G will not only be an evolution of mobile broadband networks but will "bring" new unique network and service capabilities, creating a sustainable and scalable technology as well as a proper ecosystem for technical and business innovation ( [START_REF] Chochliouros | Challenges for Defining Opportunities for Growth in the 5G Era: The SESAME Conceptual Model[END_REF], [START_REF] Andrews | What Will 5G Be?[END_REF]). 5G has the potential to deliver substantial performance and capacity improvements for personal, public and enterprise communications. It has also the ability to provide a suitable communication solution basis for many vertical applications, as well as to offer new architectural concepts and value chains to efficiently support innovation and future needs [START_REF]Communication of the Commission: "A 5G Action plan for Europe[END_REF]. 5G can therefore facilitate connectivity, network access and service security of different vertical sectors and be instrumental to the management and automation of business assets and processes.

The 5G system is expected to be able to deliver optimized support for a diversity of services, traffic loads and end-user communities [START_REF]The 3 rd Generation Partnership Project (3GPP): 3GPP TR 22[END_REF]. To this aim, several industrial approaches [START_REF] El Hattachi | Next Generation Mobile Networks (NGMN): NGMN 5G White Paper v1.0[END_REF] have proposed the conceptual framework of a system concurrently supporting various combinations of reliability, latency, throughput, positioning and availability. This sort of growth and development can be realised via the inclusion of modern technologies, both in access and the core network concept, offering flexibility and scalability of involved network resources. However, apart from increased flexibility and optimization, a 5G system has to support strict KPIs (Key Performance Indicators) for latency, reliability, throughput, etc. Furthermore, a modern 5G system is also expected to support innovative business models such as those for IoT (Internet of Things) and enterprise managed networks. Flexible network operations are essential features of the 5G system. Offering the intended degree of flexibility implicates the usage of features such as network slicing, network capability exposure, scalability, and diverse mobility. The 5G system will handle the great variability of all potential scenarios of use in a resource efficient manner. The corresponding use cases introduce new deployment requirements for indoor and outdoor, local area connectivity, high user density, wide area connectivity, and User Equipments (UEs) travelling at high speeds. Other 5G aspects may implicate requirements for various combinations of latency and reliability, as well as higher accuracy for positioning.

The work in the present paper is as follows: Section 2 discusses the critical role of the enhanced Mobile Broadband (eMBB) as a promoter of the first phase of the 5G implementation, within the scope of the New Radio (NR) actions, as originally performed by the Third Generation Partnership project (3GPP). Then Section 3 emphasizes on context of the specific eMBB trials within the framework of the 5G-DRIVE project; in particular, we identify several scenarios which include indoor hotspot, dense urban deployment and macro cells deployment and we propose related attributes. In order to promote the project actions for the scheduled trials, we also identify and briefly discuss corresponding KPIs, coming from similar research and/or standardization actions. Finally, Section 4 provides several concluding remarks.

Enhanced Mobile Broadband as Fundamental Enabler of the "First Phase of 5G"

The eMBB is one of three primary 5G New Radio use cases [START_REF]The 3 rd Generation Partnership Project (3GPP)[END_REF] defined by the 3GPP as part of its SMARTER ("Study on New Services and Markets Technology Enablers") project ( [START_REF]The 3 rd Generation Partnership Project (3GPP): 3GPP TR 22[END_REF], [START_REF]The 3 rd Generation Partnership Project (3GPP): 3GPP TR 22[END_REF]). The 3GPP qualifies ultra-fast mobile broadband as mobile systems capable of delivering speeds of 20 gigabits per second, at least unidirectionally, and without specific latency requirements [START_REF][END_REF]. The eMBB will initially be an extension to existing 4G services and will be among the first 5G services, which could be commercially available as early as 2019. The objective behind SMARTER [START_REF]Technical Specification Group Services and System Aspects; Service requirements for the 5G system; Stage 1 (Release 16)[END_REF] was to develop high level use cases and identify what features and functionality 5G would need to deliver to enable them. This specific project began in 2015 and resulted in over 70 use cases, initially grouped into five essential categories which have, since, been trimmed to three. They are characterised by the performance attributes the particular use cases will require, although there is some overlap. The three fundamental sets of use cases are as follows:

 Enhanced Mobile Broadband (eMBB): This "addresses" the human-centric use cases for access to multi-media content, services and data. Based on the original scope of the SMARTER approach, related requirements have been defined on high data rates, higher traffic or connection density, high user mobility, and those relevant to various deployment and coverage scenarios. The scenarios have addressed different service areas (e.g., indoor/outdoor, urban and rural areas, office and home, local and wide areas connectivity), and special deployments (e.g., massive gatherings, broadcast, residential, and high-speed vehicles). Actual technology trends purely "demonstrate" that the demand for mobile broadband will continue to increase, thus leading to enhanced Mobile Broadband. The enhanced Mobile Broadband usage scenario will come with new application areas and requirements in addition to existing Mobile Broadband applications for improved performance and an increasingly seamless user experience. This usage scenario covers a range of cases, including wide-area coverage and hotspot, which have different requirements. We can distinguish the following: (i) For a hotspot case, that is for an area with high user density, very high traffic capacity is needed, while the requirement for mobility is low and user data rate is higher than that of wide area coverage; (ii) For the wide area coverage case, seamless coverage and medium to high mobility are desired, with much improved user data rate compared to existing data rates. However, the data rate requirement may be relaxed compared to hotspot. The eMBB traffic can be considered to be a direct extension of the 4G broadband service. It is characterized by large payloads and by a device activation pattern that remains stable over an extended time interval. This allows the network to schedule wireless resources to the eMBB devices such that no two eMBB devices access the same resource simultaneously. The objective of the eMBB service is to maximize the data rate, while guaranteeing a moderate reliability, with packet error rate (PER) on the order of 10 -3 .

 Ultra-Reliable Low Latency Communications (URLLC):

This set has stringent requirements for capabilities such as throughput, latency and availability. In particular, it implicates for strict requirements on latency and reliability for mission critical communications, such as remote surgery, autonomous vehicles or the Tactile Internet; the latter can be defined by extremely low latency in combination with high availability, reliability and security and it will have a marked impact on business and society, introducing numerous new opportunities for emerging technology markets and the delivery of essential public services.

Some other examples include wireless control of industrial manufacturing or production processes, remote medical surgery, distribution automation in a smart grid, transportation safety, etc. URLLC transmissions are also intermittent, but the set of potential URLLC transmitters is much smaller than for mMTC. Supporting intermittent URLLC transmissions requires a combination of scheduling, so as to ensure a certain amount of predictability in the available resources and thus support high reliability; as well as random access, in order to avoid that too many resources being idle due to the intermittent traffic. Due to the low latency requirements, a URLLC transmission should be localized in time. Diversity, which is critical to achieve high reliability, can hence be achieved only using multiple frequency or spatial resources. The rate of a URLLC transmission is relatively low, and the main requirement is ensuring a high reliability level, with a PER typically lower than 10 -5 , despite the small block-lengths.

 Massive Machine Type Communications (mMTC):

This set is characterised by a very large number of connected devices typically transmitting a relatively low volume of non-delay-sensitive data. These devices are usually located in a small area, which may only send data sporadically, such as Internet of Things (IoT) use cases. Devices are required to be low cost, and have a very long battery life. The mMTC has been already developed as part of 3GPP Release 13/ Release 14 ([13], [START_REF]Technical Specification Group Services and System Aspects; Release 14 Description; Summary of Rel-14 Work Items (Release 14)[END_REF]) low power wide area (LPWA) technologies [START_REF]GSMA): 3GPP Low Power Wide Area Technologies -GSMA White Paper[END_REF], which includes Narrowband Internet of Things (NB-IoT). These are expected to "meet" most 5G mMTC requirements, while others that require more bandwidth with ultra-reliable low latency (full URLLC) will require the 5G Core deployment for full end-to-end latency reduction. Mission critical applications that are especially latencysensitive will also require wide coverage, which is highly unlikely in early 5G deployments, so this development is expected to come later. An mMTC device is active intermittently and uses a fixed, typically low, transmission rate in the uplink. A huge number of mMTC devices may be connected to a given base station (BS), but at a given time only an unknown (random) subset of them becomes active and attempt to send their data. The large number of potentially active mMTC devices makes it infeasible to allocate a priori resources to individual mMTC devices. Instead, it is necessary to provide resources that can be shared through random access. The size of the active subset of mMTC devices is a random variable, whose average value measures the mMTC traffic arrival rate.

The objective in the design of mMTC is to maximize the arrival rate that can be supported in a given radio resource. The targeted PER of an individual mMTC transmission is typically low, e.g., on the order of 10 -1 .

The grand objective of 5G wireless technology is to support the above three generic services with vastly heterogeneous requirements, that is eMBB, mMTC and URLLC. Service heterogeneity can be accommodated by network slicing, through which each service is allocated resources to provide performance guarantees and isolation from the other services [START_REF] Popovski | Wireless Network Slicing for eMBB, URLLC, and mMTC: A Communication-Theoretic View[END_REF]. Under a broader concept, the Mobile Broadband (MBB) enhancements aim to fulfil a number of innovative KPIs. These pertain to high data rates, high user density, high user mobility, highly variable data rates, deployment, and coverage. High data rates are driven by the increasing use of data for services such as streaming (e.g., video, music, and user generated content), interactive services (e.g., Augmented Reality (AR)), and IoT.

More specifically, to support the initial rollout of eMBB services, since March 2017 the 3GPP's RAN (Radio Access Network) Group committed to finalise the Non-Standalone (NSA) 5G NR variant by March 2018. In fact, the standard was approved in December 2017 [17]. The NSA mode considers the current 4G network, as supplemented by 5G NR carriers to boost data rates and decrease latency. The NSA 5G NR will utilize the existing LTE (Long Term Evolution) radio and core network as an "anchor" for mobility management and coverage, while adding a new 5G carrier. This is the configuration that will be the target of early 2019 deployments (in 3GPP terminology, this is NSA 5G NR deployment scenario Option 3). The Standalone (SA) variant was to be completed by September 2018 but was also finished early, in June 2018. The SA 5G NR implies full user and control plane capability for 5G NR, utilizing the 5G next-generation core network architecture (5G NGC) also being done in 3GPP. SA 5G NR technical specifications have been completed in June 2018 as part of 3GPP Release 15. Thus, eMBB can be assessed as "the first phase of 5G", which will be encompassed in the 3GPP Release 15 standard [START_REF]The 3rd Generation Partnership Project (3GPP)[END_REF]. The 5G Phase 2 will go beyond the eMBB services to more transformational URLLC and mMTC applications and will be included in Release 16, which is due to be completed at the end of 2019. By considering the case of connected cars as a characteristic example, the first phase of eMBB services will include enhanced in-vehicle infotainment, like real-time traffic alerts, high-speed internet access, streaming real-time video or playing games involving 3D 4K video. The second phase would involve autonomous vehicles on a mass scale capable of connecting to and interacting with other vehicles and/or with the nearby road infrastructure [START_REF]The 3rd Generation Partnership Project (3GPP): 3GPP[END_REF].

Following to the above context, the eMBB can be seen as a natural development of current 4G networks that will deliver faster data rates and, consequently, a better user experience than present mobile broadband services [START_REF]5G Network Architecture -A High Level Perspective[END_REF]. Nevertheless, it is expected that eMBB will evolve further and assure faster downloads to offer a progressively more seamless user experience that will conceal the quality of service we now experience from fixed broadband technologies. In the long run, it will also allow the provision of 360 o video streaming, rightly immersive Virtual Reality (VR) and Augmented Reality applications and a variety of modern solutions. In fact, if we assess the framework of potential eMBB use cases, there are three separate features that 5G has to guarantee: (i) Higher capacity, which implicates that broadband access has to be offered in densely populated areas, both indoors and outdoors (including, for example: city centres, office buildings or public venues such as malls, stadiums, conference centres, concerts sites, etc.); (ii) enhanced connectivity, implicating that broadband access has to be obtainable everywhere with the aim of offering a reliable user experience, and; (iii) higher user mobility that will allow for the provision of mobile broadband services in moving vehicles (including cars, buses, trains and planes). These categories of use cases will implicate for diverse requirements. In fact, as of the case of a "hotspot" scenario where there are many end-users (such as viewers at a sporting event), there will be a prerequisite for very high traffic capacity to satisfy the needs of all the participating end-users, but these will be static -or moving slowly-so the request for mobility will be assessed as low. However, when offering eMBB services/facilities to travellers in a highspeed train this be default necessitates for a high degree of mobility although the traffic capacity shall be lower than if compared to that of a hotspot scenario. Being somewhere between the above two essential use cases, the coverage of a wide area may implicate for a kind of "medium" level of mobility and although data throughput can be higher than the actual experience, it cannot be as high as in a hotspot.

eMBB within 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] as it is actually funded by the European Commission within the framework of the 5G-PPP Phase 3 [START_REF]5G Public Private Partnership (5G-PPP) website of Phase 3 projects[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 main scope of the 5G-DRIVE's effort is to conduct 5G trials addressing two specific scenarios of prime importance in modern 5G-oriented applications, as follows: (i) Scenario no 1 -enhanced Mobile Broadband (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 Vehicle-to-Vehicle (V2V) and the 3.5 GHz band for Vehicle-to-Network (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 Research and Innovation (R&I). Both scenarios shall be implemented in both regions 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.

The focus of the testing will be upon the radio interface except for some KPIs, features and use cases selected for eMBB scenarios considered in the 5G-DRIVE, which need to be quantified in an end-to-end configuration, (e.g., various latency measurements, network slicing, etc.). Subject to the availability of trial sites, the nonstandalone version or the standalone version of 5G NR will be chosen accordingly. In the NSA architecture, the trials need to consider collocated and non-collocated NR and LTE radio suites to test the dual connectivity between both technologies.

eMBB Trials Scenarios in the 5G-DRIVE Framework

The collected data of tests will be compared with the targeted requirements and expected performance defined in 3GPP release 15 or release 16 or in the ITU-RM.2410-0 [START_REF]Minimum requirements related to technical performance for IMT-2020 radio interface(s)[END_REF], subject to the individual requirement of each trial. The primary aim of building pre-commercial end-to-end testbeds is to test 5G NR with a focus on eMBB using close-to-commercial equipment in realistic settings, which can "reflect" near real-life network performance. Table 1. Deployment attributes for the eMBB trials scenarios within the 5G-DRIVE framework (i.e.: indoor hotspot, dense urban and urban macro, accordingly).

Attributes Indoor Hotspot scenario

Dense Urban scenario

Urban macro scenario

Carrier Frequency Sub 6 GHz (around 3.5 GHz) Sub In the 5G-DRIVE project, the collected test data from the two regions (EU and China) will be analysed and compared for interoperability purposes. Therefore, some general trial setup requirements should be listed so as that to harmonize the trial development facilities at the various trial sites. The trials planned will cover several deployment scenarios, which include indoor hotspot, dense urban deployment and macro cells deployment. In order to achieve important results, there would have to be enough sites to fulfil the following: (i) Areas with excellent signal to noise ratio (SNR) and negligible interference for peak performance measurements; (ii) areas with poor SNR for minimum performance measurements, and; (iii) areas with low to very high interference for realistic interference measurements. For a dense urban macro deployment, the minimum number of sites would be different to that in an indoor or rural environment in order to achieve the above.

Table 1 illustrates descriptions of the three scenarios, as also provided by NGMN in [START_REF]Next Generation Mobile Networks (NGMN) Alliance: Definition of the Testing Framework for the NGMN 5G Pre-Commercial Networks Trials -Version 1.0[END_REF], which will be employed as reference setups for the 5G-DRIVE trial sites, together with the relevant attributes. The indoor hotspot scenario focuses on high user density and high capacity/throughput in indoor small coverage areas. The dense urban scenario focuses on high use density and high traffic loads in city centres with outdoor coverage scenario. Finally, the urban macro scenario focuses on continuous coverage in urban areas.

KPIs for the Assessment of the eMBB Trials

In the following subsections we list the relevant 5G KPIs related to the radio access network and use as a reference for different trial sites to conduct trials under the same baseline. The sources of the KPI definitions come from various prior works such as: (i) the ITU-R M.2410-0 [START_REF]Minimum requirements related to technical performance for IMT-2020 radio interface(s)[END_REF]; (ii) the NGMN 5G Trial and Testing Initiative Precommercial Network Trials Framework Definition [START_REF]Next Generation Mobile Networks (NGMN) Alliance: Definition of the Testing Framework for the NGMN 5G Pre-Commercial Networks Trials -Version 1.0[END_REF]; (iii) 5G-PPP KPI definitions ([25]- [START_REF] Cattoni | An End-to-End Testing Ecosystem for 5G the TRIANGLE Testing House Test Bed[END_REF]), and; (iv) the framework proposed by 3GPP in TS 28.554 [START_REF]The 3rd Generation Partnership Project (3GPP): 3GPP TS 28[END_REF]. Note that the KPIs defined in the above documents are used to provide a throughout performance evaluation of 5G systems, within a broader scope. However, considering the capability and availability of the trial facilities in the 5G-DRIVE project, only a selected set of KPIs will be later evaluated in this project. For this reason, we only list the KPIs relevant to the 5G-DRIVE trials and provide the test procedure. By following the common test procedure, the trial results from different trial sites can be compared. A selected set of KPIs are planned to be measured in both the 5G-DRIVE and the Chinese "twin" project and results will be reported in the joint trial reports. In the following, we first provide the definition of the selected KPIs and describe the test setup, test procedure, evaluation criteria and reporting process of each KPI.

Data rate is the number of bits transmitted through the system per unit of time. There are different terms to represent the data rate. Some documents use the term "data rate", while others use the term "throughput". They have the same meaning in this document. Thus otherwise explicitly mentioned, they are used exchangeable. There are several data rate KPIs related to the performance of 5G systems. In the 5G-DRIVE project, we will "address" peak data rate, user experienced data rate, and celledge user data rate. The definitions of these are given below.

Peak data rate in bit/s is the maximum achievable data rate under ideal conditions to a single mobile station, to which all assignable radio resources for the corresponding link direction are assigned, except the radio resources used for PHY (Physical) layer synchronization, reference and pilot signals, guard bands and guard times. In some documents, it is referred to the peak user throughput. The peak data rate can be derived from the simulation and theoretical analysis. In this project, we will measure the data rate through the trials. In this context, it is the maximum DL (Downlink) / UL (Uplink) data rate achievable for a single user located at the best location within a cell. Peak data rate is defined for a single mobile station. In a single band, is related to the peak spectral efficiency in that band. Let W denote the channel bandwidth and SEp denote the peak spectral efficiency in that band. Then the user peak data rate Rp is given by:

(1) Peak spectral efficiency and available bandwidth may have different values in different frequency ranges. In case bandwidth is aggregated across multiple bands, the peak data rate will be summed over the bands. Therefore, if bandwidth is aggregated across Q bands then the total peak data rate is [START_REF]European Commission: 5G: Challenges, Research Priorities, and Recommendations -Joint White Paper[END_REF] where Wi and SEpi (i = 1,…Q) are the component bandwidths and spectral efficiencies respectively. The peak data rate is defined for the purpose of evaluation in the eMBB usage scenario. In [START_REF]Minimum requirements related to technical performance for IMT-2020 radio interface(s)[END_REF], the minimum requirements for peak data rate are as follows: (i) Downlink peak data rate is 20 Gbit/s; (ii) Uplink peak data rate is up to 10 Gbit/s. Note that these requirements make assumptions on the 5G spectrum used in the system. In the project, the measurement results will depend on the availability of the spectrum and the trial setting, which may result in lower peak data rate values.

User experienced data rate is the 5% point of the cumulative distribution function (CDF) of the user throughput. User throughput is defined as the number of correctly received bits, i.e. the number of bits contained in the service data units (SDUs) delivered to Layer 3, over a certain period of time. In case of one frequency band and one layer of transmission reception points (TRxP), the user experienced data rate could be derived from the 5 th percentile user spectral efficiency through the following equation. Let W denote the channel bandwidth and SEuser denote the 5 th percentile user spectral efficiency. Then the user experienced data rate, Ruser is given by:

(3)

In case bandwidth is aggregated across multiple bands (one or more TRxP layers), the user experienced data rate will be summed over the bands. The target values for the user experienced data rate are as follows in the Dense Urban -eMBB test environment: (i) Downlink user experienced data rate is 100 Mbit/s; (ii) Uplink user experienced data rate is 50 Mbit/s. These values are defined assuming supportable bandwidth as described in [START_REF]Minimum requirements related to technical performance for IMT-2020 radio interface(s)[END_REF] for each test environment. In the project, the measurement results will depend on the availability of the bandwidth.

Cell-edge user data rate:

In 3GPP, the cell edge user data rate is defined as the 5% point of the CDF of user's average data rate. The user's average data rate is linked to the average spectral efficiency, used bandwidth and the number of TRxP used in the system. The average spectral efficiency is the aggregate throughput of all users divided by the channel bandwidth of a specific band divided by the number of TRxPs and is measured in bit/s/Hz/TRxP. In the NGMN trial framework document [START_REF]Next Generation Mobile Networks (NGMN) Alliance: Definition of the Testing Framework for the NGMN 5G Pre-Commercial Networks Trials -Version 1.0[END_REF], it refers to cell edge coverage throughput, which is defined as the user data rate at the location of the cell edge with 1-3 dB lower path loss compared to control channel coverage limit (path loss limit).

Latency: As 5G will support ultra-low latency services, the latency is an important parameter to be evaluated in trials. In communications, the latency is the time interval for a bit of data, usually a data packet, to travel across the network from one node or endpoint to another. According to the purpose of evaluation, there are two types of latencies: user plane latency and control plane latency. Depending on which part of user plane is targeted, the user plane latency can be the end-to-end latency, RAN latency and core latency [START_REF]Next Generation Mobile Networks (NGMN) Alliance: Definition of the Testing Framework for the NGMN 5G Pre-Commercial Networks Trials -Version 1.0[END_REF]. The control plane latency under the 5G context is tightly related to the radio resource control (RRC) state transitions in the control plane. In this work, we only give the brief definition of these two latencies.

User plane latency: At the application level, the most important latency is the endto-end latency, which is the maximum tolerable elapsed time from the instant a data packet is generated at the source application to the instant it is received by the destination application. If direct mode is used, this is essentially the maximum tolerable air interface latency. If infrastructure mode is used, this includes the time needed for uplink, any necessary routing in the infrastructure, and downlink. The RAN latency refers to the delay occurring in the RAN. It includes the time it takes to successfully deliver an application layer packet/message from the radio protocol layer 2/3 service data unit ingress point to the radio protocol layer 2/3 SDU egress point via the radio interface in both uplink and downlink directions, where neither device nor base station reception is restricted by discontinuous reception mode (DRX). The core latency refers to the round trip time between gNB and the Application Server. The minimum requirements (recommended by [START_REF]Minimum requirements related to technical performance for IMT-2020 radio interface(s)[END_REF]) for the RAN latency are 8 ms for eMBB assuming unloaded conditions (i.e. a single user) for small Internet protocol (IP) packets (e.g. 0 byte payload + IP header), for both downlink and uplink.

Control plane latency refers to the transition time at the UE side from the idle or inactive RRC state to the start of continuous data transfer (e.g. Active state) [START_REF]The 3rd Generation Partnership Project (3GPP): 3GPP TR 38[END_REF]. The minimum requirement for control plane latency is 20 ms.

Cell capacity is the KPI to evaluate the aggregate capacity of multiple users served by a cell. It can be measured by the cell peak throughput and cell average throughput. In the measurement, multiple UEs are placed in the different locations to represent different propagation environments. The results of cell capacity can be explained as the spectrum efficiency. The cell peak throughput represents the maximum transmission capability of a single cell, without inter-cell interference from other cells. In the measurement of this KPI, all UEs will be placed in the locations with good channel quality. The cell average throughput represents the average transmission capability of a single cell, with the inter-cell interference from neighbouring cells. The tested UEs will be placed in different locations with different level of channel qualities. Among the UEs, the channel qualities to the gNB will be divided to four types: excellent, good, medium, and poor. The average capacity test should consider both UL and DL interference from other cells. In the cell capacity measurement, both single cell and multi-cell should be considered to reflect the cell capacity in different inter-cell interference.

Spectrum efficiency refers to the data rate that can be transmitted over a given bandwidth in a communication system. In the 5G-DRIVE project, it is the data rate per UE at given condition divided by the bandwidth used by UE for corresponding data rate. It is an important performance KPI of 5G system. The spectrum efficiency is also a good indicator to show the performance difference between 4G and 5G systems. Since the spectrum efficiency is obtained from the data rate measurement under a certain bandwidth configuration, the results from user throughput and cell capacity measurement can be used to derive the spectrum efficiency. The project will take into account three spectrum efficiency KPIs: (i) Peak spectral efficiency, derived from peak user rate test results; (ii) average spectral efficiency, derived from cell average throughput test results, and; (iii) cell edge spectrum efficiency, derived from Cell-edge user data rate test results.

Coverage:

The coverage area of a mobile system is the geographic area where the base station and the user device can communicate. The coverage is evaluated by the communication availability, in which the signal strength of a cell needs to exceed a defined minimum threshold and the achieved service quality, in which a minimum data rate in case of mobile data services or a minimum speech quality in case of voice services is required. The coverage depends on several factors, including the environment, buildings, technology and radio frequency and most importantly for two-way telecommunications the sensitivity and transmit efficiency including maximum output power of the base station and the end-user device. To evaluate the coverage of 5G system, the following aspects need to be taken into account: coverage gap between 4G and 5G, beamforming capability of 5G gNB (next generation NodeB) [START_REF]The 3 rd Generation Partnership Project (3GPP)[END_REF], downlink data/control channel coverage difference, and uplink coverage enhancement at UE. Depending on the setting of the trial networks, the following coverage can be evaluated: outdoor single-cell coverage, outdoor multi-cell continues coverage, outdoor to indoor coverage, and indoor coverage. Considering the capability of the trial sites in the project, we will focus on the outdoor single-cell coverage and indoor coverage evaluation.

Mobility is the maximum mobile station speed at which a defined QoS can be achieved (in km/h).The following classes of mobility are defined: (i) Stationary: 0 km/h; (ii) pedestrian: 0 km/h to 10 km/h; (iii) vehicular: 10 km/h to 120 km/h, and; (iv) high speed vehicular: 120 km/h to 500 km/h. Reliability relates to the capability of transmitting a given amount of traffic within a predetermined time duration with high success probability. Reliability is the success probability of transmitting a layer 2/3 packet within a required maximum time, which is the time it takes to deliver a small data packet from the radio protocol layer 2/3 SDU ingress point to the radio protocol layer 2/3 SDU egress point of the radio interface at a certain channel quality. While the reliability is one of the main KPIs to evaluate URLLC performance, we will evaluate this KPI under eMBB trials. The minimum requirement for the reliability is 1-10 -5 success probability of transmitting a layer 2 PDU (protocol data unit) of 32 bytes within 1 ms in channel quality of coverage edge for the Urban Macro-URLLC test environment, assuming small application data (e.g. 20 bytes application data + protocol overhead). The larger packet sizes, e.g. layer 2 PDU size of up to 100 bytes, may be considered in the trials.

Area traffic capacity is the total traffic throughput served per geographic area (in Mbit/s/m 2 ). The throughput is the number of correctly received bits, i.e. the number of bits contained in the SDUs delivered to Layer 3, over a certain period of time. This can be derived for a particular use case (or deployment scenario) of one frequency band and one TRxP layer, based on the achievable average spectral efficiency, network deployment (e.g. TRxP (site) density) and bandwidth. Let W denote the channel bandwidth and ρ the TRxP density (TRxP/m 2 ). The area traffic capacity Carea is related to average spectral efficiency SEavg through equation, .

(4)

In case bandwidth is aggregated across multiple bands, the area traffic capacity will be summed over the bands. The target value for Area traffic capacity in downlink is 10 Mbit/s/m 2 in the Indoor Hotspot -eMBB test environment. The conditions for evaluation for the test environment are described in [START_REF]Minimum requirements related to technical performance for IMT-2020 radio interface(s)[END_REF].

Conclusion

In the present work we have first delineate the importance and the multiple benefits coming from the 5G evolution, affecting both market growth and innovation. In particular, within the context of the effort for the development of the 5G NR, we have identified and discussed the critical role that eMBB has to realize, as a "promoter" of the first phase of the 5G, in parallel with standardization actions coming from the 3GPP. Thus, we have discussed the three fundamental sets of use cases comprising of eMBB, URLLC and mMTC and then we have primarily focused upon the eMBB that can be initially assessed as an extension to existing 4G services and will be among the first 5G services, which could be commercially available as early as 2019. Then we have focused on the specific framework of the actual 5G-DRIVE project funded by the European Commission, which promotes a framework for international research and cooperation between the European Union and China. Among the two core scenarios of this project is the realization of eMBB trials on the 3.5GHz band for the support of suitable services such as AR/VR. Based on the 5G-DRIVE proposed methodology as well as to approaches proposed by the actual trends and/or bibliography, we have initially distinguished three scenarios which include indoor hotspot, dense urban deployment and macro cells deployment and we have propose corresponding attributes. With the aim of creating a framework for the effective support of the scheduled trials we have then identified and briefly discussed several corresponding KPIs. The 5G-DRIVE trials for the eMBB validation will be aligned to existing practices in order to make results comparable and meaningful.

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).


Georgios Drakopoulos

Xenophon Liapakis
email: liapakisx@interamerican.com

Evaggelos Spyrou
email: espyrou@iit.demikritos.gr

Giannis Tzimas
email: tzimas@teimes.gr

Phivos Mylonas
email: fmylonas@ionio.gr

Spyros Sioutas
email: sioutas@ceid.upatras.gr

Computing Long Sequences Of Consecutive Fibonacci Integers With TensorFlow

Keywords: Fibonacci sequence, Linear recurrence equations, Finite differences, Google TensorFlow, GPU computing, Parallel computing

Fibonacci numbers appear in numerous engineering and computing applications including population growth models, software engineering, task management, and data structure analysis. This mandates a computationally efficient way for generating a long sequence of successive Fibonacci integers. With the advent of GPU computing and the associated specialized tools, this task is greatly facilitated by harnessing the potential of parallel computing. This work presents two alternative parallel Fibonacci generators implemented in TensorFlow, one based on the well-known recurrence equation generating the Fibonacci sequence and one expressed on inherent linear algebraic properties of Fibonacci numbers. Additionally, the question of using lookup tables in conjunction with spline interpolation or direct computation within a parallel context for the computation of the powers of known quantities is explored. Although both parallel generators outperform the baseline serial implementation in terms of wallclock time and FLOPS, there is no clear winner between them as the results rely on the number of integers generated. Additionally, replacing computations with a lookup table degrades performance, which can be attributed to the frequent access to the shared memory.

Introduction

The sequence of Fibonacci integers f k appears often in a broad spectrum of engineering applications including coding theory, cryptography, simulation, and software management. Additionally, Fibonacci numbers are very closely tied to the golden ratio ϕ which is frequently encountered in nature, such as in population growth models and in botanics. Moreover, in architecture ϕ is almost considered synonymous to harmony. Thus, Fibonacci integers are arguably among the most signifiant sequences. Although their defining linear recurrence equation is simple, serially generating a long sequence of consecutive Fibonacci integers is by no means a trivial task.

However, with the advent of GPU computing, the efficient parallel generation of f k has been rendered feasible. Indeed, by exploiting known properties of the Fibonacci integers it is possible to build parallel generators which exploit the underlying hardware potential to a great extent, achieving low response times. This requires specialized scientific software such as TensorFlow which not only contains very efficient libraries, but also facilitates the development of high quality custom source code.

The primary research contribution of this work is twofold. First, it lays the groundwork for two parallel Fibonacci integer generators, one based on a closed form for each number in the sequence and one based on certain linear algebraic properties of Fibonacci integer pairs. The performance of the two proposed generators developed in TensorFlow for Python is evaluated in terms of both total turnaround time and FLOPS against a serial implementation with the same software tools. Second, it explores the question whether it is worth substituing the computation of known quantities with a lookup table.

This conference paper is structured as follows. Section 2 reviews current scientific literature regarding the computational aspects of Fibonacci numbers. Their fundamental properties are described in section 3. The TensorFlow implementation is presented in section 4, whereas future research directions are outlined in section 5. Table 1 summarizes the notation of this work. Concerning notation, matrices and vectors are depicted with boldface uppercase and boldface lowercase respectively, whereas roman lowercase is reserved for scalars.

Previous Work

The Fibonacci sequence of integers, examined among others in [START_REF] Capocelli | Fibonacci facts and formulas[END_REF] and [START_REF] Takahashi | A fast algorithm for computing large Fibonacci numbers[END_REF], has perhaps the most applications not only in computer science and in engineering but in science as a whole. Closed forms for the spectral norms of circulant matrics whose entries are either Fibonacci or Lucas integers are derived in [START_REF] İpek | On the spectral norms of circulant matrices with classical Fibonacci and Lucas numbers entries[END_REF]. In data structure analysis the Fibonacci heap [START_REF] Fredman | Fibonacci heaps and their uses in improved network optimization algorithms[END_REF] and the associated pairing heap [START_REF] Fredman | The pairing heap: A new form of self-adjusting heap[END_REF] have efficient search and insertion operations with numerous applications such as network optimization. A pair of successive Fibonacci numbers are known to be the worst case in Euclidean integer division algorithm as shown in [START_REF] Dixon | The number of steps in the Euclidean algorithm[END_REF] as well as in [START_REF] Sorenson | An analysis of Lehmer's Euclidean GCD algorithm[END_REF]. Fibonacci numbers play a central role in estimating task duration and, consequently, task difficulty in scrum based software engineering methodologies [START_REF] Schwaber | The scrum guide[END_REF][27], including inaccurate estimation discovery [START_REF] Raith | Identification of inaccurate effort estimates in agile software development[END_REF] and using agile methodologies to predict student progress [START_REF] Rodríguez | Measuring the impact of agile coaching on students performance[END_REF]. Moreover, Fibonacci numbers are very closely linked to the golden ratio ϕ6 as well as to symmetry of many geometric shapes, the latter having important implications in group theory [START_REF] Dunlap | The golden ratio and Fibonacci numbers[END_REF].

Many identities in combinatorics regarding Fibonacci numbers can be found in [START_REF] Zhang | Some identities involving the Fibonacci numbers[END_REF] as well as in the most recent works [START_REF] Bolat | On the properties of k-Fibonacci numbers[END_REF] and [START_REF] Koshy | Fibonacci and Lucas numbers with applications[END_REF]. Finally, the Lucas sequence k is closely associated with the Fibonacci sequence f k since the two integer sequences constitute a Lucas complementary pair and share similar properties such as growth rate and generation mechanism [3][4].

TensorFlow, originally developed by Google for massive brain circuit simulation, is an open source computational framework whose algorithmic cornerstone is the dataflow paradigm as described in [START_REF] Abadi | TensorFlow: A system for large-scale machine learning[END_REF], [START_REF] Abadi | TensorFlow: Learning functions at scale[END_REF], or [START_REF] Goldsborough | A tour of TensorFlow[END_REF]. A library for generating Gaussian processes in TensorFlow is presented in [START_REF] Matthews | GPflow: A Gaussian process library using TensorFlow[END_REF]. For a genetic algorithm implementation in TensorFlow for heuristically discovering community structure, a problem examined in [START_REF] Drakopoulos | Graph communities in Neo4j: Four algorithms at work[END_REF], in large multilayer graphs, such as those presented in [START_REF] Drakopoulos | Towards a framework for tensor ontologies over Neo4j: Representations and operations[END_REF] and in [START_REF] Drakopoulos | Tensorbased semantically-aware topic clustering of biomedical documents[END_REF], see [START_REF] Drakopoulos | A genetic algorithm for spatiosocial tensor clustering: Exploiting TensorFlow potential[END_REF]. In [START_REF] Drakopoulos | Blockchain for mobile health applications: Acceleration with GPU computing[END_REF] the ways insurance and digital health markets can benefit from blockchain and GPU computing are explored. For a path and triangle based graph resilience metric in TensorFlow see [START_REF] Drakopoulos | A graph resilience metric based on paths: Higher order analytics with GPU[END_REF]. A very popular front end for the low level TensorFlow is keras, which allows the easy manipulation of neural network layers, including connecticity patterns and activation functions [START_REF] Chollet | Keras: Deep learning library for theano and TensorFlow[END_REF]. Model training and prediction generation is done also easily in keras in four stages [START_REF] Ketkar | Introduction to keras[END_REF]. Convolutional kernels whose lengths depend on their relative location inside the neural network architecture for computational vision purposes implemented in keras are introduced [START_REF] Chollet | Xception: Deep learning with depthwise separable convolutions[END_REF].

Fibonacci Numbers

The n-th integer in the Fibonacci sequence f n is defined as:

f n = f n-1 + f n-2 , f 0 = 0, f 1 = 1 (1)
Theorem 1. The n-th Fibonacci number f n has the closed form:

f n = 1 √ 5 1 + √ 5 2 n - 1 √ 5 1 - √ 5 2 n = ϕ n + (1 -ϕ) n √ 5 (2)
Proof. The characteristic polynomial of (1) is:

z 2 = z + 1 ⇔ z 2 -z -1 = 0 (3)
And its two real and distinct roots are:

r 0,1 = 1 ± √ 5 2 (4)
Therefore, it follows that:

f n = α 0 r 0 + α 1 r 1 = 1 √ 5 1 + √ 5 2 n - 1 √ 5 1 - √ 5 2 n (5)
The constants α 0 and α 1 are computed using the initial conditions derived by the first two Fibonacci numbers as follows:

α 0 + α 1 = f 0 = 0 α 0 r 0 + α 1 r 1 = f 1 = 1 (6)
The above conditions yield:

α 0 = 1 r 0 -r 1 = 1 √ 5 α 1 = -α 0 = 1 r 1 -r 0 = - 1 √ 5 (7)
Another way to prove theorem 1 is the following:

Proof. Another way to directly compute the n-th Fibonacci number f n is to rewrite the Fibonacci definition of equation ( 1) and the identity f n = f n combined in matrix-vector format as follows:

f n f n-1 = 1 1 1 0 f n-1 f n-2 = A f n-1 f n-2 = Af n-2 (8)
The eigenvalues λ 1 and λ 2 (recall that det (A) = λ 1 λ 2 and that tr (A) = λ 1 +λ 2 ) and the corresponding eigenvectors e 1 and e 2 are:

λ 1 = 1 + √ 5 2 = ϕ λ 2 = 1 - √ 5 2 = 1 -ϕ (9)
e 1 = 1 1 + λ 2 1 λ 1 1 e 2 = 1 1 + λ 2 2 λ 2 1 ( 10
)
Notice that e 1 2 = 1 and e 2 2 = 1 and, additionally, e T 1 e 2 = 0. From the spectral decomposition of A it follows that:

A = λ 1 e 1 e T 1 + λ 2 e 2 e T 2 A n = λ n 1 e 1 e T 1 + λ n 2 e 2 e T 2 A n f 0 = f n-1 = λ n 1 e T 1 f 0 e 1 + λ n 2 e T 2 f 0 e 2 (11)
Observe that the first element of vector f n-1 is f n and it is equal to:

f n = λ n+1 1 1 + λ 1 1 + λ 2 1 + λ n+1 2 1 + λ 2 1 + λ 2 2 (12)
Finally, the structure of A n can be shown by induction to be:

A n = f n f n-1 f n-1 f n-2 (13)
Despite form [START_REF] Bolat | On the properties of k-Fibonacci numbers[END_REF], Fibonacci numbers as evident by the initial conditions and their generation mechanism. Another way to see this is the following theorem: Theorem 2. Fibonacci numbers are integers.

Proof. Applying the Newton binomial theorem to (5) yields:

f n = 1 √ 5 n k=0 n k 2 -n √ 5 k -- √ 5 k γ k ( 14
) When k ≡ 0 (mod 2) then γ k is zero, whereas if k ≡ 1 (mod 2) then γ k equals 2 • 5 k+1
2 . Therefore:

f n = k n k 2 1-n 5 k 2 , k ≡ 1 (mod 2) ( 15
)

TensorFlow Implementation

As stated earlier, TensorFlow relies on the dataflow algorithmic paradigm, which essentially utilizes a potentially large operations graph in order to break down the desired computational task to smaller manageable components. Dataflow graphs have the following properties:

-Vertices represent a wide array of mathematical operations including advanced ones such as eingenvector computation, singular value decomposition, and least squares fitting. -Edges describe the directed data flow between operation results.

-The operands between the various graph operations are tensors of aritrary dimensions as long as they are compatible.

TensorFlow r1.12 was installed to Ubuntu 18.04 LTS for Python 3.6 using the pip package installer. An NVIDIA Titan Xp GPU based on Pascal architecture was available in the system and was successfully discovered by TensorFlow as gpu0.

Three Fibonacci generators were implemented in total. Each such generator yields a batch consisting of the first n consecutive Fibonacci integers. In the experiments, n ranged from 2 to 1024 with an exponentially increasing distance between two successive batch sizes. Since the particular GPU has 3840 CUDA cores, the parallelism potential is high. The generators are:

-A serial implementation which consists of a single loop which adds one new Fibonacci number with each pass. -A parallel implementation which directly computes the k-th element of the sequence f k based on equation ( 11). -A second implementation which relies on the slightly simpler closed expression of equation ( 2).

Figure 1 shows the total number of floating point operations which were required for each batch size. The values are the arithmetic mean of ten executions for each batch size. Preceding each such execution there was a trial run not taken into consideration in the final results which served the single purpose of loading the data into system cache. Since the serial implementation is a loop, the number of additions is linear in terms of n. On the contrary, the parallel implementations require a number of auxiliary floating point operations, most notably the exponentiation of certai parameters. Thus, they require more operations whose number is a polynomical function, approximately quadratic, of batch size, with the second generator clearly always being more more expensive in terms of operations.

However, the fact that a generator requires more floating point operations does not necessarily makes it slower in terms of total execution time. Instead, the results shown in figure 2 indicate that both parallel generators achieve considerably lower wallclock execution time in milliseconds. Since the computation of f k is GPU-bound process and the design of both parallel generators entail very low communication across the memory hierarchy, ordinary wallclock time in this case consists almost entirely of time spent to actual computations.

Notice that, unlike the previous figure, no parallel implementation appears to be ideal for every batch size. Specifically, the second implementation is better for lower sized batches, whereas the first one becomes more preferable as batch size grows despite its more complex formula. This can be attributed to the fact that the second generator achieves more locality as certain parameters are common across each batch size.

This difference in performance can be also seen by dividing the number of floating point operations to the wallclock time, yielding an approximation of the FLOPS for each generator as seen in figure 3. Although by no means a single absolute benchmark, especially within a parallel computation context, FLOPS are in this case indicative since the algorithmic core is purely computational. The difference from the serial implementation is now obvious, as is also the fact that the second generatorfor large batches performs better.

Notice that in both parallel implementations appear many consecutive powers of known quantities. In order to save floating point operations, a lookup table could have been used according the design principles found for instance in [START_REF] Fateman | Lookup tables, recurrences and complexity[END_REF]. In order to evaluate the impact of relying on a lookup table to the total FLOPS for the two parallel generators, two variants of each were also implemented. The first version used locally half the known quantities required, whereas the second only only one quarter of them. In both cases, and in order to achieve comparable result accuracy for fairness reasons, spline interpolation was used. As it can be seen from figure 4, the introduction of a lookup table for both generators downgraded their FLOPS counter. This can be explained from the facts that TensorFlow has an efficient multipication algorthm especially for large numbers and that frequently accessesing the shared GPU memory eventually slowed computations down.

Conclusions And Future Work

This conference paper presented two parallel Fibonacci integer generators for TensorFlow running over Python 3.6 and an NVIDIA Titan Xp GPU and discussed certain implementation aspects. Both generators yield batches of n integers, with n ranging from 2 to 1024. The maximum batch size is smaller than the number of cores in the GPU, increasing thus parallel potential. The baseline was a serial implementation for TensorFlow consisting of a single loop generating one new Fibonacci integer in each pass. The primary finding of the experiments is the superior performance of parallel generators. Although requiring more floating point operations in total, both parallel implementations outperform the baseline in terms of wallclock time and of FLOPS. This is attributed to the efficient use of parallelism. A secondary finding was that replacing actual computations with frequent accesses to a shared lookup table led to lower FLOPS values. This can be explained by the latency caused by a large number of threads asking for the same information.

Concerning future research directions, there is a number of options which can be followed. Experiments with larger batch sizes should be conducted, especially with sizes which exceed the number of GPU cores. Additionally, more algorithmic schemes should be tested, such as those constructing Fibonacci integers bitwise, as they may led in generators with higher parallelism. Finally, the performance of the proposed algorithms to other parallel architectures can be evaluated, in order to understand whether a given hardware architecture is more appropriate for particular algorithmic principles. Additionally, this conference paper is part of Project 451, a long term research initiative whose primary objective is the development of novel, scalable, numerically stable, and interpretable tensor analytics.

Moreover, the authors gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.

Fig. 1 .

 1 Fig. 1. Number of operations vs batch size of Fibonacci numbers.

Fig. 2 .

 2 Fig. 2. Wall clock time (msec) vs batch size of Fibonacci numbers.

Fig. 3 .

 3 Fig. 3. Approximate FLOPS vs batch size of Fibonacci numbers.

Table 1 .

 1 Notation of this conference paper.

	Symbol Meaning
	=	Definition or equality by definition
	x	Norm of vector x
	det (A) Determinant of matrix A
	tr (A) Trace of matrix A
	ϕ	Golden ratio
	sn	Sequence of integers sn

OEIS sequence A001622

Acknowledgements

This research was conducted within the project "Development of technologies and methods for cultural inventory data (ANTIKLIA)" under the EPAnEK


Juan Sánchez-González

Jordi Pérez-Romero

Oriol Sallent

A New Self-planning Methodology Based on Signal Quality and User Traffic in Wi-Fi Networks

Keywords: Self-planning, Wi-Fi, genetic algorithms, data analytics

Wi-Fi networks have become one of the most popular technologies for the provisioning of multimedia services. Due to the exponential increase in the number of Access Points (AP) in these networks, the automation of the planning, configuration, optimization and management tasks has become of prime importance. The efficiency of these automated processes can be improved with the inclusion of data analytics mechanisms able to process the large amount of data that can be collected from Wi-Fi networks by powerful monitoring systems. This paper presents a new self-planning methodology that collects historical network measurements and extracts knowledge about user signal quality and traffic demands to determine adequate AP relocations. The performance of the proposed AP relocation methodology based on a genetic algorithm is validated in a real Wi-Fi network. The proposed approach can be easily adapted to other contexts such as small cell networks.

Introduction

In the last years, mobile users are increasingly demanding new multimedia services (i.e. high quality video, online multimedia applications, augmented/virtual reality, etc.) that have high bandwidth demands and strict Quality of Service (QoS) requirements. To cope with this demand, network densification through the deployment of small cells operating cellular technologies (e.g. 4G/5G), complemented with Wi-Fi hotspots, which benefit from the unlicensed use of the spectrum, becomes a relevant solution. Indeed, the popularity of Wi-Fi technology among mobile users makes it a competitive option for serving multimedia demands. The amount of traffic of IEEE 802.11 (i.e. Wi-Fi) has suffered a high increase in the last years. It is expected that by 2021, 63% of the global cellular data traffic will be offloaded to Wi-Fi or small cell networks [START_REF]Cisco Visual Networking Index[END_REF]. Globally, there will be nearly 549 million public Wi-Fi hotspots by 2022, up from 124 million in 2017, a fourfold increase. Such a high increase in the amount of traffic volume and the number of Wi-Fi Access Points (AP)/Small Cells makes much more complex the planning, configuration, optimization and management tasks of these networks. For this reason, the automation of these processes is of paramount importance. Legacy systems such as 2G/3G/4G already started the path towards a higher degree of automation through the introduction of Self-Organizing Network (SON) functionalities [START_REF] Ramiro | Self-Organising Networks. Self-planning, self-optimization and selfhealing for GSM, UMTS and LTE[END_REF], namely self-planning, self-optimization and selfhealing. However, the lessons learnt from cellular SON cannot be directly applied to Wi-Fi systems. Several works such as [START_REF] Gacanin | Wi-Fi Self-Organizing Networks: SON challenges and use cases[END_REF][4] identify challenges and use cases to introduce SON paradigms in Wi-Fi networks. Concerning self-planning aspects in Wi-Fi/Small Cells networks, most of the works found in the literature focus on the determination of the most adequate AP location and the frequency channel allocation and AP transmitted power. As an example, [START_REF] Muñoz | Self-Dimensioning and Planning of Small Cell Capacity in Multi-Tenant 5G Networks[END_REF] presents a framework for automated cellplanning in multi-tenant Small Cells networks that considers actions such as adding/removing channels and Small Cells. Similarly, [START_REF] Politi | An optimization model for the indoor access point placement problem with different types of obstacles[END_REF] presents a theoretical model to determine the most adequate AP location by an accurate characterization of impact of the environment (walls, doors, etc.) on the signal propagation. Other works, such as [START_REF] Atawia | Self-deployment of future indoor Wi-Fi networks: An artificial intelligence approach[END_REF], deal with the determination of the most appropriate locations of Wi-Fi extenders.

On the other side, it is envisaged that the efficiency of automated network management processes can be substantially enhanced through the exploitation of powerful data analytics technologies able to process the large amount of data that can be collected from Wi-Fi networks by powerful monitoring systems. In this respect, (big) data monitoring and analytics combined with SON technologies will become fundamental in order to enable full scale automated network deployment and optimization [START_REF] Liu | On Big Data Analytics for Greener and Softer RAN[END_REF]. The monitoring system provides the ability to collect information about the network resources and performance of the services while the analytics system allows extracted knowledge of the collected data in order to support different decision making processes over the network. As an example, [START_REF] Perez-Romero | Monitoring and Analytics for the Optimisation of Cloud Enabled Small Cells[END_REF] presents a framework for data monitoring and analytics for the optimization of Cloud Enabled Small Cells in the context of the 5G ESSENCE project. The data collected by the monitoring system can be valuable for the extraction of knowledge related to user habits, user mobility patterns, spatio/temporal user resource demands, etc. Some examples are presented in [START_REF] Kim | Analysing the mobility, predictability and evolution of wlan users[END_REF] that evaluates user mobility models in a University campus, in [START_REF] Ruiz-Ruiz | Analysis methods for extracting knowledge from large-scale wifi monitoring to inform building facility planning[END_REF] that makes use of Wi-Fi measurements to analyze mobility patterns in a Hospital or in [START_REF] Redondi | Understanding the Wifi usage of University Students[END_REF] that assesses the spatio/temporal traffic correlation of Wi-Fi traffic data measurements and presents a supervised learning approach to classify the different campus spaces.

Within this context, this paper presents a new self-planning methodology to determine adequate relocations of APs to improve the provided coverage/quality at those regions of the network with low signal quality and high traffic demands. The methodology makes use of a monitoring system that collects historical network measurements and an analytics system that extracts valuable knowledge from the collected data. This knowledge feeds the AP relocation strategy that is based on a genetic algorithm. The performance of the proposed relocation actions are validated in a real Wi-Fi network. Although the AP relocation methodology considered in this paper focuses on a Wi-Fi network, it could be adapted as well to other contexts, for example exploiting the monitoring and analytics framework for small cell networks defined by the 5G ESSENCE project in [START_REF] Perez-Romero | Monitoring and Analytics for the Optimisation of Cloud Enabled Small Cells[END_REF].

The remaining of the paper is organized as follows. Section 2 presents the different steps of the proposed self-planning methodology, while the details of the AP relocation algorithm are presented in Section 3. The performance of the proposed approach is empirically evaluated in Section 4, while Section 5 summarizes the conclusions.

Proposed Self-planning Methodology

The proposed self-planning methodology assumes a Wi-Fi network with monitoring capabilities enabling the collection of a number of measurements reported by the users when connected to the different APs. The collected data is processed by a data analytics module to generate actionable insights to be used by the self-planning process. As a result, the self-planning process will be able to detect those situations in which the network performance is not satisfactory thus requiring relocation of one or several APs to other positions. The network is represented as a group of K Access Points located at specific geographical locations (x AP,k ,y AP,k ). The complete scenario is represented with a set of pixels R, each one associated to a geographical location. According to Fig. 1, the Network Monitoring module collects and stores user measurements during a large period of time T meas (i.e. weeks or even months). Each measurement is associated to a particular location s (with s=1,…,S) with coordinates (x s ,y s ), i.e. one of the pixels of the scenario, so that S⊆R is the set of locations in the scenario with associated measurements. The location information can be obtained by means of different positioning methodologies which are out of the scope of this work, such as Time of Arrival [START_REF] Ciurana | Indoor tracking in WLAN location with TOA measurements[END_REF] or fingerprint-based approaches [START_REF] Wang | CSI-Based Fingerprinting for Indoor Localization: A Deep Learning Approach[END_REF]. With these inputs, the Data Analytics module generates the following metrics for each location:

1. List of APs where the user was connected to as best server at the s-th location.

2. Average Received Signal Strength Indicator (RSSI s ) from the best AP at the s-th location.

3. Average Signal to Noise Ratio (SNR s ) at the s-th location. 4. Normalized traffic volume (V s ) defined as the percentage of traffic transmitted/received at the s-th location with respect to the total amount of traffic volume transmitted/received in all the scenario in the period T meas . 5. The activity factor (T s ) defined as the amount of time in which a user at the s-th location is connected to the network with respect to the measurement period T meas .

These historical measurements are very valuable for the self-planning process in order to decide adequate actions of AP relocation. For example, AP relocations may decide to enhance the performance in certain regions with high traffic demands that experience poor SNR or bit rate by moving APs from regions with lower traffic demands (e.g. stairs and corridors of the building where users do not stay connected too much time and do not transmit large traffic volumes).

The Data Analytics module also identifies the regions with relatively high traffic demands by selecting the set U⊆S where the normalized traffic volume V s and activity factor T s are higher than some specific thresholds Th_v and Th_t, respectively. Then, with the set of samples U, a filtering process is carried out to determine the set of samples W⊆U in which the provided coverage and quality are below some specific thresholds Th_RSSI and Th_SNR, respectively. Therefore, the set W represents the most relevant locations of the network where users transmit a high amount of traffic and the network coverage/quality requirements are not guaranteed. The set W is used to trigger the AP relocation process. Specifically, if the set W is not empty (i.e. there are some relevant locations with poor coverage/quality), the AP relocation algorithm described in Section 3 is triggered. Otherwise, if the set W is empty, (i.e. there are no samples with poor coverage/quality) no AP relocation is done and a new period of time T meas starts.

An adequate setting of thresholds Th_v and Th_t is prime important in order to adequately determine the relevant sample locations considered for optimization. A too low value for these thresholds Th_v and Th_t may cause the appearance of false alarms (i.e. wrong activations of the AP relocation algorithm) since the existence of non-relevant locations (i.e. low traffic volume and low activity) with bad RSSI and SNR will unnecessarily activate the AP Relocation algorithm. On the contrary, a too high value in these thresholds Th_v and Th_t may cause that the AP Relocation algorithm may not be activated when needed. Note also that, in case of activation of the AP Relocation algorithm, high values of Th_v and Th_t may cause the methodology to miss relevant sample locations (with relatively high traffic volume and activity), which will lead to a sub-optimal AP relocation. As for the thresholds Th_RSSI and Th_SNR, they must be selected in accordance with the RSSI and SNR values that ensure a certain user performance (e.g. a minimum required user bit rate).

By observing the set of measurements W, the AP Relocation algorithm will determine a group of M Access Points (M≤K) considered as candidates to be relocated. These M APs are selected as the best serving APs for the samples of the set W. Then, the objective of the AP Relocation algorithm is to move one or several of these APs closer to the locations with high traffic demands (i.e. set of samples U) but, at the same time, maintaining enough coverage at all the R pixels of the scenario. Since the APs are usually connected to the wired network and hanged on the walls, the selection of potential AP locations depends on how easily they can be placed. In this respect, an adequate filtering of unfeasible locations will determine the subset R C ⊆R that includes only the feasible locations where the APs can be placed. This filtering will reduce the computational complexity of the solution search, which consists in running a metaheuristic algorithm that iteratively proposes and evaluates different candidate solutions until a termination condition is fulfilled. Different metaheuristic algorithms can be considered (e.g. genetic algorithm [START_REF] Aarts | Simulated Annealing and Boltzmann Machines[END_REF], simulated annealing [START_REF]Handbook of genetic algorithms[END_REF], etc.). This paper makes use of a genetic algorithm approach which is described in Section 3. If the AP Relocation algorithm provides a feasible solution, then, the corresponding AP(s) are relocated to the identified position(s).

AP Relocation Algorithm

The AP Relocation Algorithm makes use of a genetic algorithm optimization that consists on iteratively proposing possible candidate solutions (individuals), each one consisting in a combination of AP locations, and evaluate the performance of each individual according to a cost function. The best individuals in an iteration (generation) are combined to obtain new individuals that are again evaluated in the subsequent iteration. This process is repeated during multiple generations G. Each individual is represented by a combination of the geographical coordinates of all the K APs in the network. During the execution of the genetic algorithm, only the M APs (M≤K) that are the best serving APs for the samples of the set W are candidates to be relocated, while the rest of APs will remain at the same position. To following steps summarize the operation of the genetic algorithm for relocating the M APs: 1) Generate a population of N individuals: Each n-th individual (n=1,…,N) is represented by a vector vn with the geographical coordinates of the K APs vn={x AP,1 , y AP,1 ,…, x AP,k , y AP,k ,…, x AP,K , y AP,K }. Each element of the vector vn is called a gene.

For each individual, the coordinates of the M candidate APs to be relocated are determined randomly with uniform distribution among the subset of feasible locations R C , while the coordinates of the rest (K-M) APs are fixed at their current locations in the real network.

2) Execute the following sub-steps for each n-th individual:

2.1-Determine the path loss L u between each sample uU and its best AP, i.e. the AP with the lowest path loss between the position of sample u and the positions of the APs in the n-th individual according to a predefined propagation model.

2.2-

The cost of the n-th individual C n is calculated as the weighted average of the propagation loss L u from each u-th sample to its best AP as follows:

𝐶 ∑ 𝑤 𝐿 (1)
where the weights w u are defined to give more relevance to the regions with high traffic volume and low SNR. For this reason, the weights w u are defined as:

𝑤 ∑ (2)
where:

𝛼 ∑ (3) 𝛽 ⁄ ∑ ⁄ (4)
V u denotes the normalized traffic volume demanded at the u-th sample and SNR u is the Signal to Noise Ratio of sample u-th. α u and β u are normalized parameters so that 0≤w u ≤1 as shown in ( 3) and ( 4).

According to equation ( 1), an individual with a lower cost C n corresponds to a better network layout since it relocates the different APs so that lower values of path loss are observed, especially for samples with high traffic volume or low SNR.

2.3-Two feasibility conditions are checked. The first condition checks whether the individual guarantees the overall network coverage or not. Then, an individual is not feasible if there is any geographical location in the scenario (i.e. a pixel of the set R) with RSSI lower than RSSI cov . The second feasibility condition checks that the proposed individual guarantees that the interference generated from an AP to a neighbor AP that is using the same channel is lower than a threshold Th_RSSI neigh . 3) Selection: This step determines which feasible individuals are selected for the generation of new individuals to be evaluated in the subsequent generation. The cost of each individual C n is used for the selection process by considering a roulette wheel [START_REF]Handbook of genetic algorithms[END_REF] so that the probability of selecting the n-th individual for recombination is:

𝑃𝑟𝑜𝑏 ∑ (5)
According to this, individuals with lower cost have higher probability to be selected for recombination. Individuals that are not feasible according to the feasibility conditions in step 2.3, are not considered in this selection process. 4) Recombination: This process combines the different genes (i.e. elements of the vectors vn) of the two individuals selected in the previous step (called parents) to generate two new individuals (called children). The rationality of this process is to search for new solutions similar to the best individuals of the previous generation by combining their genes. The recombination process considered here is the so-called "1-point crossover" [START_REF]Handbook of genetic algorithms[END_REF]. Considering the genes of the two parents, a crossover point is defined randomly and all the genes beyond this crossover point are swapped between both parents to obtain the children. 5) Mutation: This operator makes small random changes in the genes of the two individuals obtained after recombination. The probability of selecting a gene for mutation is 1/N genes where N genes =2M corresponds to the two coordinates of all the M AP that can be relocated in the individual. As a result, very few genes of an individual are usually modified by this process. When a gene is selected for mutation, the new value, which represents a new AP location, is either an increase or decrease (with equal probability) in one pixel, allowing only changes to locations of the set R C . As a result of the selection, recombination and mutation process, a total of two new individuals will be obtained. This process is repeated N/2 times until getting the N individuals for the new generation. With the newly generated N individuals, the algorithm executes again the evaluation procedure and computes the associated costs. The process is repeated iteratively until reaching a maximum number of generations G. Then, the final solution of the algorithm is the individual with minimum cost that has been found throughout all the generations.

Performance Evaluation

The proposed methodology has been evaluated empirically in a scenario consisting of a building of 40m x 20m covered by K=3 APs represented in Fig. 2. A grid with pixel resolution of 1m is considered. The measurements are made and reported by 241 different users during a period T meas of one month. These reported measurements are collected by the Cisco Prime Infrastructure tool [START_REF][END_REF]. The locations of the set of U=12 samples in which the demand of traffic is relatively high (i.e. Normalized Traffic Volume higher than Th_v=1% and Activity Factor higher than Th_t=1%) are represented in Fig. 2. Table 1 presents the average SNR u , the normalized traffic volume V u , the normalized parameters α u and β u and the weight w u for the identified U=12 samples. By filtering the U samples, the set W contains one sample with RSSI and SNR below Th_RSSI=-81dBm and Th_SNR=15dB, respectively. This sample corresponds to u 12 located at coordinates [39,2] as shown in Fig. 2. Although it is just a single sample, it represents a substantial amount of traffic (i.e. 4.83% of normalized traffic volume) and exhibits a very low SNR compared to the rest of samples. At this location, an average bit rate of 14 Mb/s has been observed while the bit rate at other locations inside the building usually varies between 30 and 50 Mb/s. At this location, the users can connect to two different APs, namely AP1 and AP3. Then, the AP relocation algorithm will evaluate the relocation of these M=2 APs to improve the per- formance at the U=12 locations, while maintaining the network coverage (i.e. RSSI of all the R pixels in the scenario is higher than RSSI cov =-75dBm) and keeping the interference between neighbor APs using the same channel below RSSI neigh =-70dBm.

The genetic algorithm considers a population of N=50 individuals, each one represented by the location coordinates of the K=3 APs. A total of G=5000 generations have been considered. The propagation model is taken from the ITU-R [START_REF]Propagation data and prediction methods for the planning of indoor radio communication systems and the radio local area networks[END_REF] so that the path loss between the transmitter and the receiver is calculated as:

𝐿 𝑑𝐵 20𝑙𝑜𝑔 𝑓 𝑁 𝑙𝑜𝑔 𝑑 𝑃 𝑛 28 𝑁 𝐿 (6)
where f=2.4GHz is the carrier frequency, N o =30 for office areas, P f (n) is the floor attenuation factor considered here 0 dB since all the APs and the users are located at the same floor, N d is the number of obstacles (i.e. walls and doors) between the transmitter and the receiver and L o =2dB is the path loss associated to each obstacle. After executing the AP relocation algorithm, the best solution found is illustrated in Fig. 3. It consists in moving AP3 to the location [32,2] while keeping AP1 in its current location. This solution has a cost of 75.58 which is considerably better than the cost of 93.6 that was obtained before the execution of the AP relocation algorithm. In order to empirically validate the solution proposed by the genetic AP relocation algorithm, several RSSI measurements have been done at 47 different locations of the building before and after the AP relocation (i.e. with AP3 located at coordinates [20,4] and [32,2], respectively). For each location, the RSSI averaged in a period of 2 minutes is obtained. Fig. 4 and Fig. 5 present the average RSSI measurements obtained before and after the AP relocation, respectively. After the AP3 relocation, an average RSSI=-67dBm has been measured at u 12 (i.e. coordinates [39,2]) which is considerably better than an average RSSI=-82dBm at the same location before the AP3 relocation). Moreover, the AP relocation guarantees an RSSI higher than the threshold RSSI cov =-75dBm for all the locations of the scenario. Some other validations have been done by moving AP3 to different locations and collecting the new values of the average RSSI measurements. For example, moving AP3 closer to the location of sample u 12 (e.g. to coordinates [37,2]) is not a feasible solution since it causes some coverage problems at region around [START_REF][END_REF][START_REF] Liu | On Big Data Analytics for Greener and Softer RAN[END_REF]. Similarly, relocating AP3 to coordinates [28,2] (i.e. not so close to u 12 ]) guarantees the coverage RSSI requirements at all the locations of the building but, an average RSSI=-73dBm is measured at u 12 , which is not as good as the average RSSI=-67dBm obtained when implementing the best solution proposed by the genetic AP relocation algorithm.

Conclusions

This paper has presented a new self-planning methodology to determine adequate relocations of APs in a Wi-Fi network based on information about signal quality and user traffic demands extracted from network measurements collected and processed by a monitoring and analytics system. From these measurements, the proposed methodology identifies regions with poor RSSI/SNR and high traffic demand, determines candidate APs to be relocated and applies a genetic algorithm optimization to recommend new locations for these APs. The paper has presented an empirical validation of the algorithm performance, making use of measurements collected from a real Wi-Fi network and comparing the obtained RSSI at different locations before and after the execution of the AP relocation.

Fig. 1 .

 1 Fig. 1. Self-optimisation methodology.

Fig. 2 .

 2 Fig. 2. Considered scenario with the set of U=12 samples.

Fig. 3 .

 3 Fig. 3. Solution proposed by the Genetic Algorithm.

Fig. 4 .

 4 Fig. 4. RSSI measurements obtained before the AP Relocation i.e. AP3 at [20,4].

Fig. 5 .

 5 Fig. 5. RSSI measurements obtained after the AP Relocation i.e. AP3 at [32,2].

Table 1 .

 1 -Observed metrics and obtained weight for each sample of the set U=12.

	Sample	Location	SNRu(dB)	Vu(%)	αu	βu	w u
	u 1	[14,7]	33.04	1.83	0.006	0.050	0.004
	u 2	[22,3]	30.12	1.75	0.012	0.048	0.007
	u 3	[7,4]	17.54	1.68	0.231	0.046	0.118
	u 4	[3,14]	27.24	8.27	0.024	0.227	0.062
	u 5	[1,2]	23.95	1.77	0.052	0.048	0.028
	u 6	[13,15]	29.32	3.56	0.015	0.098	0.017
	u 7	[13,5]	27.13	1.68	0.025	0.046	0.013
	u 8	[35,18]	23.82	1.8	0.054	0.049	0.030
	u 9	[39,18]	22.80	2.34	0.068	0.064	0.049
	u 10	[39,14]	22.08	1.78	0.081	0.049	0.044
	u 11	[37,18]	33.70	4.96	0.005	0.136	0.008
	u12	[39,2]	14.92	4.83	0.427	0.139	0.620

Acknowledgements. This work has been supported by the EU funded H2020 5G-PPP project 5G ESSENCE under grant agreement 761592 and by the Spanish Research Council and FEDER funds under SONAR 5G grant (ref. TEC2017-82651-R).