A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Cooperative and coordinated Mobile Femtocells technology in high-speed vehicular environments: mobility and interference management

In future networks, most users who will be accessing wireless broadband will be vehicular. Serving those users cost-effectively and improving their signal quality has been the main concern of many studies. Thus, the deployment of Mobile Femtocell (Mobile-Femto) technology on public transportation is seen to be one of the promising solutions. Mobile-Femto comes with its mobility and interference challenges. Therefore, eliminating the Vehicular Penetration Loss (VPL) and interference while improving signal quality and mobility for train passengers is the main concern of this paper. The initial system-level evaluation showed that the dedicated Mobile-Femto deployment has great potential in improving users’ experience inside public transportation. The Downlink (DL) results of the Proposed Interference Management Scheme (PIMS) showed significant improvement in Mobile-Femto User Equipment (UE) gains (up to 50%) without impacting the performance of macro UEs. In contrast, the Uplink (UL) results showed noticeable gains for both macro UEs and Mobile-Femto UEs

ENERGY EFFICIENCY VIA HETEROGENEOUS NETWORK

The mobile telecommunication industry is growing at a phenomenal rate. On a daily basis, there are continuous inflow of mobile users and sophisticated devices into the mobile network. This has triggered a meteoric rise in mobile traffic; forcing network operators to embark on a series of projects to increase the capacity and coverage of mobile networks in line with growing traffic demands. A corollary to this development is the momentous rise in energy bills for mobile operators and the emission of a significant amount of CO2 into the atmosphere. This has become worrisome to the extent that regulatory bodies and environmentalist are calling for the adoption of more “green operation” to curtail these challenges. Green communication is an all-inclusive approach that champions the cause of overall network improvement, reduction in energy consumption and mitigation of carbon emission. The emergence of Heterogeneous network came as a means of fulfilling the vision of Green communication. Heterogeneous network is a blend of low power node overlaid on Macrocell to offload traffic from the Macrocell and enhance quality of service of cell edge users. Heterogeneous network seeks to boost the performance of LTE-Advanced beyond its present limit, and at the same time, reduce energy consumption in mobile wireless network. In this thesis, we explore the potential of heterogeneous network in enhancing the energy efficiency of mobile wireless network. Simulation process sees the use of a co-deployment of Macrocell and Picocell in cluster (Hot spot) and normal scenario. Finally, we compared the performance of each scenario using Cell Energy Efficiency and the Area Energy Efficiency as our performance metricfi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format