98 research outputs found
Sparsity in the Delay-Doppler Domain for Measured 60 GHz Vehicle-to-Infrastructure Communication Channels
We report results from millimeter wave vehicle-to-infrastructure (V2I)
channel measurements conducted on Sept. 25, 2018 in an urban street
environment, down-town Vienna, Austria. Measurements of a frequency-division
multiplexed multiple-input single-output channel have been acquired with a
time-domain channel sounder at 60 GHz with a bandwidth of 100 MHz and a
frequency resolution of 5 MHz. Two horn antennas were used on a moving
transmitter vehicle: one horn emitted a beam towards the horizon and the second
horn emitted an elevated beam at 15-degrees up-tilt. This configuration was
chosen to assess the impact of beam elevation on V2I communication channel
characteristics: propagation loss and sparsity of the local scattering function
in the delay-Doppler domain. The measurement results within urban speed limits
show high sparsity in the delay-Doppler domain.Comment: submitted to IEEE International Conference on Communication
Stationarity analysis of V2I radio channel in a suburban environment
Due to rapid changes in the environment, vehicular communication channels no longer satisfy the assumption of wide-sense stationary uncorrelated scattering. The non-stationary fading process can be characterized by assuming local stationarity regionswith finite extent in time and frequency. The local scattering function (LSF) and channel correlation function (CCF) provide a framework to characterize the mean power and correlation of the non-stationary channel scatterers, respectively. In this paper, we estimate the LSF and CCF from measurements collected in a vehicle-to-infrastructure radio channel sounding campaign in a suburban environment in Lille, France. Based on the CCF, the stationarity region is evaluated in time as 567 ms and used to capture the non-stationary fading parameters. We obtain the time-varying delay and Doppler power profiles fromthe LSF, and we analyze the corresponding root-mean-square delay and Doppler spreads. We show that the distribution of these parameters follows a lognormal model. Finally, application relevance in terms of channel capacity and diversity techniques is discussed. Results show that the assumption of ergodic capacity and the performance of various diversity techniques depend on the stationarity and coherence parameters of the channel. The evaluation and statistical modeling of such parameters can provide away of tracking channel variation, hence, increasing the performance of adaptive schemes
6G Wireless Systems: Vision, Requirements, Challenges, Insights, and Opportunities
Mobile communications have been undergoing a generational change every ten
years or so. However, the time difference between the so-called "G's" is also
decreasing. While fifth-generation (5G) systems are becoming a commercial
reality, there is already significant interest in systems beyond 5G, which we
refer to as the sixth-generation (6G) of wireless systems. In contrast to the
already published papers on the topic, we take a top-down approach to 6G. We
present a holistic discussion of 6G systems beginning with lifestyle and
societal changes driving the need for next generation networks. This is
followed by a discussion into the technical requirements needed to enable 6G
applications, based on which we dissect key challenges, as well as
possibilities for practically realizable system solutions across all layers of
the Open Systems Interconnection stack. Since many of the 6G applications will
need access to an order-of-magnitude more spectrum, utilization of frequencies
between 100 GHz and 1 THz becomes of paramount importance. As such, the 6G
eco-system will feature a diverse range of frequency bands, ranging from below
6 GHz up to 1 THz. We comprehensively characterize the limitations that must be
overcome to realize working systems in these bands; and provide a unique
perspective on the physical, as well as higher layer challenges relating to the
design of next generation core networks, new modulation and coding methods,
novel multiple access techniques, antenna arrays, wave propagation,
radio-frequency transceiver design, as well as real-time signal processing. We
rigorously discuss the fundamental changes required in the core networks of the
future that serves as a major source of latency for time-sensitive
applications. While evaluating the strengths and weaknesses of key 6G
technologies, we differentiate what may be achievable over the next decade,
relative to what is possible.Comment: Accepted for Publication into the Proceedings of the IEEE; 32 pages,
10 figures, 5 table
Enabling AI in Future Wireless Networks: A Data Life Cycle Perspective
Recent years have seen rapid deployment of mobile computing and Internet of
Things (IoT) networks, which can be mostly attributed to the increasing
communication and sensing capabilities of wireless systems. Big data analysis,
pervasive computing, and eventually artificial intelligence (AI) are envisaged
to be deployed on top of the IoT and create a new world featured by data-driven
AI. In this context, a novel paradigm of merging AI and wireless
communications, called Wireless AI that pushes AI frontiers to the network
edge, is widely regarded as a key enabler for future intelligent network
evolution. To this end, we present a comprehensive survey of the latest studies
in wireless AI from the data-driven perspective. Specifically, we first propose
a novel Wireless AI architecture that covers five key data-driven AI themes in
wireless networks, including Sensing AI, Network Device AI, Access AI, User
Device AI and Data-provenance AI. Then, for each data-driven AI theme, we
present an overview on the use of AI approaches to solve the emerging
data-related problems and show how AI can empower wireless network
functionalities. Particularly, compared to the other related survey papers, we
provide an in-depth discussion on the Wireless AI applications in various
data-driven domains wherein AI proves extremely useful for wireless network
design and optimization. Finally, research challenges and future visions are
also discussed to spur further research in this promising area.Comment: Accepted at the IEEE Communications Surveys & Tutorials, 42 page
Abstracts on Radio Direction Finding (1899 - 1995)
The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography).
Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM.
The contents of these files are:
1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format];
2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format];
3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion
PHY-layer Security in Cognitive Radio Networks through Learning Deep Generative Models: an AI-based approach
PhD ThesisRecently, Cognitive Radio (CR) has been intended as an intelligent radio endowed with
cognition which can be developed by implementing Artificial Intelligence (AI) techniques.
Specifically, data-driven Self-Awareness (SA) functionalities, such as detection of spectrum
abnormalities, can be effectively implemented as shown by the proposed research. One important
application is PHY-layer security since it is essential to establish secure wireless communications
against external jamming attacks.
In this framework, signals are non-stationary and features from such kind of dynamic
spectrum, with multiple high sampling rate signals, are then extracted through the Stockwell
Transform (ST) with dual-resolution which has been proposed and validated in this work as
part of spectrum sensing techniques.
Afterwards, analysis of the state-of-the-art about learning dynamic models from observed
features describes theoretical aspects of Machine Learning (ML). In particular, following the
recent advances of ML, learning deep generative models with several layers of non-linear
processing has been selected as AI method for the proposed spectrum abnormality detection
in CR for a brain-inspired, data-driven SA.
In the proposed approach, the features extracted from the ST representation of the wideband
spectrum are organized in a high-dimensional generalized state vector and, then, a generative
model is learned and employed to detect any deviation from normal situations in the analysed
spectrum (abnormal signals or behaviours). Specifically, conditional GAN (C-GAN),
auxiliary classifier GAN (AC-GAN), and deep VAE have been considered as deep generative
models.
A dataset of a dynamic spectrum with multi-OFDM signals has been generated by using
the National Instruments mm-Wave Transceiver which operates at 28 GHz (central carrier frequency)
with 800 MHz frequency range. Training of the deep generative model is performed
on the generalized state vector representing the mmWave spectrum with normality pattern
without any malicious activity. Testing is based on new and independent data samples corresponding
to abnormality pattern where the moving signal follows a different behaviour which
has not been observed during training.
An abnormality indicator is measured and used for the binary classification (normality hypothesis
otherwise abnormality hypothesis), while the performance of the generative models
is evaluated and compared through ROC curves and accuracy metrics
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