5G Dimensioning And Optimization Through Use Analysis Of A Real Scenario

Mobile networks have become essential to our daily communications. The growth of mobile traffic and users has increased exponentially in recent years, with increasing demands on throughput and latency. To handle this growing traffic, a scaling strategy that guarantees quality of service over time is essential. This thesis proposes the dimensioning of a mobile network based on a real 4G scenario, using techniques such as the implementation of new carriers and 5G technology. It also proposes the dynamic implementation of Cloud RAN, assigning the location of BBU pools according to network characteristics

Sparse Signal Processing Concepts for Efficient 5G System Design

As it becomes increasingly apparent that 4G will not be able to meet the emerging demands of future mobile communication systems, the question what could make up a 5G system, what are the crucial challenges and what are the key drivers is part of intensive, ongoing discussions. Partly due to the advent of compressive sensing, methods that can optimally exploit sparsity in signals have received tremendous attention in recent years. In this paper we will describe a variety of scenarios in which signal sparsity arises naturally in 5G wireless systems. Signal sparsity and the associated rich collection of tools and algorithms will thus be a viable source for innovation in 5G wireless system design. We will discribe applications of this sparse signal processing paradigm in MIMO random access, cloud radio access networks, compressive channel-source network coding, and embedded security. We will also emphasize important open problem that may arise in 5G system design, for which sparsity will potentially play a key role in their solution.Comment: 18 pages, 5 figures, accepted for publication in IEEE Acces

Unsupervised clustering for 5G network planning assisted by real data

The fifth-generation (5G) of networks is being deployed to provide a wide range of new services and to manage the accelerated traffic load of the existing networks. In the present-day networks, data has become more noteworthy than ever to infer about the traffic load and existing network infrastructure to minimize the cost of new 5G deployments. Identifying the region of highest traffic density in megabyte (MB) per km2 has an important implication in minimizing the cost per bit for the mobile network operators (MNOs). In this study, we propose a base station (BS) clustering framework based on unsupervised learning to identify the target area known as the highest traffic cluster (HTC) for 5G deployments. We propose a novel approach assisted by real data to determine the appropriate number of clusters k and to identify the HTC. The algorithm, named as NetClustering, determines the HTC and appropriate value of k by fulfilling MNO's requirements on the highest traffic density MB/km2 and the target deployment area in km2. To compare the appropriate value of k and other performance parameters, we use the Elbow heuristic as a benchmark. The simulation results show that the proposed algorithm fulfills the MNO's requirements on the target deployment area in km2 and highest traffic density MB/km2 with significant cost savings and achieves higher network utilization compared to the Elbow heuristic. In brief, the proposed algorithm provides a more meaningful interpretation of the underlying data in the context of clustering performed for network planningThis work was supported by the Spanish National Project IRENE-EARTH (PID2020-115323RB-C33/AEI/10.13039/501100011033

Joint-rollout of FTTH and smart city fiber networks as a way to reduce rollout cost

Making cities smarter is the future. By bringing more technology into existing city infrastructure, smart city applications can arise. Whether these applications track devices e.g. public lightning, environmental measurements e.g. temperature or air quality, or analyze video streams e.g. for people density, it is expected that these will require a (near-) real time data connection. Upcoming 5G networks will be able to handle large amounts of connections at high speeds and low latencies and will therefor outperform current technologies such as 4G and low-power wide-area networks. In order to do so, these 5G networks fall back to numerous fiber connected small cells for up & downlink to the Internet. In this publication, we are looking into the additional fiber equipment and deployment cost to connect the required smart city network infrastructure, taking into account a Fiber-to-the-Home (FTTH) network is already available or will be installed as part of the smart city network rollout. More concretely, we are proposing a methodology comparing an anticipated and incremental planning approach for a number of different extensions upon the FTTH-network: connecting all electrical cabinets, connecting public lightning, and the connection of 5G using small cells. From this, we want to learn how much the total rollout cost can be reduced using a future-oriented smart city approach taking into account all future extensions, compared to an incremental short-time planning only planning additional fiber when required. In the meantime, we want to show the additional cost of creating a smart city network is limited when it is being combined with a FTTH rollout. Results of the proposed methodology and use case will be modeled planning and design software Comsof Fiber and will be published in a future work

Service-based network dimensioning for 5G networks assisted by real data

The fifth-generation (5G) of cellular communications is expected to be deployed in the next years to support a wide range of services with different demands of peak data rates, latency and quality of experience (QoE). In this work, we propose a novel approach for radio network dimensioning (RND), named as Heuristic RND (HRND), which uses real open data in the network dimensioning process. This procedure, named as NetDataDrilling, provides the dimensioning target area by means of network data selection and visualization from the existing infrastructure. Moreover, the proposed NetDimensioning heuristic considers the necessary parameters of numerology and bandwidth parts (BWP) supported by New Radio (NR) to provide a balanced network design mediating among the requirements of coverage, capacity, QoE and cost. The proposed HRND is based on the novel quality of experience (QoE) parameter ζ by probabilistically characterizing the 5G radio resource control (rrc) states to ensure the availability of peak data rates for the MNO's required percentage of the time. The simulation results show the fulfilment of QoE and load balancing parameters with significant cost savings compared to the conventional RND methodology.This work was supported by the Spanish National Project TERESA-ADA (MINECO/AEI/R, UE), under Grant TEC2017-90093-C3-2-R

Pilvipohjaisen radioliityntäverkon kustannusten mallintaminen

The rapid growth of mobile data traffic is challenging the current way of building and operating the current radio access network. Cloud-based radio access network is researched as a solution to provide the required capacity for rapidly growing traffic demand in more economical manner. Scope of this thesis is to evaluate the costs of different existing and future radio access network architectures depending on the given network and traffic scenario. This is done by creating a cost model based on expert interviews to solve the most economical solution for the given network in terms of total cost of ownership. The results show that the cloud-based radio access network’s cost benefits are dependent on the expected traffic growth. In the low traffic growth scenario, the cost benefits of cloud-based radio access network are questionable, but in the high traffic growth scenario clear cost benefits are achieved.Mobiilidataliikenteen nopea kasvu haastaa nykyisen tavan rakentaa ja hallinnoida tämän hetkisiä radioliityntäverkkoja. Pilvipohjaista radioliityntäverkkoa tutkitaan ratkaisuksi tarjota tarvittavaa verkkokapasiteettia entistä taloudellisemmin. Tämän opinnäytetyön tarkoituksena on arvioida nykyisten ja pilvipohjaisten radioliityntäverkkoarkkitehtuurien kustannuksia riippuen verkon rakenteesta ja liikenteestä. Tämä toteutetaan luomalla kustannusmalli, joka perustuu asiantuntijoiden haastatteluihin. Mallin avulla on mahdollista vertailla annetun verkon eri arkkitehtuurien kokonaiskustannuksia ja selvittää taloudellisin radioliityntäverkkoarkkitehtuuri verkolle. Mallinnuksen tulokset osoittavat, että pilvipohjaisen radioliityntäverkon taloudelliset hyödyt ovat riippuvaisia dataliikenteen kasvusta verkossa. Vähäisellä data-liikenteen kasvulla pilvipohjaisen radioliityntäverkon kustannusedut ovat kyseenalaiset, mutta suurella dataliikenteen kasvulla saadaan selviä säästöjä verrattuna nykyisiin arkkitehtuureihin

Techno-economic viability of integrating satellite communication in 4G networks to bridge the broadband digital divide

Bridging the broadband digital divide between urban and rural areas in Europe is one of the main targets of the Digital Agenda for Europe. Though many technological options are proposed in literature, satellite communication has been identified as the only possible solution for the most rural areas, due to its global coverage. However, deploying an end-to-end satellite solution might, in some cases, not be cost-effective. The aim of this study is to give insights into the economic effectiveness of integrating satellite communications into 4G networks in order to connect the most rural areas (also referred to as white areas) in Europe. To this end, this paper proposes a converged solution that combines satellite communication as a backhaul network with 4G as a fronthaul network to bring enhanced broadband connectivity to European rural areas, along with a techno-economic model to analyse the economic viability of this integration. The model is based on a Total Cost of Ownership (TCO) model for 5 years, taking into account both capital and operational expenditures, and aims to calculate the TCO as well as the Average Cost Per User (ACPU) for the studied scenarios. We evaluate the suggested model by simulating a hypothetical use case for two scenarios. The first scenario is based on a radio access network connecting to the 4G core network via a satellite link. Results for this scenario show high operational costs. In order to reduce these costs, we propose a second scenario, consisting of caching the popular content on the edge to reduce the traffic carried over the satellite link. This scenario demonstrates a significant operational cost decrease (more than 60%), which also means a significant ACPU decrease. We evaluate the robustness of the results by simulating for a range of population densities, hereby also providing an indication of the economic viability of our proposed solution across a wider range of areas