741 research outputs found
Generative Adversarial Estimation of Channel Covariance in Vehicular Millimeter Wave Systems
Enabling highly-mobile millimeter wave (mmWave) systems is challenging
because of the huge training overhead associated with acquiring the channel
knowledge or designing the narrow beams. Current mmWave beam training and
channel estimation techniques do not normally make use of the prior beam
training or channel estimation observations. Intuitively, though, the channel
matrices are functions of the various elements of the environment. Learning
these functions can dramatically reduce the training overhead needed to obtain
the channel knowledge. In this paper, a novel solution that exploits machine
learning tools, namely conditional generative adversarial networks (GAN), is
developed to learn these functions between the environment and the channel
covariance matrices. More specifically, the proposed machine learning model
treats the covariance matrices as 2D images and learns the mapping function
relating the uplink received pilots, which act as RF signatures of the
environment, and these images. Simulation results show that the developed
strategy efficiently predicts the covariance matrices of the large-dimensional
mmWave channels with negligible training overhead.Comment: to appear in Asilomar Conference on Signals, Systems, and Computers,
Oct. 201
RADIANCE: Radio-Frequency Adversarial Deep-learning Inference for Automated Network Coverage Estimation
Radio-frequency coverage maps (RF maps) are extensively utilized in wireless
networks for capacity planning, placement of access points and base stations,
localization, and coverage estimation. Conducting site surveys to obtain RF
maps is labor-intensive and sometimes not feasible. In this paper, we propose
radio-frequency adversarial deep-learning inference for automated network
coverage estimation (RADIANCE), a generative adversarial network (GAN) based
approach for synthesizing RF maps in indoor scenarios. RADIANCE utilizes a
semantic map, a high-level representation of the indoor environment to encode
spatial relationships and attributes of objects within the environment and
guide the RF map generation process. We introduce a new gradient-based loss
function that computes the magnitude and direction of change in received signal
strength (RSS) values from a point within the environment. RADIANCE
incorporates this loss function along with the antenna pattern to capture
signal propagation within a given indoor configuration and generate new
patterns under new configuration, antenna (beam) pattern, and center frequency.
Extensive simulations are conducted to compare RADIANCE with ray-tracing
simulations of RF maps. Our results show that RADIANCE achieves a mean average
error (MAE) of 0.09, root-mean-squared error (RMSE) of 0.29, peak
signal-to-noise ratio (PSNR) of 10.78, and multi-scale structural similarity
index (MS-SSIM) of 0.80.Comment: 6 pages, 6 figure
Physical-Layer Authentication Using Channel State Information and Machine Learning
Strong authentication in an interconnected wireless environment continues to
be an important, but sometimes elusive goal. Research in physical-layer
authentication using channel features holds promise as a technique to improve
network security for a variety of devices. We propose the use of machine
learning and measured multiple-input multiple-output communications channel
information to make a decision on whether or not to authenticate a particular
device. This work analyzes the use of received channel state information from
the wireless environment and demonstrates the employment of a generative
adversarial neural network (GAN) trained with received channel data to
authenticate a transmitting device. We compared a variety of machine learning
techniques and found that the local outlier factor (LOF) algorithm reached 100%
accuracy at lower signal to noise ratios (SNR) than other algorithms. However,
before LOF reached 100%, we also show that the GAN was more accurate at lower
SNR levels.Comment: Submitted to 14th International Conference on Signal Processing and
Communication Systems (ICSPCS) 202
A Holistic Investigation on Terahertz Propagation and Channel Modeling Toward Vertical Heterogeneous Networks
User-centric and low latency communications can be enabled not only by small
cells but also through ubiquitous connectivity. Recently, the vertical
heterogeneous network (V-HetNet) architecture is proposed to backhaul/fronthaul
a large number of small cells. Like an orchestra, the V-HetNet is a polyphony
of different communication ensembles, including geostationary orbit (GEO), and
low-earth orbit (LEO) satellites (e.g., CubeSats), and networked flying
platforms (NFPs) along with terrestrial communication links. In this study, we
propose the Terahertz (THz) communications to enable the elements of V-HetNets
to function in harmony. As THz links offer a large bandwidth, leading to
ultra-high data rates, it is suitable for backhauling and fronthauling small
cells. Furthermore, THz communications can support numerous applications from
inter-satellite links to in-vivo nanonetworks. However, to savor this harmony,
we need accurate channel models. In this paper, the insights obtained through
our measurement campaigns are highlighted, to reveal the true potential of THz
communications in V-HetNets.Comment: It has been accepted for the publication in IEEE Communications
Magazin
- …