T - Mobile Investment Report

This investment report shows T-Mobile\u27s key drivers to invest based on the following elements: pricing information, business description, industry analysis, financial analysis, competitors\u27 performance, and business risks

5GEx: realising a Europe-wide multi-domain framework for software-defined infrastructures

Market fragmentation has resulted in a multitude of network and cloud/data center operators, each focused on different countries, regions and technologies. This makes it difficult and costly to create infrastructure services spanning multiple domains, such as virtual connectivity or compute resources. In this article, we discuss the goals and work being done within the 5GEx (5G Exchange) project in realising a Europe-wide multi-domain platform. This platform aims at enabling cross-domain orchestration of services over multiple administrations or over multi-domain single administrations in the context of emerging 5G networking. The 5GEx vision is based on introducing a unification via network function virtualisation/software-defined networking compatible multi-domain orchestration for networks, clouds and services. We describe the motivation and 5GEx vision, the adopted architecture and the next steps in terms of implementation and experimentation.This work is performed in the framework of the H2020-ICT-2014 project 5GEx (Grant Agreement no. 671636), which is partially funded by the European Commission

The European framework for regulating telecommunications - a 25-year appraisal

The European telecommunications sector has been radically transformed in the past 25 years: from a group of state monopolies to a set of increasingly competitive markets. In this paper we summarize how this process has unfolded -- for both fixed and mobile telecommunications -- by focusing on the evolution of the regulatory framework and by drawing some parallels with the evolution of the sector in the US. Given the major strategic importance of the sector, we highlight some of the challenges that lie ahead

Equity research - BT Group PLC

Mestrado Bolonha em FinançasEste relatório apresenta a avaliação do BT Group PLC como Projeto de Trabalho Final de Mestrado em Finanças no ISEG. Segue o formato de Equity Research recomendado pelo CFA Institute. O autor escolheu o BT Group PLC devido à sua posição como um dos principais players do setor de telecomunicações, que atua em vários países do mundo. O autor tinha interesse em estudar a fundo a indústria. O estudo considera informações publicamente disponíveis sobre a empresa em 31 de setembro de 2022, como relatórios da empresa, Thomson Reuters, Bloomberg L.P. e yahoofinance.com. O preço alvo (TP) foi obtido com base no método de Fluxos de Caixa Descontados, complementado com o Valor Presente Ajustado, o Modelo de Dividendos Descontados e a Avaliação" Relativa. A recomendação final é COMPRAR, com um preço-alvo de £ 1,58/ação 2023YE e um potencial de valorização de 31.3% em 29 de novembro de 2022 com risco médio avaliado. O crescimento esperado da receita e o aumento da lucratividade suportam essa recomendação durante o período de previsão, especialmente no setor de consumo. A evolução da Covid-19 e a pressão inflacionária em todo o mundo são riscos vitais que impactam o preço-alvo, que atua a empresa em nível operacional juntamente com a incerteza das perspectivas macroeconômicas globais, influenciando a receita média por usuário (ARPU).This report presents the valuation of BT Group PLC as a Master’s in Finance Final Work Project at ISEG. It follows the equity research format recommended by the CFA Institute. BT Group PLC was selected due to its position as one of the major players in the telecom communications industry, which operates in many countries worldwide. The author had an interest in studying the industry in-depth. The study considers publicly available information concerning the company as of 31st September 2022, such as company reports, Thomson Reuters, Bloomberg L.P., and yahoofinance.com. The target price (TP) is based on the Discounted Cash Flow method, complemented with the Adjusted Present Value, the Discounted Dividend Model, and the Relative Valuation. The final recommendation is BUY, with a target price of £ 1.58/share 2023YE and a 31.3% upside potential as of 29th November 2022 with medium risk assessed. The expected revenue growth and increased profitability support this recommendation during the forecast period, especially in the consumer sector. The evolution of Covid-19 and inflationary pressure across the world are vital risks impacting the target price, which acts the company on an operational level along with the uncertainty of the global macroeconomic outlook by influencing the average revenue per user (ARPU).info:eu-repo/semantics/publishedVersio

Demand Response Strategy Based on Reinforcement Learning and Fuzzy Reasoning for Home Energy Management

As energy demand continues to increase, demand response (DR) programs in the electricity distribution grid are gaining momentum and their adoption is set to grow gradually over the years ahead. Demand response schemes seek to incentivise consumers to use green energy and reduce their electricity usage during peak periods which helps support grid balancing of supply-demand and generate revenue by selling surplus of energy back to the grid. This paper proposes an effective energy management system for residential demand response using Reinforcement Learning (RL) and Fuzzy Reasoning (FR). RL is considered as a model-free control strategy which learns from the interaction with its environment by performing actions and evaluating the results. The proposed algorithm considers human preference by directly integrating user feedback into its control logic using fuzzy reasoning as reward functions. Q-learning, a RL strategy based on a reward mechanism, is used to make optimal decisions to schedule the operation of smart home appliances by shifting controllable appliances from peak periods, when electricity prices are high, to off-peak hours, when electricity prices are lower without affecting the customer’s preferences. The proposed approach works with a single agent to control 14 household appliances and uses a reduced number of state-action pairs and fuzzy logic for rewards functions to evaluate an action taken for a certain state. The simulation results show that the proposed appliances scheduling approach can smooth the power consumption profile and minimise the electricity cost while considering user’s preferences, user’s feedbacks on each action taken and his/her preference settings. A user-interface is developed in MATLAB/Simulink for the Home Energy Management System (HEMS) to demonstrate the proposed DR scheme. The simulation tool includes features such as smart appliances, electricity pricing signals, smart meters, solar photovoltaic generation, battery energy storage, electric vehicle and grid supply.Peer reviewe