27,543 research outputs found
6G White Paper on Machine Learning in Wireless Communication Networks
The focus of this white paper is on machine learning (ML) in wireless
communications. 6G wireless communication networks will be the backbone of the
digital transformation of societies by providing ubiquitous, reliable, and
near-instant wireless connectivity for humans and machines. Recent advances in
ML research has led enable a wide range of novel technologies such as
self-driving vehicles and voice assistants. Such innovation is possible as a
result of the availability of advanced ML models, large datasets, and high
computational power. On the other hand, the ever-increasing demand for
connectivity will require a lot of innovation in 6G wireless networks, and ML
tools will play a major role in solving problems in the wireless domain. In
this paper, we provide an overview of the vision of how ML will impact the
wireless communication systems. We first give an overview of the ML methods
that have the highest potential to be used in wireless networks. Then, we
discuss the problems that can be solved by using ML in various layers of the
network such as the physical layer, medium access layer, and application layer.
Zero-touch optimization of wireless networks using ML is another interesting
aspect that is discussed in this paper. Finally, at the end of each section,
important research questions that the section aims to answer are presented
A Review of the Enviro-Net Project
Ecosystems monitoring is essential to properly understand their development
and the effects of events, both climatological and anthropological in nature.
The amount of data used in these assessments is increasing at very high rates.
This is due to increasing availability of sensing systems and the development
of new techniques to analyze sensor data. The Enviro-Net Project encompasses
several of such sensor system deployments across five countries in the
Americas. These deployments use a few different ground-based sensor systems,
installed at different heights monitoring the conditions in tropical dry
forests over long periods of time. This paper presents our experience in
deploying and maintaining these systems, retrieving and pre-processing the
data, and describes the Web portal developed to help with data management,
visualization and analysis.Comment: v2: 29 pages, 5 figures, reflects changes addressing reviewers'
comments v1: 38 pages, 8 figure
Will SDN be part of 5G?
For many, this is no longer a valid question and the case is considered
settled with SDN/NFV (Software Defined Networking/Network Function
Virtualization) providing the inevitable innovation enablers solving many
outstanding management issues regarding 5G. However, given the monumental task
of softwarization of radio access network (RAN) while 5G is just around the
corner and some companies have started unveiling their 5G equipment already,
the concern is very realistic that we may only see some point solutions
involving SDN technology instead of a fully SDN-enabled RAN. This survey paper
identifies all important obstacles in the way and looks at the state of the art
of the relevant solutions. This survey is different from the previous surveys
on SDN-based RAN as it focuses on the salient problems and discusses solutions
proposed within and outside SDN literature. Our main focus is on fronthaul,
backward compatibility, supposedly disruptive nature of SDN deployment,
business cases and monetization of SDN related upgrades, latency of general
purpose processors (GPP), and additional security vulnerabilities,
softwarization brings along to the RAN. We have also provided a summary of the
architectural developments in SDN-based RAN landscape as not all work can be
covered under the focused issues. This paper provides a comprehensive survey on
the state of the art of SDN-based RAN and clearly points out the gaps in the
technology.Comment: 33 pages, 10 figure
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