D4.2 Final report on trade-off investigations

Research activities in METIS WP4 include several as pects related to the network-level of future wireless communication networks. Thereby, a large variety of scenarios is considered and solutions are proposed to serve the needs envis ioned for the year 2020 and beyond. This document provides vital findings about several trade-offs that need to be leveraged when designing future network-level solutions. In more detail, it elaborates on the following trade- offs: • Complexity vs. Performance improvement • Centralized vs. Decentralized • Long time-scale vs. Short time-scale • Information Interflow vs. Throughput/Mobility enha ncement • Energy Efficiency vs. Network Coverage and Capacity Outlining the advantages and disadvantages in each trade-off, this document serves as a guideline for the application of different network-level solutions in different situations and therefore greatly assists in the design of future communication network architectures.Aydin, O.; Ren, Z.; Bostov, M.; Lakshmana, TR.; Sui, Y.; Svensson, T.; Sun, W.... (2014). D4.2 Final report on trade-off investigations. http://hdl.handle.net/10251/7676

D4.3 Final Report on Network-Level Solutions

Research activities in METIS reported in this document focus on proposing solutions to the network-level challenges of future wireless communication networks. Thereby, a large variety of scenarios is considered and a set of technical concepts is proposed to serve the needs envisioned for the 2020 and beyond. This document provides the final findings on several network-level aspects and groups of solutions that are considered essential for designing future 5G solutions. Specifically, it elaborates on: -Interference management and resource allocation schemes -Mobility management and robustness enhancements -Context aware approaches -D2D and V2X mechanisms -Technology components focused on clustering -Dynamic reconfiguration enablers These novel network-level technology concepts are evaluated against requirements defined by METIS for future 5G systems. Moreover, functional enablers which can support the solutions mentioned aboveare proposed. We find that the network level solutions and technology components developed during the course of METIS complement the lower layer technology components and thereby effectively contribute to meeting 5G requirements and targets.Aydin, O.; Valentin, S.; Ren, Z.; Botsov, M.; Lakshmana, TR.; Sui, Y.; Sun, W.... (2015). D4.3 Final Report on Network-Level Solutions. http://hdl.handle.net/10251/7675

Toward spectrum sharing: Opportunities and technical enablers

The vast increase in the number of mobile devices and their mobile traffic demands indicates the need for additional spectrum for cellular communications. Since it is not trivial to allocate exclusively new spectrum bands for cellular communications, it is imperative to improve the spectrum usage through new spectrum sharing mechanisms. This implies that the mobile network operators will have to cooperate and interact to cover the augmented traffic requirements. In this article we present a novel architectural framework that enables the mobile network operators and other spectrum license holders to exchange information about spectrum availability. We also present a novel spectrum sharing mechanism based on fuzzy logic to facilitate operators in selecting the most suitable spectrum to cover their needs. © 1979-2012 IEEE

Context-aware, user-driven, network-controlled RAT selection for 5G networks

It is expected that in the very near future, cellular networks will have to deal with a massive data traffic increase, as well as a vast number of devices. Optimal placement of the end devices to the most suitable access network is expected to provide the best Quality of Service (QoS) experience to the users but also the maximum utilization of the scarce wireless resources by the operators. Several on-going proposals attempt to overcome the existing barriers by enabling the use of Wi-Fis and femto-cells to cater for part of the load generated by the end devices. The evolution of the Access Network Discovery and Selection Function (ANDSF) for the core part of the cellular network, as well as the Hotspot 2.0 approach, are currently being subject to thorough discussions and studies and are expected to facilitate a seamless 3GPP-WiFi interworking. During the past years, several Radio Access Technology (RAT) selection schemes have been proposed. However, these schemes do not take into consideration the opportunities offered by these new standardized approaches. Our paper acts in a manifold way: Firstly, it proposes COmpAsS, a Context-Aware RAT Selection mechanism, the main part of which operates on the User Equipment (UE)-side, minimizing signaling overhead over the air interface and computation load on the base stations. Secondly, we discuss in detail the architectural perspective; i.e., the extensions needed in the network interfaces that enable the exchange of the required context information among the respective network entities and in accordance with the 3GPP trends in relation to the context-aggregating entities. Furthermore, we quantify the signaling overhead of the proposed mechanism by linking it to the current 3GPP specifications and performing a comprehensive per-parameter analysis. Finally, we evaluate the novel scheme via extensive simulations in a complex and realistic 5G use case, illustrating the clear advantages of our approach in terms of key QoS metrics, i.e. the user-experienced throughput and delay, both in the uplink and the downlink. © 2016 Elsevier B.V

Enhancing a fuzzy logic inference engine through machine learning for a self- Managed network

Existing network management systems have static and predefined rules or parameters, while human intervention is usually required for their update. However, an autonomic network management system that operates in a volatile network environment should be able to adapt continuously its decision making mechanism through learning from the system's behavior. In this paper, a novel learning scheme based on the network wide collected experience is proposed targeting the enhancement of network elements' decision making engine. The algorithm employs a fuzzy logic inference engine in order to enable self-managed network elements to identify faults or optimization opportunities. The fuzzy logic engine is periodically updated through the use of two well known data mining techniques, namely k-Means and k-Nearest Neighbor. The proposed algorithm is evaluated in the context of a load identification problem. The acquired results prove that the proposed learning mechanism improves the deduction capability, thus promoting our algorithm as an attractive approach for enhancing the autonomic capabilities of network elements. © 2011 Springer Science+Business Media, LLC

A scheme for adaptive self-diagnosis of QoS degradation in future networks

The capability of a network to identify problematic situations, named self-diagnosis, enables it to react promptly and autonomously once an event or error has been identified. The use of service information in this process enables it to identify more composite problems and to act more targeted in order to solve complex errors. This paper proposes a novel fuzzy logic-based self-diagnosis mechanism for identifying Quality of Service (QoS) degradation events. Furthermore, we introduce a framework for the adaptation of the self-diagnosis scheme, which enables the network elements to evolve the way they interpret the context information. The adaptation scheme is based on the statistical analysis of the measurements and reacts accordingly without requiring any external human intervention. The adaptive self-diagnosis scheme has been evaluated through simulations in order to showcase the benefits from its application in IP networks for the VoIP service. The simulation results show that the adaptive self-diagnosis scheme performs very well compared to existing solutions, increases significantly the event detection rate and, as a result, the capability of controlling the QoS on top of the involved network elements. © 2013 IFIP

A fuzzy reinforcement learning approach for pre-congestion notification based admission control

Admission control aims to compensate for the inability of slow-changing network configurations to react rapidly enough to load fluctuations. Even though many admission control approaches exist, most of them suffer from the fact that they are based on some very rigid assumptions about the per-flow and aggregate underlying traffic models, requiring manual reconfiguration of their parameters in a "trial and error" fashion when these original assumptions stop being valid. In this paper we present a fuzzy reinforcement learning admission control approach based on the increasingly popular Pre-Congestion Notification framework that requires no a priori knowledge about traffic flow characteristics, traffic models and flow dynamics. By means of simulations we show that the scheme can perform well under a variety of traffic and load conditions and adapt its behavior accordingly without requiring any overly complicated operations and with no need for manual and frequent reconfigurations. © 2012 IFIP International Federation for Information Processing

Coverage and capacity optimization of self-managed future internet wireless networks

Future Internet network management systems are expected to incorporate self-x capabilities in order to tackle the increased management needs that cannot be addressed through human intervention. Towards this end, Self-NET developed a self-management framework based on the introduction of cognitive capabilities in network elements. In this paper, the experimentation platform for "Coverage and Capacity Optimization of Self-managed Future Internet Wireless Network", incorporating the self-management framework of Self-NET, is presented. © 2010 Springer-Verlag