3,898 research outputs found
A Novel Millimeter-Wave Channel Simulator and Applications for 5G Wireless Communications
This paper presents details and applications of a novel channel simulation
software named NYUSIM, which can be used to generate realistic temporal and
spatial channel responses to support realistic physical- and link-layer
simulations and design for fifth-generation (5G) cellular communications.
NYUSIM is built upon the statistical spatial channel model for broadband
millimeter-wave (mmWave) wireless communication systems developed by
researchers at New York University (NYU). The simulator is applicable for a
wide range of carrier frequencies (500 MHz to 100 GHz), radio frequency (RF)
bandwidths (0 to 800 MHz), antenna beamwidths (7 to 360 degrees for azimuth and
7 to 45 degrees for elevation), and operating scenarios (urban microcell, urban
macrocell, and rural macrocell), and also incorporates multiple-input
multiple-output (MIMO) antenna arrays at the transmitter and receiver. This
paper also provides examples to demonstrate how to use NYUSIM for analyzing
MIMO channel conditions and spectral efficiencies, which show that NYUSIM is an
alternative and more realistic channel model compared to the 3rd Generation
Partnership Project (3GPP) and other channel models for mmWave bands.Comment: 7 pages, 8 figures, in 2017 IEEE International Conference on
Communications (ICC), Paris, May 201
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Providing over-the-horizon awareness to driver support systems
Vehicle-to-vehicle communications is a promising technique for driver support systems to increase traffic safety and efficiency. A proposed system is the Congestion Assistant [1], which aims at supporting drivers when approaching and driving in a traffic jam. Studies have shown great potential for the Congestion Assistant to reduce the impact of congestion, even at low penetration. However, these studies assumed complete and instantaneous availability of information regarding position and velocity of vehicles ahead. In this paper, we introduce a system where vehicles collaboratively build a so-called TrafficMap, providing over-the-horizon awareness. The idea is that this TrafficMap provides highly compressed information that is both essential and sufficient for the Congestion Assistant to operate. Moreover, this TrafficMap can be built in a distributed way, where only a limited subset of the vehicles have to alter it and/or forward it in the upstream direction. Initial simulation experiments show that our proposed system provides vehicles with a highly compressed view of the traffic ahead with only limited communication
Spatial networks with wireless applications
Many networks have nodes located in physical space, with links more common
between closely spaced pairs of nodes. For example, the nodes could be wireless
devices and links communication channels in a wireless mesh network. We
describe recent work involving such networks, considering effects due to the
geometry (convex,non-convex, and fractal), node distribution,
distance-dependent link probability, mobility, directivity and interference.Comment: Review article- an amended version with a new title from the origina
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