NOSTROMO - D1.2 - Final Project Results Report

The main objective of the NOSTROMO project has been to develop, demonstrate and evaluate an innovative modelling approach for the rigorous and comprehensive assessment of the performance impact of future ATM concepts and solutions at ECAC network level. This approach brings together the ability of bottom-up microscopic models to capture emergent behaviour and interdependencies between different solutions with the level of tractability and interpretability required to effectively support decision-making. This report provides a summary of NOSTROMO accomplishments and contributions to the SESAR Programme. It gathers technical lessons learned and concludes proposing further developments to facilitate the use of the NOSTROMO methodology in the future SESAR 3 Programme

NOSTROMO - D5.1 - ATM Performance Metamodels - Preliminary Release

This deliverable presents the results obtained with the meta-modelling process presented in D3.1 and D3.2 applied to the two micromodels (or simulators), Mercury and FLITAN, themselves implementing concepts from four SESAR solutions, PJ01.01, PJ07.02, PJ08-01, and PJ02.08. The objective of the meta-modelling process is explained briefly again in the introduction, in particular with respect to performance assessment. The rationale for the selection of the SESAR solutions implemented in the simulators are briefly explained too. The simulators are presented in two distinct chapters. First, a general presentation of each simulator is given, with past challenges and development, before explaining the development steps carried out to implement the concepts from the chosen solutions. Domain research questions that could be answered by these implementations are highlighted along the way. The meta-modelling process is then briefly explained again, followed by the results obtained with the two simulators, in distinct sections. The results highlight the performance of the meta-model with respect to approximating the output of the micromodels, but not the performance of the models themselves with respect to the research questions, which will be explored in WP7 instead. The deliverable closes with some considerations on the meta-modelling performance and next steps for this line of work

Deep Q-Learning for Self-Organizing Networks Fault Management and Radio Performance Improvement

We propose an algorithm to automate fault management in an outdoor cellular network using deep reinforcement learning (RL) against wireless impairments. This algorithm enables the cellular network cluster to self-heal by allowing RL to learn how to improve the downlink signal to interference plus noise ratio through exploration and exploitation of various alarm corrective actions. The main contributions of this paper are to 1) introduce a deep RL-based fault handling algorithm which self-organizing networks can implement in a polynomial runtime and 2) show that this fault management method can improve the radio link performance in a realistic network setup. Simulation results show that our proposed algorithm learns an action sequence to clear alarms and improve the performance in the cellular cluster better than existing algorithms, even against the randomness of the network fault occurrences and user movements.Comment: (c) 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

NOSTROMO: Lessons learned, conclusions and way forward

This White Paper sets out to explain the value that metamodelling can bring to air traffic management (ATM) research. It will define metamodelling and explore what it can, and cannot, do. The reader is assumed to have basic knowledge of SESAR: the Single European Sky ATM Research project. An important element of SESAR, as the technological pillar of the Single European Sky initiative, is to bring about improvements, as measured through specific key performance indicators (KPIs), and as implemented by a series of so-called SESAR 'Solutions'. These 'Solutions' are new or improved operational procedures or technologies, designed to meet operational and performance improvements described in the European ATM Master Plan