Panagiotis Spapis

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.

An efficient RAT selection mechanism for 5G cellular networks

The design of an efficient radio access selection mechanism for 5G cellular networks is of paramount importance. Several proposals exist in the literature, but up to now the deployed systems are still using simple mechanisms mainly related to the evaluation of the RSS to make a handover decision. However, this is an inadequate solution for 5G networks. In this paper, we describe a novel multi-criteria handover scheme, we provide details on solutions for acquiring the necessary contextual information and we describe the algorithm to select the most appropriate RAT. Our solution is based on the use of fuzzy logic controllers for combining diverse inputs (such as a users mobility, the load of the candidate base stations etc.) The efficiency of our mechanism is evaluated through appropriate simulations. The results related to throughput, delay and the number of the executed handovers clearly show the merits of our proposal when compared to a well-established LTE handover algorithm.

Impact of network slicing on 5G Radio Access Networks

Network slicing addresses the deployment of multiple logical networks as independent business operations on a common physical infrastructure. The concept has initially been proposed for the 5th Generation (5G) core network (CN) however, it has not been investigated yet what network slicing would represent to the design of the 5G radio access network (RAN). The paper explains how network slicing may impact several aspects of the 5G RAN design such as the protocol architecture, the design of network functions (NFs) and the management framework that needs to support both the management of the infrastructure to be shared among the slices and the slice operation.

5G service requirements and operational use cases: Analysis and METIS II vision

One of the objectives of METIS-II project is to facilitate discussion on scenarios, use cases, KPIs and requirements for 5G, building upon the comprehensive work conducted in the METIS-I project and taking the work of other European projects as well as other bodies such as ITU-R, NGMN, etc. into account. This paper analyses the landscape of 5G use cases and presents METIS-II 5G use cases that cover the main 5G services, have stringent requirements and whose technical solutions are expected to serve other similar use cases as well. It also links these use cases to the business cases defined by 5G PPP so that requirements of vertical industries can be taken into account when designing the 5G Radio Access Network (RAN).

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.

On the Way to Massive Access in 5G: Challenges and Solutions for Massive Machine Communications

Machine Type Communication (MTC) is expected to play a significant role in fifth generation (5G) wireless and mobile communication systems. The requirements of such type of communication mainly focus on scalability (i.e., number of supported end-devices) and timing issues. Since existing cellular systems were not designed to support such vast number of devices, it is expected that they will throttle the limited network resources. In this paper, we introduce an effective solution for handling the signalling bottlenecks caused by massive machine communications in future 5G systems. The proposed approach is based on a device classification scheme using the devices’ requirements and position for forming groups of devices with the same or similar device characteristics. Our scheme is analysed, and the evaluation results indicate that the proposed solution yields significant reduction in collisions compared to the standard when MTC devices attempt to access the Random Access CHannel (RACH).

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.

Embedding cognition in wireless network management: an experimental perspective

The Future Internet promises to pervade our everyday lives by interweaving an increased variety of access technologies (cellular broadband, wireless hotspots, short range radios). Cognitive network management is a promising approach to cope with such access diversity and to enable dynamic adaptation of the network configurations and parameters. This article reveals the insights and conclusions of a unique real life implementation of a cognitive architecture, comprising software agents and artificial intelligence algorithms, and its deployment within a heterogeneous access network composed of a Broadband WiMAX base station and WiFi access points. We discuss the architectural challenges that this approach poses, and present the distributed software architecture we implemented. We detail the experimental access network we deployed and the algorithms that enable channel reselection and vertical assisted handover. We conclude with our findings and recommendations for the deployment of cognitive network management architectures and technologies in the Future Internet.

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.

A context extraction and profiling engine for 5G network resource mapping

Abstract Future 5G network ecosystems comprise a plethora of 3GPP and non 3GGP Radio Access Technologies - RATs. Deployment scenarios envision a multi-layer use of macro, micro and femto-cells where multi-mode end devices, supporting different applications, are served by different technologies. The association of end devices to the most appropriate RAT/layer will therefore become a tantalizing process necessitating the introduction of mechanisms that decide and execute an optimal mapping. The latter is of paramount importance since sub-optimal configuration of network components will affect overall network performance. Towards this end, we introduce the Context Extraction and Profiling Engine (CEPE), a knowledge discovery (KDD) framework catering for the extraction and exploitation of user behavioral patterns from network and service information. An eNB exploits the knowledge scheme derived by CEPE in order to improve the placement of end devices to RATs/layers. In the context of this paper, we provide a thorough analysis of existing standards, research papers and patents, discuss the main innovation of our proposal and highlight the differences with existing schemes. Building on use cases involving mobility management mechanisms that typically affect device to technology mapping (i.e. cell (re)selection, handover) we provide an extensive set of experiments that demonstrate the validity and viability of our idea. Overall evaluation showcases that CEPE achieves high quality results thus emerging as a viable approach for network optimization in future 5G environments.