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

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

Feasibility study: investigation of car park-based V2G services in the UK central hub

The increasing uptake of electric vehicles, and the established practice of long-term parking at stations and airports, offers an opportunity to develop a flexible approach to help with the energy storage dilemma. This paper investigates the feasibility of using a number of EV batteries as an energy storage and grid balancing solution within the UK Central Hub area. Here, the capital cost of the vehicle is a sunk cost to the EV owner. The potential income generated, or discount on long-term parking, is an additional benefit of ownership. This paper considers the income available to a small and large size car park from the different market mechanisms to offer grid support in the UK and contrasts this with the complexity and costs of the EV charging infrastructure required within these types of scheme

An Analysis of the Network Selection Problem for Heterogeneous Environments with User-Operator Joint Satisfaction and Multi-RAT Transmission

The trend in wireless networks is that several wireless radio access technologies (RATs) coexist in the same area, forming heterogeneous networks in which the users may connect to any of the available RATs. The problem of associating a user to the most suitable RAT, known as network selection problem (NSP), is of capital importance for the satisfaction of the users in these emerging environments. However, also the satisfaction of the operator is important in this scenario. In this work, we propose that a connection may be served by more than one RAT by using multi-RAT terminals. We formulate the NSP with multiple RAT association based on utility functions that take into consideration both user’s satisfaction and provider’s satisfaction. As users are characterized according to their expected quality of service, our results exhaustively analyze the influence of the user’s profile, along with the network topology and the type of applications served