15,830 research outputs found
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
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
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
AN ENERGY EFFICIENT CROSS-LAYER NETWORK OPERATION MODEL FOR MOBILE WIRELESS SENSOR NETWORKS
Wireless sensor networks (WSNs) are modern technologies used to sense/control the environment whether indoors or outdoors. Sensor nodes are miniatures that can sense a specific event according to the end user(s) needs. The types of applications where such technology can be utilised and implemented are vast and range from households’ low end simple need applications to high end military based applications. WSNs are resource limited. Sensor nodes are expected to work on a limited source of power (e.g., batteries). The connectivity quality and reliability of the nodes is dependent on the quality of the hardware which the nodes are made of. Sensor nodes are envisioned to be either stationary or mobile. Mobility increases the issues of the quality of the operation of the network because it effects directly on the quality of the connections between the nodes
Distance-based sensor node localization by using ultrasound, RSSI and ultra-wideband - A comparision between the techniques
Wireless sensor networks (WSNs) have become one of the most important topics in wireless communication during the last decade. In a wireless sensor system, sensors are spread over a region to build a sensor network and the sensors in a region co-operate to each other to sense, process, filter and routing.
Sensor Positioning is a fundamental and crucial issue for sensor network operation and management. WSNs have so many applications in different areas such as health-care, monitoring and control, rescuing and military; they all depend on nodes being able to accurately determine their locations.
This master’s thesis is focused on distance-based sensor node localization techniques; Received signal strength indicator, ultrasound and ultra-wideband. Characteristics and factors which affect these distance estimation techniques are analyzed theoretically and through simulation the quality of these techniques are compared in different scenarios.
MDS, a centralized algorithm is used for solving the coordinates. It is a set of data analysis techniques that display the structure of distance-like data as a geometrical picture. Centralized and distributed implementations of MDS are also discussed.
All simulations and computations in this thesis are done in Matlab. Virtual WSN is simulated on Sensorviz. Sensorviz is a simulation and visualization tool written by Andreas Savvides.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format
Distance-based sensor node localization by using ultrasound, RSSI and ultra-wideband - A comparision between the techniques
Wireless sensor networks (WSNs) have become one of the most important topics in wireless communication during the last decade. In a wireless sensor system, sensors are spread over a region to build a sensor network and the sensors in a region co-operate to each other to sense, process, filter and routing.
Sensor Positioning is a fundamental and crucial issue for sensor network operation and management. WSNs have so many applications in different areas such as health-care, monitoring and control, rescuing and military; they all depend on nodes being able to accurately determine their locations.
This master’s thesis is focused on distance-based sensor node localization techniques; Received signal strength indicator, ultrasound and ultra-wideband. Characteristics and factors which affect these distance estimation techniques are analyzed theoretically and through simulation the quality of these techniques are compared in different scenarios.
MDS, a centralized algorithm is used for solving the coordinates. It is a set of data analysis techniques that display the structure of distance-like data as a geometrical picture. Centralized and distributed implementations of MDS are also discussed.
All simulations and computations in this thesis are done in Matlab. Virtual WSN is simulated on Sensorviz. Sensorviz is a simulation and visualization tool written by Andreas Savvides.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format
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