01572nas a2200145   4500008004100000245010200041210006900143300001200212490000600224520097800230100002101208700002201229700002301251856015201274       2022                        eng d00aApplying Machine Learning and Dynamic Resource Allocation Techniques in Fifth Generation Networks0 aApplying Machine Learning and Dynamic Resource Allocation Techni  a662-6730 v13 aAccording to Internet of Things (IoT) Analytics, soon, the online devices in IoT networks will range from 25 up to 50 billion. Thus, it is expected that IoT will require more effective and efficient analysis methods than ever before with the use of Machine Learning (ML) powered by Fifth Generation (5G) networks. In this paper, we incorporate the K-means algorithm inside a 5G network infrastructure to better associate devices with Base Stations (BSs). We use multiple datasets consisting of user distribution in our area of focus and propose a Dynamic Resource Allocation (DRA) technique to learn their movement and predict the optimal position, RB usage and optimize their resource allocation. Users can experience significantly higher data rates and extended coverage with minimized interference and in fact, the DRA mechanism can mitigate the need for small cell infrastructure and prove a cost-effective solution, due to the resources transferred within the network.1 aBouras, Christos1 aMichos, Evangelos1 aProkopiou, Ioannis  uhttp://telematics.upatras.gr/telematics/publications/applying-machine-learning-and-dynamic-resource-allocation-techniques-fifth-generation-networks01977nas a2200193   4500008004100000245008500041210006900126300001200195490000700207520128800214100002101502700002401523700002201547700002201569700001801591700002301609700002301632856012801655       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  uhttp://telematics.upatras.gr/telematics/publications/introduction-upcoming-radio-resource-management-techniques-5g-networks01572nas a2200133   4500008004100000245007100041210006900112520104900181100002101230700002301251700002201274700002401296856011801320       2022                        eng d00aPerformance Analysis of MIMO using Machine Learning in 5G Networks0 aPerformance Analysis of MIMO using Machine Learning in 5G Networ3 aMassive Multiple-Input Multiple-output (MIMO) is an important radio antenna technology for mobile wireless networks, such as 5th Generation (5G) with high potential. The use of hybrid analog and digital precoding to minimize the energy consumption as well as the hardware complexity of mixed signal components is an essential strategy. Machine Learning (ML) could be able to boost 5G technologies due to the rising difficulty of configuring cellular networks. More than ever, a ML computational framework focused on successfully processing the expected huge data generated normally by 5G networks with high subscriber cell density, is required. In the Ultra-Dense Networks (UDNs) of 5G and beyond high demanding networks paired with beamforming and massive MIMO technologies, ML struggles to define network traffic aspects distinctively, especially when they are projected to be much more dynamic and complicated. This paper presents a state-of-the-art analysis of the combined and multiple uses of ML along with MIMO technology in 5G Networks.1 aBouras, Christos1 aProkopiou, Ioannis1 aGkamas, Apostolos1 aKokkinos, Vasileios  uhttp://telematics.upatras.gr/telematics/publications/performance-analysis-mimo-using-machine-learning-5g-networks