477 research outputs found
Prediction-Based Energy Saving Mechanism in 3GPP NB-IoT Networks
The current expansion of the Internet of things (IoT) demands improved communication platforms that support a wide area with low energy consumption. The 3rd Generation Partnership Project introduced narrowband IoT (NB-IoT) as IoT communication solutions. NB-IoT devices should be available for over 10 years without requiring a battery replacement. Thus, a low energy consumption is essential for the successful deployment of this technology. Given that a high amount of energy is consumed for radio transmission by the power amplifier, reducing the uplink transmission time is key to ensure a long lifespan of an IoT device. In this paper, we propose a prediction-based energy saving mechanism (PBESM) that is focused on enhanced uplink transmission. The mechanism consists of two parts: first, the network architecture that predicts the uplink packet occurrence through a deep packet inspection; second, an algorithm that predicts the processing delay and pre-assigns radio resources to enhance the scheduling request procedure. In this way, our mechanism reduces the number of random accesses and the energy consumed by radio transmission. Simulation results showed that the energy consumption using the proposed PBESM is reduced by up to 34% in comparison with that in the conventional NB-IoT method
End-to-End Simulation of 5G mmWave Networks
Due to its potential for multi-gigabit and low latency wireless links,
millimeter wave (mmWave) technology is expected to play a central role in 5th
generation cellular systems. While there has been considerable progress in
understanding the mmWave physical layer, innovations will be required at all
layers of the protocol stack, in both the access and the core network.
Discrete-event network simulation is essential for end-to-end, cross-layer
research and development. This paper provides a tutorial on a recently
developed full-stack mmWave module integrated into the widely used open-source
ns--3 simulator. The module includes a number of detailed statistical channel
models as well as the ability to incorporate real measurements or ray-tracing
data. The Physical (PHY) and Medium Access Control (MAC) layers are modular and
highly customizable, making it easy to integrate algorithms or compare
Orthogonal Frequency Division Multiplexing (OFDM) numerologies, for example.
The module is interfaced with the core network of the ns--3 Long Term Evolution
(LTE) module for full-stack simulations of end-to-end connectivity, and
advanced architectural features, such as dual-connectivity, are also available.
To facilitate the understanding of the module, and verify its correct
functioning, we provide several examples that show the performance of the
custom mmWave stack as well as custom congestion control algorithms designed
specifically for efficient utilization of the mmWave channel.Comment: 25 pages, 16 figures, submitted to IEEE Communications Surveys and
Tutorials (revised Jan. 2018
Performance Comparison of Dual Connectivity and Hard Handover for LTE-5G Tight Integration in mmWave Cellular Networks
MmWave communications are expected to play a major role in the Fifth
generation of mobile networks. They offer a potential multi-gigabit throughput
and an ultra-low radio latency, but at the same time suffer from high isotropic
pathloss, and a coverage area much smaller than the one of LTE macrocells. In
order to address these issues, highly directional beamforming and a very
high-density deployment of mmWave base stations were proposed. This Thesis aims
to improve the reliability and performance of the 5G network by studying its
tight and seamless integration with the current LTE cellular network. In
particular, the LTE base stations can provide a coverage layer for 5G mobile
terminals, because they operate on microWave frequencies, which are less
sensitive to blockage and have a lower pathloss. This document is a copy of the
Master's Thesis carried out by Mr. Michele Polese under the supervision of Dr.
Marco Mezzavilla and Prof. Michele Zorzi. It will propose an LTE-5G tight
integration architecture, based on mobile terminals' dual connectivity to LTE
and 5G radio access networks, and will evaluate which are the new network
procedures that will be needed to support it. Moreover, this new architecture
will be implemented in the ns-3 simulator, and a thorough simulation campaign
will be conducted in order to evaluate its performance, with respect to the
baseline of handover between LTE and 5G.Comment: Master's Thesis carried out by Mr. Michele Polese under the
supervision of Dr. Marco Mezzavilla and Prof. Michele Zorz
Millimeter Wave Cellular Networks: A MAC Layer Perspective
The millimeter wave (mmWave) frequency band is seen as a key enabler of
multi-gigabit wireless access in future cellular networks. In order to overcome
the propagation challenges, mmWave systems use a large number of antenna
elements both at the base station and at the user equipment, which lead to high
directivity gains, fully-directional communications, and possible noise-limited
operations. The fundamental differences between mmWave networks and traditional
ones challenge the classical design constraints, objectives, and available
degrees of freedom. This paper addresses the implications that highly
directional communication has on the design of an efficient medium access
control (MAC) layer. The paper discusses key MAC layer issues, such as
synchronization, random access, handover, channelization, interference
management, scheduling, and association. The paper provides an integrated view
on MAC layer issues for cellular networks, identifies new challenges and
tradeoffs, and provides novel insights and solution approaches.Comment: 21 pages, 9 figures, 2 tables, to appear in IEEE Transactions on
Communication
Separation Framework: An Enabler for Cooperative and D2D Communication for Future 5G Networks
Soaring capacity and coverage demands dictate that future cellular networks
need to soon migrate towards ultra-dense networks. However, network
densification comes with a host of challenges that include compromised energy
efficiency, complex interference management, cumbersome mobility management,
burdensome signaling overheads and higher backhaul costs. Interestingly, most
of the problems, that beleaguer network densification, stem from legacy
networks' one common feature i.e., tight coupling between the control and data
planes regardless of their degree of heterogeneity and cell density.
Consequently, in wake of 5G, control and data planes separation architecture
(SARC) has recently been conceived as a promising paradigm that has potential
to address most of aforementioned challenges. In this article, we review
various proposals that have been presented in literature so far to enable SARC.
More specifically, we analyze how and to what degree various SARC proposals
address the four main challenges in network densification namely: energy
efficiency, system level capacity maximization, interference management and
mobility management. We then focus on two salient features of future cellular
networks that have not yet been adapted in legacy networks at wide scale and
thus remain a hallmark of 5G, i.e., coordinated multipoint (CoMP), and
device-to-device (D2D) communications. After providing necessary background on
CoMP and D2D, we analyze how SARC can particularly act as a major enabler for
CoMP and D2D in context of 5G. This article thus serves as both a tutorial as
well as an up to date survey on SARC, CoMP and D2D. Most importantly, the
article provides an extensive outlook of challenges and opportunities that lie
at the crossroads of these three mutually entangled emerging technologies.Comment: 28 pages, 11 figures, IEEE Communications Surveys & Tutorials 201
Final report on the evaluation of RRM/CRRM algorithms
Deliverable public del projecte EVERESTThis deliverable provides a definition and a complete evaluation of the RRM/CRRM algorithms selected in D11 and D15, and evolved and refined on an iterative process. The evaluation will be carried out by means of simulations using the simulators provided at D07, and D14.Preprin
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