01990nas a2200193   4500008004100000245008500041210006900126300001200195490000700207520128800214100002101502700002401523700002201547700002201569700001801591700002301609700002301632856014101655       2022                        eng d00aAn Introduction of Upcoming Radio Resource Management Techniques for 5G Networks0 aIntroduction of Upcoming Radio Resource Management Techniques fo  a147-1880 v463 a5G networks are the next generation of mobile internet connectivity, that are able to offer vastly increased speeds, more reliable connections, minimal latency and more supported devices. 5G networks are expected to supercharge Internet of Things (IoT) technology, so as to provide the infrastructure needed in order to support and transfer large data amounts that will enable a smarter and more connected world. To this direction, 5G incorporates many technologies and mechanisms that aid towards the overall goal, such as Multiple-Input and Multiple-Output (MIMO), Downlink (DL) and Uplink (UL) Decoupling (DUDe) and Machine Learning (ML). These technologies can significantly help towards more efficient resource allocation inside the next generation networks, offering increased spectral efficiency and data rates, better signal coverage, reduced latencies and many more. In this chapter, we will provide insights over the aforementioned technologies through firstly a literature review and later on by analyzing their architecture and their models. We will explain how these technologies can be taken advantage of in order to support the 5G networks and why they are core components of future networks, as it is expected that also 5G and Beyond networks will capitalize on them.1 aBouras, Christos1 aKokkinos, Vasileios1 aGkamas, Apostolos1 aMichos, Evangelos1 aSina, Ioannis1 aProkopiou, Ioannis1 aAllayiotis, Foivos  uhttps://telematics.upatras.gr/telematics/publications/introduction-upcoming-radio-resource-management-techniques-5g-networks?language=el01383nas a2200121   4500008004100000245005200041210005200093520093500145100002101080700002701101700001801128856011501146       2021                        eng d00aClustering Based User Allocation in 5G Networks0 aClustering Based User Allocation in 5G Networks3 aMachine Learning is an extremely efficient technique for solving complex problems without the use of traditional programming but rather enabling machines to learn from an input of data and train them to cope with various problems. The rapid growth in the number of active mobile devices, mobile applications and services dictate an efficient utilization of mobile and wireless networking infrastructure. Communication networks need to evolve and valorize Machine Learning methods in order to process large volumes of data without introducing excessive time delay in these computations. Upcoming 5G systems are expected to be the first network infrastructure to support exploding mobile traffic volumes and Machine Learning techniques can be used in order to help manage the rise in data volumes. We present a mechanism for resource allocation in mobile and wireless networks, that effectively utilizes Machine Learning techniques.1 aBouras, Christos1 aKalogeropoulos, Rafail1 aSina, Ioannis  uhttps://telematics.upatras.gr/telematics/publications/clustering-based-user-allocation-5g-networks?language=el