Ilman analyyttista ajattelua syntyy kaaos

Perinteinen tutkimus perustuu 1600-luvulla luotuun kokeellisuuden ja analyyttisen ajattelun yhdistämiseen, jossa ongelma pelkistetään tai hajotetaan osiinsa ja tulokset yleistetään induktiivisesti alhaalta ylöspäin. Siten tutkijan ja myös opiskelijan pitää uutta oppiakseen rakentaa kokonaisuus uudestaan itse. Nyt tämä erikoistumisena ja oppiainelähtöisyytenä näkyvä pelkistäminen kyseenalaistetaan laajasti opetuksessa ja joskus myös tutkimuksessa. Tilalle tarjotaan kokonaisvaltaisia lähestymistapoja, kuten työelämälähtöisyys, joka opiskelijan näkökulmasta vaikuttaa kaoottiselta. Ylhäältä alaspäin etenevää systeemiajattelua tarvitaan myös, sillä se vahvistaa oppimista ja estää liiallista lokeroitumista, jota kutsutaan myös fakkiutumiseksi

Machine Learning Threatens 5G Security

Machine learning (ML) is expected to solve many challenges in the fifth generation (5G) of mobile networks. However, ML will also open the network to several serious cybersecurity vulnerabilities. Most of the learning in ML happens through data gathered from the environment. Un-scrutinized data will have serious consequences on machines absorbing the data to produce actionable intelligence for the network. Scrutinizing the data, on the other hand, opens privacy challenges. Unfortunately, most of the ML systems are borrowed from other disciplines that provide excellent results in small closed environments. The resulting deployment of such ML systems in 5G can inadvertently open the network to serious security challenges such as unfair use of resources, denial of service, as well as leakage of private and confidential information. Therefore, in this article we dig into the weaknesses of the most prominent ML systems that are currently vigorously researched for deployment in 5G. We further classify and survey solutions for avoiding such pitfalls of ML in 5G systems

Database-assisted spectrum sharing in satellite communications:A survey

This survey paper discusses the feasibility of sharing the spectrum between satellite telecommunication networks and terrestrial and other satellite networks on the basis of a comprehensive study carried out as part of the European Space Agency's (ESA) Advanced Research in Telecommunications Systems (ARTES) programme. The main area of investigation is the use of spectrum databases to enable a controlled sharing environment. Future satellite systems can largely benefit from the ability to access spectrum bands other than the dedicated licensed spectrum band. Potential spectrum sharing scenarios are classified as: a) secondary use of the satellite spectrum by terrestrial systems, b) satellite system as a secondary user of spectrum, c) extension of a terrestrial network by using the satellite network, and d) two satellite systems sharing the same spectrum. We define practical use cases for each scenario and identify suitable techniques. The proposed scenarios and use cases cover several frequency bands and satellite orbits. Out of all the scenarios reviewed, owing to the announcement of many different mega-constellation satellite networks, we focus on analysing the feasibility of spectrum sharing between geostationary orbit (GSO) and non-geostationary orbit (NGSO) satellite systems. The performance is primarily analysed on the basis of widely accepted recommendations of the Radiocommunications Sector of the International Telecommunications Union (ITU-R). Finally, future research directions are identified