6,034 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
Feature selection for chemical sensor arrays using mutual information
We address the problem of feature selection for classifying a diverse set of chemicals using an array of metal oxide sensors. Our aim is to evaluate a filter approach to feature selection with reference to previous work, which used a wrapper approach on the same data set, and established best features and upper bounds on classification performance. We selected feature sets that exhibit the maximal mutual information with the identity of the chemicals. The selected features closely match those found to perform well in the previous study using a wrapper approach to conduct an exhaustive search of all permitted feature combinations. By comparing the classification performance of support vector machines (using features selected by mutual information) with the performance observed in the previous study, we found that while our approach does not always give the maximum possible classification performance, it always selects features that achieve classification performance approaching the optimum obtained by exhaustive search. We performed further classification using the selected feature set with some common classifiers and found that, for the selected features, Bayesian Networks gave the best performance. Finally, we compared the observed classification performances with the performance of classifiers using randomly selected features. We found that the selected features consistently outperformed randomly selected features for all tested classifiers. The mutual information filter approach is therefore a computationally efficient method for selecting near optimal features for chemical sensor arrays
Sixth Generation (6G)Wireless Networks: Vision, Research Activities, Challenges and Potential Solutions
The standardization activities of the fifth generation communications are clearly over
and deployment has commenced globally. To sustain the competitive edge of wireless networks,
industrial and academia synergy have begun to conceptualize the next generation of wireless
communication systems (namely, sixth generation, (6G)) aimed at laying the foundation for the
stratification of the communication needs of the 2030s. In support of this vision, this study highlights
the most promising lines of research from the recent literature in common directions for the
6G project. Its core contribution involves exploring the critical issues and key potential features of
6G communications, including: (i) vision and key features; (ii) challenges and potential solutions;
and (iii) research activities. These controversial research topics were profoundly examined in relation
to the motivation of their various sub-domains to achieve a precise, concrete, and concise conclusion.
Thus, this article will contribute significantly to opening new horizons for future research direction
Digital Twins for Industry 4.0 in the 6G Era
Having the Fifth Generation (5G) mobile communication system recently rolled
out in many countries, the wireless community is now setting its eyes on the
next era of Sixth Generation (6G). Inheriting from 5G its focus on industrial
use cases, 6G is envisaged to become the infrastructural backbone of future
intelligent industry. Especially, a combination of 6G and the emerging
technologies of Digital Twins (DT) will give impetus to the next evolution of
Industry 4.0 (I4.0) systems. This article provides a survey in the research
area of 6G-empowered industrial DT system. With a novel vision of 6G industrial
DT ecosystem, this survey discusses the ambitions and potential applications of
industrial DT in the 6G era, identifying the emerging challenges as well as the
key enabling technologies. The introduced ecosystem is supposed to bridge the
gaps between humans, machines, and the data infrastructure, and therewith
enable numerous novel application scenarios.Comment: Accepted for publication in IEEE Open Journal of Vehicular Technolog
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