On The Modelling and Performance Analysis of Lower Layer Mobility in 5G-Advanced

One of upcoming mobility enhancements in 5G-Advanced networks is to execute handover based on Layer 1 (L1) measurements using the so called lower layer mobility procedure. In this paper, we provide a system model for lower layer mobility procedure and we evaluate it against existing higher layer mobility procedures, such as baseline and conditional handover, using system level simulations. The benefits and drawbacks of lower layer mobility procedure are analyzed and compared against higher layer handover mechanisms using the relevant mobility key performance indicators. It has been shown that lower layer mobility procedure outperforms the existing handover mechanisms with respect to radio communication reliability at the expense of higher number of handovers and ping-pongs. To tackle these drawbacks, additional filtering for the L1 measurements used in handover decision is introduced to reduce the fluctuations caused by fast fading and measurement errors. Moreover, lower layer mobility procedure is enhanced with dynamic switching mechanism enabling the UE to change cells without being reconfigured by the network. The evaluations have shown that the introduction of such techniques is beneficial in reducing the number of ping-pongs and signaling overhead at the expense of an increase in the delay to react to rapid signal degradation and resource reservation overhead, respectively.Comment: 9 pages, Submitted to IEEE WCNC 202

Low-Latency Infrastructure-Based Cellular V2V Communications for Multi-Operator Environments With Regional Split

[EN] Mobile network operators are interested in providing Vehicle-to-Vehicle (V2V) communication services using their cellular infrastructure. Regional split of operators is one possible approach to support multi-operator infrastructure-based cellular V2V communication. In this approach, a geographical area is divided into non-overlapping regions, each one served by a unique operator. Its main drawback is the communication interruption motivated by the inter-operator handover in border areas, which prevents the fulfillment of the maximum end-to-end (E2E) latency requirements of fifth generation (5G) V2V services related to autonomous driving. In this work, we enable a fast inter-operator handover based on the pre-registration of the users on multiple operators, which substantially reduces the handover time to guarantee maximum E2E latency values of 100 ms in non-congested scenarios. To further reduce the latency of time-critical services to always less than 70 ms, even with the handover interruption time, while providing a latency around 20 ms in the majority of locations, we propose to complement the former technique with a mobile edge computing approach. Our proposal consists in the localization of application servers and broadcasting entities in all the base stations, to avoid the communication through the core network, together with the use of a new set of nodes in the base stations of cross-border areas called inter-operator relays, to minimize the communication latency between operators. Based on analytic and simulation results, it is demonstrated that the proposed techniques are effective to support low-latency infrastructure-based cellular V2V communications in multi-operator environments with regional split.The work of S. Roger was partially supported by the Spanish Ministry of Science, Innovation and Universities through grant number RYC-2017-22101.Martín-Sacristán, D.; Roger, S.; Garcia-Roger, D.; Monserrat Del Río, JF.; Spapis, P.; Zhou, C.; Kaloxylos, A. (2021). Low-Latency Infrastructure-Based Cellular V2V Communications for Multi-Operator Environments With Regional Split. IEEE Transactions on Intelligent Transportation Systems. 22(2):1052-1067. https://doi.org/10.1109/TITS.2019.29620971052106722

D2.2 Draft Overall 5G RAN Design

This deliverable provides the consolidated preliminary view of the METIS-II partners on the 5 th generation (5G) radio access network (RAN) design at a mid-point of the project. The overall 5G RAN is envisaged to operate over a wide range of spectrum bands comprising of heterogeneous spectrum usage scenarios. More precisely, the 5G air interface (AI) is expected to be composed of multiple so-called AI variants (AIVs), which include evolved legacy technology such as Long Term Evolution Advanced (LTE-A) as well as novel AIVs, which may be tailored to particular services or frequency bands.Arnold, P.; Bayer, N.; Belschner, J.; Rosowski, T.; Zimmermann, G.; Ericson, M.; Da Silva, IL.... (2016). D2.2 Draft Overall 5G RAN Design. https://doi.org/10.13140/RG.2.2.17831.1424

Αντίληψη κατάστασης ενισχυμένη με γνώση για αυτοδιαχειριζόμενα δίκτυα

The networks in the future it is envisaged that they will be able to operate in an autonomous manner. In other words, it is consider that the networks will monitor their environment, analyze the environment stimuli, plan their operation and execute their plan. Moreover, the networks should be efficient and adaptable solutions so as to cover the diverse network requirements, ranging from typical human traffic, to ultra reliable or massive machine type traffic. This thesis aims at providing a scheme for situation aware networking, based on a hierarchical architecture, which enables the network elements to operate in a self managed way. We propose the introduction of two levels of hierarchy in the network management and control, the Network Element Controllers (NEC), and the Network Domain Controllers (NDC). The first ones have local network view and may proceed in handling of local problems, whereas the later have broader network view and may identify optimization opportunities or problems that are related to larger network compartments. Both NECs and NDCs are able to characterize their environment and identify their operational status, a functionality defined as situation perception. The development of the previously mentioned situation perception mechanisms, based on fuzzy reasoners, is the second major contribution of this thesis. The developed Situation schemes target QoS degradation events’ identification, Load events’ identification, and Cooperative power control. The third major contribution of this dissertation is the proposal of two learning schemes (a supervised one and an unsupervised one) for the enhancement of the situation perception mechanisms. The enhancement is related to the adaptation of the environment modeling of the fuzzy reasoners. This functionality enables the network elements to operate in new, unknown environments. The presentation of a generic reference problem, which is linked to the learning schemes, enables the application of these solutions in other problems, with similar formulations. Finally, the learning schemes are compared so as to provide directions on how these schemes may be used in other similar problems on the one hand, and what are the drawbacks and benefits of each scheme on the other.Τα μελλοντικά δίκτυα θα έχουν τη δυνατότητα να λειτουργούν με αυτόνομο τρόπο. Τα συστήματα αυτά μπορούν να διαχειρίζονται ένα σύνολο από λειτουργίες, που περιλαμβάνουν να την των ελεγκήρων ασαφούς λογικής. Αναπτύχθηκαν δύο μηχανισμοί εκμάθησης που βασίζονται σε αλλαγή της μοντελοποίησης του περιβάλλοντος του μηχανισμού λήψης αποφάσεων. Ο πρώτος μηχανισμός εκμάθησης βασίζεται σε ένα σχήμα εποπτευμένης μάθησης, ενώ ο δεύτερος σε ένα σχήμα μη εποπτευμένης μάθησης. Οι δύο μηχανισμοί στηρίζονται σε μία βασική μοντελοποίηση του προβλήματος εκμάθησης (που επίσης προτείνεται στα πλαίσια αυτής της διατριβής) και αποσκοπεί στη γενίκευση των συγκεκριμένων μεθόδων εκμάθησης

A Fuzzy Reinforcement Learning Approach for Pre-Congestion Notification Based Admission Control

Part 2: Autonomic and Distributed Network ManagementInternational audienceAdmission 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

Technological Enablers for Self-manageable Future Internet Elements

In its 5(th) decade, the Internet’s future evolution is hampered by the increasing complexity and sophistication of the processes managing its infrastructure. Management costs require a technically skilled human intellect, amounting to a substantial part of total cost of ownership. Self-management is positioned as a key capacity towards a scalable Future Internet architecture. Herein we present key technological enablers for self-manageable Future Internet systems, namely monitoring, decision-making and machine learning. We survey the state-of-the-art in network management aspects of these thematic areas and present a use case where their synergetic capacities render solutions to a known transport problem in wireless settings. Finally, we conclude the paper with a discussion of important issues for these technological enablers in the context of Future Internet