To help telecommunication operators in their network planning, namely coverage estimation and optimisation tasks, this paper presents a comparison between a semi-empirical propagation model and a propagation model generated using Artificial Intelligence (AI). These two types of propagation models are quite different in their design. The semi-empiric Automatically Calibrated Standard Propagation Model (ACSPM) is specific for an operating antenna, being calibrated every time a use case application is used and the Artificial Intelligence Propagation Model (AIPM) can be applied in different scenarios, once trained, allowing to estimate coverage for a new antenna location, using information from neighboring antennas. These models have quite different features and applicability. The ACSPM should be applied in network optimisation, when using data from the current state of the antennas. The AIPM can be used in the deployment of new antennas, as it uses data from a certain geographical area. For a better comparison of the models studied, extensive Drive Tests (DT) collection campaigns conducted by operators are used, since coverage estimations are more realistic when DTs are considered. Both models are generated using very different methodologies, but their resulting performance is very similar. The AIPM achieves a Mean Absolute Error (MAE) up to 6:1 dB with a standard deviation of 4 dB. When compared to the ACSPM we have an improvement of 0:5 dB, since this only achieves a MAE up to 6:6 dB. AIPM achieves better results and is the characterised for being completely agnostic and definition-free, when compared with known propagation models.

In this work, we propose a methodology to improve the precision of cell traffic forecasting with a machine learning approach. To develop this methodology, we first performed a systematic analysis in order to reduce bias by selecting the cells with less missing data occurrences. Then, we selected the features and trained a classifier to allocate the cells between predictable and non- predictable, taking into account previous traffic forecast error. The Naive Bayes classifier and Holt-Winters method was selected to perform the proposed methodology in real time. The system was applied to a set of 786 cells in a real network. The classifier presented a 91% accuracy, which leads the predictable cells, using Holt-Winters, to present an average RMSE of 2.74%. This means that it is now possible to implement optimisation algorithms that are highly sensitive to traffic prediction.

The virtualisation of Radio Access Networks (RANs) is one of the goals in designing 5G mobile networks. This paper aims at presenting a proof of concept for the virtualisation of radio resources using Open Air Interface (OAI), a software-based Long-Term Evolution (LTE) eNodeB physical emulator. OAI was extended to support multi-tenancy, representing diverse Virtual mobile Network Operators (VNOs) with different Service Level Agreements (SLAs). A comprehensive analytical model for managing the virtual radio resources has been proposed, with two key parts: estimation of available radio resources and their allocation to different VNOs. The estimation is performed by the model, and the allocation is managed by OAI scheduling. Various scenarios and use cases are studied in this virtual RAN environment, network performance being evaluated for different situations, by varying guaranteed levels, serving weights, and used services. Results show that the proposed approach offers almost the same capacity to guaranteed VNOs regardless of other existing VNOs, experiencing at worst a degradation of 32% of its initial allocated data rate, without violation of the guaranteed data rate. The data rate allocated to best effort VNOs may decrease up to 7% of its initial value, which is acceptable, to guarantee other more demanding SLAs.

A study of the extra signal attenuation due to building penetration associated to path loss from the base stations to mobile terminals, for different types of buildings and rooms is presented for GSM (900 and 1800 MHz) and UMTS. In this study, a statistical model for the attenuation penetration is developed, following the log-normal distribution, applied to a building classification, and supported on measurements of the actual systems under consideration, which can be used for radio network planning purposes. Different types of buildings (High-Integrated, High-Isolated, Low-Integrated, and Low-Isolated) and rooms (Indoor Light, Indoor, Deep Indoor) are considered. A study on the variation of the attenuation per floor, room and building type is performed. An average attenuation of 5.7 dB for GSM900 is observed, with a standard deviation of 11.1 dB.

In the traditional model for wireless mesh networks, a two-tiered architecture classifies nodes into mesh routers or clients. Such an approach, based on strong separation of roles, is interesting when an administrative entity deploys and controls the network. Nevertheless, in spontaneous and self-organized networks, where there is no administrative entity behind the network formation, this model does not hold anymore. In such a scenario the heterogeneity of nodes should be fully exploited in order to increase, as much as possible, network availability and usability. In this article we propose an approach for opportunistic wireless mesh network formation and maintenance that breaks the rigidness of the traditional architecture. We relax the role separation constraint and consider the case of spontaneous network formation relying on the concept of self-organization and collaboration. The main idea is to make the network take advantage of the specific resources and characteristics of the nodes in an opportunistic fashion. In our vision, any node (routers and clients) can perform any network functionality, if they can and if they wish.

In radio access network planning and optimisation, the estimation and evaluation of cell coverage is crucial. For it, operators use several methodologies, ranging from theoretical propagation models for planning cell coverage, to drive test measurements for evaluating the performance within a cell. A novel propagation model is proposed which combines drive test measurements, cell reach statistics, antenna radiation patterns, terrain morphologies, and classical theoretical propagation models. Considering a realistic scenario of an LTE cell, the use of a classical propagation model such as Walfish- Ikegami results in an average error of -27 dB of estimations with respect to measurements, evidencing a pessimistic estimation of signal. By opposition, the proposed model fits with DT measurements.