@inbook {4569,
	title = {An Introduction of Upcoming Radio Resource Management Techniques for 5G Networks},
	booktitle = {Advances in Engineering Research, Nova Science Publishers},
	volume = {46},
	year = {2022},
	pages = {147-188},
	chapter = {3},
	abstract = {5G 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.},
	author = {Christos Bouras and Vasileios Kokkinos and Apostolos Gkamas and Evangelos Michos and Ioannis Sina and Ioannis Prokopiou and Foivos Allayiotis}
}
@conference {4589,
	title = {Α Machine Learning Mechanism for improved Energy Consumption in 5G Small Cells},
	booktitle = {The 27th  IEEE Symposium on Computers and Communications (ISCC 2022), June 30 {\textendash} July 3, 2022, Rhodes, Greece},
	year = {2022},
	abstract = {By introducing 5G networks in our lives, we are faced with the challenge to lower energy consumption, which is expected to increase significantly. As a result, the communications sector must devise solutions to improve the energy efficiency of 5G networks while maintaining spectrum performance. We propose a Machine Learning Mechanism that provides new sleep mode functionalities, that reduce energy consumption during idle hours, on small cells. We will describe our model and examine its performance using data collected from a simulated environment. Furthermore, we present the results of our findings and conclude that such models are necessary for the introduction of 5G networks. Lastly, possible future steps, are discussed.},
	author = {Foivos Allayiotis and Christos Bouras and Vasileios Kokkinos}
}
@conference {4571,
	title = {A mechanism for 5G MIMO performance optimization and evaluation},
	booktitle = {The 13th IFIP Wireless and Mobile Networking Conference (WMNC 2021),  October 20 {\textendash} 22, 2021, Montreal, Canada },
	year = {2021},
	abstract = {MIMO (Multiple-Input Multiple-Output) technology has become a fundamental technique in current wireless communications, both in cellular and Wi-Fi networks. Fifth generation networks (5G) are expected to support extremely high data rates and more reliable services. The performance of 5G networks can be significantly improved with MIMO; yet the integration of optimization algorithms can further improve its performance. The purpose of this paper is to study an optimization technique for Channel State Prediction in a MIMO environment. We will examine a specific interconnection between 5G and optimization algorithms, to produce a feasible Channel State Prediction mechanism. For our experiments, we use a Dataset produced by the DeepMIMO simulator, which sets up all necessary parameters for our experiment. On this dataset we apply the Hungarian Algorithm to optimize the total throughput and based on the results we conclude that such mechanism is feasible and required for the introduction of 5G Networks in day-to-day tasks.},
	author = {Dimosthenis Tzanakos and Foivos Allayiotis and Vasileios Kokkinos and Christos Bouras}
}
