95 research outputs found
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
D2D-Based Grouped Random Access to Mitigate Mobile Access Congestion in 5G Sensor Networks
The Fifth Generation (5G) wireless service of sensor networks involves
significant challenges when dealing with the coordination of ever-increasing
number of devices accessing shared resources. This has drawn major interest
from the research community as many existing works focus on the radio access
network congestion control to efficiently manage resources in the context of
device-to-device (D2D) interaction in huge sensor networks. In this context,
this paper pioneers a study on the impact of D2D link reliability in
group-assisted random access protocols, by shedding the light on beneficial
performance and potential limitations of approaches of this kind against
tunable parameters such as group size, number of sensors and reliability of D2D
links. Additionally, we leverage on the association with a Geolocation Database
(GDB) capability to assist the grouping decisions by drawing parallels with
recent regulatory-driven initiatives around GDBs and arguing benefits of the
suggested proposal. Finally, the proposed method is approved to significantly
reduce the delay over random access channels, by means of an exhaustive
simulation campaign.Comment: First submission to IEEE Communications Magazine on Oct.28.2017.
Accepted on Aug.18.2019. This is the camera-ready versio
Enhancements in spectrum management techniques for heterogeneous 5G future networks
Mención Internacional en el tÃtulo de doctorIn the last decade, cellular networks are undergoing with a radical change in their basic design foundations. The huge increase in traffic demand requires a novel design of future cellular networks.
Driven by this increase, a network densification phenomena is occurring thereby, which in turns requires to devise efficient and reliable mechanisms to deal with the interference problems resulting from such densification. The architecture and mechanisms resulting from such drastic re-design of the network are commonly referred under the term ’5G network’.
In this context, this work unveils that current networking solutions are no longer sufficient to (i) provide the required network spectral efficiency, and (ii) guarantee the desired level of quality of experience from the user side. In order to address this problem, in this thesis we propose a novel SDN-like framework that incorporates the needed mechanisms to improve spectral efficiency while delivering the desired quality of experience to users. In particular, our architecture includes the following two approaches:
Our first approach addresses the intercell interference issues resulting from high network densification. To this end, we propose novel mechanisms to mitigate the inter-cell interference problem. We address the design of such schemes from two angles: (i) a controller-aided mechanism, which gathers all the information of the network at a centralized point and, based on this information, optimally schedules the transmission from different users, and (ii) a semi-distributed mechanism, which limits the signaling overhead involved in sending the information to a centralized point while providing close to optimal performance. One of the key novelties of our scheduling algorithms is that they are based on the Almost Blank SubFrame (ABSF) scheme; indeed, this scheme has been standardized only recently and very little work has addressed the design of algorithm to use it.
Our second approach addresses spectral efficiency from a complementary angle: cellular traffic offloading for content update applications. This approach leverages high user mobility to offload the cellular downlink traffic through a device-to-device communication.
In this context, we propose an adaptive algorithm to decide how to optimally transmit content to base stations in order to maximize traffic offload. By relying on control theory techniques, our approach delivers near optimally performance.
A third key contribution of this thesis is the design of a solution that combines the above two approaches. In particular, our solution takes into account that traffic offload is taking place in the network and addresses the design of an optimal scheduling algorithm that leverages on the Almost Blank SubFrame (ABSF) scheme. Indeed, the combination of these kind of approaches has received little attention from the literature.
The feasibility and performance of the approaches described above are thoroughly evaluated and compared against state-of-the-art solutions through an exhaustive simulation campaign. Our results show that the proposed approaches outperform conventional eICIC techniques as well as standard offloading mechanisms, respectively, and confirm their feasibility in terms of overhead and computational complexity.
To the best of our knowledge, this thesis is the first attempt to design an unified framework which is able to optimally perform offloading for content-update distribution applications while boosting the network performance in terms of spectral efficiency.Programa Oficial de Doctorado en IngenierÃa TelemáticaPresidente: Pablo Serrano Yáñez-Mingot.- Secretario: Juan José Alacaraz EspÃn.- Vocal: Matteo Cesan
Passive and Privacy-preserving Human Localization via mmWave Access Points for Social Distancing
The pandemic outbreak has profoundly changed our life, especially our social
habits and communication behaviors. While this dramatic shock has heavily
impacted human interaction rules, novel localization techniques are emerging to
help society in complying with new policies, such as social distancing.
Wireless sensing and machine learning are well suited to alleviate viruses
propagation in a privacy-preserving manner. However, its wide deployment
requires cost-effective installation and operational solutions. In public
environments, individual localization information-such as social
distancing-needs to be monitored to avoid safety threats when not properly
observed. To this end, the high penetration of wireless devices can be
exploited to continuously analyze-and-learn the propagation environment,
thereby passively detecting breaches and triggering alerts if required. In this
paper, we describe a novel passive and privacy-preserving human localization
solution that relies on the directive transmission properties of mmWave
communications to monitor social distancing and notify people in the area in
case of violations. Thus, addressing the social distancing challenge in a
privacy-preserving and cost-efficient manner. Our solution provides an overall
accuracy of about 99% in the tested scenarios
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