Context Information for Fast Cell Discovery in mm-wave 5G Networks

The exploitation of the mm-wave bands is one of the most promising solutions for 5G mobile radio networks. However, the use of mm-wave technologies in cellular networks is not straightforward due to mm-wave harsh propagation conditions that limit access availability. In order to overcome this obstacle, hybrid network architectures are being considered where mm-wave small cells can exploit an overlay coverage layer based on legacy technology. The additional mm-wave layer can also take advantage of a functional split between control and user plane, that allows to delegate most of the signaling functions to legacy base stations and to gather context information from users for resource optimization. However, mm-wave technology requires high gain antenna systems to compensate for high path loss and limited power, e.g., through the use of multiple antennas for high directivity. Directional transmissions must be also used for the cell discovery and synchronization process, and this can lead to a non-negligible delay due to the need to scan the cell area with multiple transmissions at different directions. In this paper, we propose to exploit the context information related to user position, provided by the separated control plane, to improve the cell discovery procedure and minimize delay. We investigate the fundamental trade-offs of the cell discovery process with directional antennas and the effects of the context information accuracy on its performance. Numerical results are provided to validate our observations.Comment: 6 pages, 8 figures, in Proceedings of European Wireless 201

Fast Cell Discovery in mm-wave 5G Networks with Context Information

The exploitation of mm-wave bands is one of the key-enabler for 5G mobile radio networks. However, the introduction of mm-wave technologies in cellular networks is not straightforward due to harsh propagation conditions that limit the mm-wave access availability. Mm-wave technologies require high-gain antenna systems to compensate for high path loss and limited power. As a consequence, directional transmissions must be used for cell discovery and synchronization processes: this can lead to a non-negligible access delay caused by the exploration of the cell area with multiple transmissions along different directions. The integration of mm-wave technologies and conventional wireless access networks with the objective of speeding up the cell search process requires new 5G network architectural solutions. Such architectures introduce a functional split between C-plane and U-plane, thereby guaranteeing the availability of a reliable signaling channel through conventional wireless technologies that provides the opportunity to collect useful context information from the network edge. In this article, we leverage the context information related to user positions to improve the directional cell discovery process. We investigate fundamental trade-offs of this process and the effects of the context information accuracy on the overall system performance. We also cope with obstacle obstructions in the cell area and propose an approach based on a geo-located context database where information gathered over time is stored to guide future searches. Analytic models and numerical results are provided to validate proposed strategies.Comment: 14 pages, submitted to IEEE Transaction on Mobile Computin

Adaptive Robust Traffic Engineering in Software Defined Networks

One of the key advantages of Software-Defined Networks (SDN) is the opportunity to integrate traffic engineering modules able to optimize network configuration according to traffic. Ideally, network should be dynamically reconfigured as traffic evolves, so as to achieve remarkable gains in the efficient use of resources with respect to traditional static approaches. Unfortunately, reconfigurations cannot be too frequent due to a number of reasons related to route stability, forwarding rules instantiation, individual flows dynamics, traffic monitoring overhead, etc. In this paper, we focus on the fundamental problem of deciding whether, when and how to reconfigure the network during traffic evolution. We propose a new approach to cluster relevant points in the multi-dimensional traffic space taking into account similarities in optimal routing and not only in traffic values. Moreover, to provide more flexibility to the online decisions on when applying a reconfiguration, we allow some overlap between clusters that can guarantee a good-quality routing regardless of the transition instant. We compare our algorithm with state-of-the-art approaches in realistic network scenarios. Results show that our method significantly reduces the number of reconfigurations with a negligible deviation of the network performance with respect to the continuous update of the network configuration.Comment: 10 pages, 8 figures, submitted to IFIP Networking 201

Facing the Millimeter-wave Cell Discovery Challenge in 5G Networks with Context-awareness

The introduction of mm-wave technologies in the future 5G networks poses a rich set of network access challenges. We need new ways of dealing with legacy network functionalities to fully unleash their great potential, among them the cell discovery procedure is one of the most critical. In this article, we propose novel cell discovery algorithms enhanced by the context information available through a C-/Uplane- split heterogeneous network architecture. They rely on a geo-located context database to overcome the severe effects of obstacle blockages. Moreover, we investigate the coordination problem of multiple mm-wave base stations that jointly process user access requests. We show that optimizing the resource allocated to the discovery has a great importance in defining perceived latency and supported user request rate. We have performed complete and accurate numerical simulations to provide a clear overview of the main challenging aspects. Results show that the proposed solutions have an outstanding performance with respect to basic discovery approaches and can fully enable mm-wave cell discovery in 5G networks

Beyond cellular green generation: Potential and challenges of the network separation

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This article introduces the ideas investigated in the BCG2 project of the GreenTouch consortium. The basic concept is to separate signaling and data in the wireless access network. Transmitting the signaling information separately maintains coverage even when the whole data network is adapted to the current load situation. Such network-wide adaptation can power down base stations when no data transmission is needed and, thus, promises a tremendous increase in energy efficiency. We highlight the advantages of the separation approach and discuss technical challenges opening new research directions. Moreover, we propose two analytical models to assess the potential energy efficiency improvement of the BCG2 approach

Will the Upper 6 GHz Bands Work for 5G NR?

In the never-ending pursuit of bandwidth, mid-bands have been recently reconsidered as the primary spectrum for 5G NR and its evolution. Laying between the crowded lower frequency bands and the propagation-unfriendly higher bands (i.e., millimeter wave), the upper 6 GHz band (6425-7125 MHz) presents a valuable compromise between capacity and coverage. Recognizing its potential, the research community has expressed interest in this spectrum and conducted several studies. Despite this enthusiasm, the deployment of upper 6 GHz testbeds remains elusive. This paper aims to address this gap by presenting the results of a comprehensive measurement campaign conducted on a 5G NR cellular system operating in the upper 6 GHz band (6425-7125 MHz), specifically deployed within the Politecnico di Milano campus. Our objective was to evaluate the system's performance in a realistic environment and provide insights supported by empirical measurements. The measurement campaign yielded positive results, showcasing a remarkable channel capacity in urban areas, which remained consistently high even at the cell edge and in challenging non-line-of-sight and outdoor-to-indoor scenarios

Semi-distributed Traffic Engineering for Elastic Flows in Software Defined Networks

Software-Defined Networking (SDN) is becoming the reference paradigm to provide advanced Traffic Engineering (TE) solutions for future networks. However, taking all TE decisions at the controller, in a centralized fashion, may require long delays to react to network changes. With the most recent advancements in SDN programmability some decisions can (and should indeed) be offloaded to switches. In this paper we present a model to route elastic demands in a general network topology adopting a semi-distributed approach of the control plane to deal with path congestion. Specifically, we envision a Stackelberg approach where the SDN controller takes the role of Leader, choosing the most appropriate subset of routing paths for the selfish users (network switches), which behave as Followers, making local routing decisions based on path congestion. To overcome the complexity of the problem and meet the time requirements of real-life settings, we propose effective heuristic procedures which take into accurate account traffic dynamics, considering a stochastic scenario where both the number and size of flows change over time. We test our framework with a custom-developed simulator in different network topologies and instance sizes. Numerical results show how our model and heuristics achieve the desired balance between making global decisions and reacting rapidly to congestion events

Green Approach for Joint Management of Geo-Distributed Data Centers and Interconnection Networks

Every time an Internet user downloads a video, shares a picture, or sends an email, his/her device addresses a data center and often several of them. These complex systems feed the web and all Internet applications with their computing power and information storage, but they are very energy hungry. The energy consumed by Information and Communication Technology (ICT) infrastructures is currently more than 4\% of the worldwide consumption and it is expected to double in the next few years. Data centers and communication networks are responsible for a large portion of the ICT energy consumption and this has stimulated in the last years a research effort to reduce or mitigate their environmental impact. Most of the approaches proposed tackle the problem by separately optimizing the power consumption of the servers in data centers and of the network. However, the Cloud computing infrastructure of most providers, which includes traditional telcos that are extending their offer, is rapidly evolving toward geographically distributed data centers strongly integrated with the network interconnecting them. Distributed data centers do not only bring services closer to users with better quality, but also provide opportunities to improve energy efficiency exploiting the variation of prices in different time zones, the locally generated green energy, and the storage systems that are becoming popular in energy networks. In this paper, we propose an energy aware joint management framework for geo-distributed data centers and their interconnection network. The model is based on virtual machine migration and formulated using mixed integer linear programming. It can be solved using state-of-the art solvers such as CPLEX in reasonable time. The proposed approach covers various aspects of Cloud computing systems. Alongside, it jointly manages the use of green and brown energies using energy storage technologies. The obtained results show that significant energy cost savings can be achieved compared to a baseline strategy, in which data centers do not collaborate to reduce energy and do not use the power coming from renewable resources

Asymptotic Analysis of Backlog Estimates for Dynamic Frame Aloha

In this paper a new analysis is presented that allowsto investigate the asymptotic behavior of some backlog estimation procedures for Dynamic Frame Aloha (DFA) in Radio Frequency Identification (RFID) environment. Although efficiency e-1 can theoretically be reached, none of the solution proposed in the literature has been shown to reach such value. Here we analyze first the Schoute’s backlog estimate, which is very attractive for its simplicity, and formally show that its asymptotic efficiency is 0:311 for any finite initial frame length. Since the analysis shows how the Schoute’s estimate impairment can be avoided, wefurther propose the Asymptotic Efficient Estimate (AE2), an improvement of the Schoute’s one, that exploits the Frame Restart property of the standard and that is proved to asymptotically reach efficiency e-1