Experimental Validation for Spectrum Cartography using Adaptive Multi-kernels

acceptedVersionNivå

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

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

Context-aware Self-Optimization in Small-Cell Networks

Most mobile communications take place at indoor environments, especially in commercial and corporate scenarios. These places normally present coverage and capacity issues due to the poor signal quality, which degrade the end-user Quality of Experience (QoE). In these cases, mobile operators are offering small cells to overcome the indoor issues, being femtocells the main deployed base stations. Femtocell networks provide significant benefits to mobile operators and their clients. However, the massive integration and the particularities of femtocells, make the maintenance of these infrastructures a challenge for engineers. In this sense, Self-Organizing Networks (SON) techniques play an important role. These techniques are a key feature to intelligently automate network operation, administration and management procedures. SON mechanisms are based on the analysis of the mobile network alarms, counters and indicators. In parallel, electronics, sensors and software applications evolve rapidly and are everywhere. Thanks to this, valuable context information can be gathered, which properly managed can improve SON techniques performance. Within possible context data, one of the most active topics is the indoor positioning due to the immediate interest on indoor location-based services (LBS). At indoor commercial and corporate environments, user densities and traffic vary in spatial and temporal domain. These situations lead to degrade cellular network performance, being temporary traffic fluctuations and focused congestions one of the most common issues. Load balancing techniques, which have been identified as a use case in self-optimization paradigm for Long Term Evolution (LTE), can alleviate these congestion problems. This use case has been widely studied in macrocellular networks and outdoor scenarios. However, the particularities of femtocells, the characteristics of indoor scenarios and the influence of users’ mobility pattern justify the development of new solutions. The goal of this PhD thesis is to design and develop novel and automatic solutions for temporary traffic fluctuations and focused network congestion issues in commercial and corporate femtocell environments. For that purpose, the implementation of an efficient management architecture to integrate context data into the mobile network and SON mechanisms is required. Afterwards, an accurate indoor positioning system is developed, as a possible inexpensive solution for context-aware SON. Finally, advanced self-optimization methods to shift users from overloaded cells to other cells with spare resources are designed. These methods tune femtocell configuration parameters based on network information, such as ratio of active users, and context information, such as users’ position. All these methods are evaluated in both a dynamic LTE system-level simulator and in a field-trial

Journal of Telecommunications and Information Technology, 2017, nr 4

kwartalni

Functional neuroimaging of the somatosensory system with Ultra-high-field fMRI and MEG

Multimodal neuroimaging using a combination of Magnetoencephalography (MEG) and ultra-high-field fMRI are used in order to gain further insight into the neural oscillations and haemodynamic responses in the somatosensory cortex. Single pulse electrical median nerve stimulation (MNS) with regular and jittered intervals is used in MEG. A preliminary study is used to determine acceptable trial number and length, and highlights points to be considered in paradigm optimization. Time-frequency analysis shows that the largest activities are beta event-related desynchronization (ERD) and event-related synchronization (ERS) between 13Hz and 30Hz. No significant difference in both the induced oscillations and evoked responses are found. Paired pulse MNS with varying ISIs are studied using MEG and 7T fMRI. The beta ERD is suggested to have a gating role with a magnitude irrespective of the starting point of stimulus. Non-linearity effects both in beta ERD/ERS and P35m are shown for ISIs of up to 2s, implying that the non-linear neural responses to the stimulus may still contribute to the BOLD non-linearity even when the evoked response has returned to baseline. Multiple pulse MNS with varying pulse train length and frequency are also investigated using MEG and MEG-fMRI. The gating role of beta ERD is further confirmed and the N160m is suggested to be modulated under this role. No accumulative effect is seen in the ERS with increasing pulse number but the amplitude of the ERS is modulated by the frequency. This can be explained by a Cortical Activation Model (CAM). Efforts to spatially separate the beta ERD and ERS are shown for all three studies. Group averaged SAM images suggest a separation of activation areas along the central gyrus. Significant difference are found in the z MNI coordinate between beta ERD and ERS peak locations, suggesting that these two effects could arise from different generators. In the multiple pulse frequency study, by including the temporal signature of beta ERD and ERS as a regressor in BOLD fMRI analysis, delayed BOLD responses are located posterior to the standard BOLD response. However, the exact nature of the relationship between this delayed BOLD response and the ERS effects requires further work