1,033 research outputs found
Integration of Carrier Aggregation and Dual Connectivity for the ns-3 mmWave Module
Thanks to the wide availability of bandwidth, the millimeter wave (mmWave)
frequencies will provide very high data rates to mobile users in next
generation 5G cellular networks. However, mmWave links suffer from high
isotropic pathloss and blockage from common materials, and are subject to an
intermittent channel quality. Therefore, protocols and solutions at different
layers in the cellular network and the TCP/IP protocol stack have been proposed
and studied. A valuable tool for the end-to-end performance analysis of mmWave
cellular networks is the ns-3 mmWave module, which already models in detail the
channel, Physical (PHY) and Medium Access Control (MAC) layers, and extends the
Long Term Evolution (LTE) stack for the higher layers. In this paper we present
an implementation for the ns-3 mmWave module of multi connectivity techniques
for 3GPP New Radio (NR) at mmWave frequencies, namely Carrier Aggregation (CA)
and Dual Connectivity (DC), and discuss how they can be integrated to increase
the functionalities offered by the ns-3 mmWave module.Comment: 9 pages, 7 figures, submitted to the Workshop on ns-3 (WNS3) 201
The edge cloud: A holistic view of communication, computation and caching
The evolution of communication networks shows a clear shift of focus from
just improving the communications aspects to enabling new important services,
from Industry 4.0 to automated driving, virtual/augmented reality, Internet of
Things (IoT), and so on. This trend is evident in the roadmap planned for the
deployment of the fifth generation (5G) communication networks. This ambitious
goal requires a paradigm shift towards a vision that looks at communication,
computation and caching (3C) resources as three components of a single holistic
system. The further step is to bring these 3C resources closer to the mobile
user, at the edge of the network, to enable very low latency and high
reliability services. The scope of this chapter is to show that signal
processing techniques can play a key role in this new vision. In particular, we
motivate the joint optimization of 3C resources. Then we show how graph-based
representations can play a key role in building effective learning methods and
devising innovative resource allocation techniques.Comment: to appear in the book "Cooperative and Graph Signal Pocessing:
Principles and Applications", P. Djuric and C. Richard Eds., Academic Press,
Elsevier, 201
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
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