60 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
Channel Power Gain Estimation for Terahertz Vehicle-to-infrastructure Networks
The use of terahertz (THz) frequencies has been recommended to achieve high-speed and ultra-low latency transmissions. Although there exist very large bandwidths in the THz frequency bands, THz channels are significantly dynamic and complicated, which is challenging for channel estimation. To improve the energy efficiency of wireless networks, THz channel power gains need to be precisely evaluated for determining optimal THz transmission frequencies and power control. Therefore, this work presents a novel conditional generative adversarial networks (GAN) based channel power gain estimation solution in the THz vehicle-to-infrastructure (V2I) networks with leaky-wave antennas, where the THz frequency has a big effect on the antenna gain, path loss and atmospheric attenuation. Simulation results confirm that our solution can accurately estimate the channel power gains versus the THz frequencies at a fast speed.acceptedVersionPeer reviewe
Deep Learning Based Channel Covariance Matrix Estimation with User Location and Scene Images
Channel covariance matrix (CCM) is one critical parameter for designing the
communications systems. In this paper, a novel framework of the deep learning
(DL) based CCM estimation is proposed that exploits the perception of the
transmission environment without any channel sample or the pilot signals.
Specifically, as CCM is affected by the user's movement, we design a deep
neural network (DNN) to predict CCM from user location and user speed, and the
corresponding estimation method is named as ULCCME. A location denoising method
is further developed to reduce the positioning error and improve the robustness
of ULCCME. For cases when user location information is not available, we
propose an interesting way that uses the environmental 3D images to predict the
CCM, and the corresponding estimation method is named as SICCME. Simulation
results show that both the proposed methods are effective and will benefit the
subsequent channel estimation.Comment: 30 pages, 18 figure
Unsupervised learning based fast beamforming design for downlink MIMO
In the downlink transmission scenario, power allocation and beamforming design at the transmitter are essential when using multiple antenna arrays. This paper considers a multiple input-multiple output broadcast channel to maximize the weighted sum-rate under the total power constraint. The classical weighted minimum mean-square error (WMMSE) algorithm can obtain suboptimal solutions but involves high computational complexity. To reduce this complexity, we propose a fast beamforming design method using unsupervised learning, which trains the deep neural network (DNN) offline and provides real-time service online only with simple neural network operations. The training process is based on an end-to-end method without labeled samples avoiding the complicated process of obtaining labels. Moreover, we use the ’APoZ’-based pruning algorithm to compress the network volume, which further reduces the computational complexity and volume of the DNN, making it more suitable for low computation-capacity devices. Finally, experimental results demonstrate that the proposed method improves computational speed significantly with performance close to the WMMSE algorithm
Inductive Matrix Completion and Root-MUSIC-Based Channel Estimation for Intelligent Reflecting Surface (IRS)-Aided Hybrid MIMO Systems
This paper studies the estimation of cascaded channels in passive intelligent
reflective surface (IRS)- aided multiple-input multiple-output (MIMO) systems
employing hybrid precoders and combiners. We propose a low-complexity solution
that estimates the channel parameters progressively. The angles of departure
(AoDs) and angles of arrival (AoAs) at the transmitter and receiver,
respectively, are first estimated using inductive matrix completion (IMC)
followed by root-MUSIC based super-resolution spectrum estimation.
Forward-backward spatial smoothing (FBSS) is applied to address the coherence
issue. Using the estimated AoAs and AoDs, the training precoders and combiners
are then optimized and the angle differences between the AoAs and AoDs at the
IRS are estimated using the least squares (LS) method followed by FBSS and the
root-MUSIC algorithm. Finally, the composite path gains of the cascaded channel
are estimated using on-grid sparse recovery with a small-size dictionary. The
simulation results suggest that the proposed estimator can achieve improved
channel parameter estimation performance with lower complexity as compared to
several recently reported alternatives, thanks to the exploitation of the
knowledge of the array responses and low-rankness of the channel using
low-complexity algorithms at all the stages.Comment: Submitted to IEE
Multi-subcarrier Physical Layer Authentication Using Channel State Information and Deep Learning
17 USC 105 interim-entered record; under temporary embargo.Strong authentication is crucial as wireless networks become more widespread and relied upon. The robust physical layer features produced by advanced communication networks lend themselves to accomplishing physical layer authentication by using channel state information (CSI). The use of deep learning with neural networks is well suited for classification tasks and can further the goal of enhancing physical layer security. To that end, we propose a semi-supervised generative adversarial network to differentiate between legitimate and malicious transmitters and accurately identify devices for authentication across a range of signal to noise ratio conditions. Our system leverages multiple input multiple output CSI across orthogonal frequency division multiplexing subcarriers using a small percentage of labeled training data.U.S. Government affiliation is unstated in article text
Multi-subcarrier Physical Layer Authentication Using Channel State Information and Deep Learning
Strong authentication is crucial as wireless networks become more widespread and relied upon. The robust physical layer features produced by advanced communication networks lend themselves to accomplishing physical layer authentication by using channel state information (CSI). The use of deep learning with neural networks is well suited for classification tasks and can further the goal of enhancing physical layer security. To that end, we propose a semi-supervised generative adversarial network to differentiate between legitimate and malicious transmitters and accurately identify devices for authentication across a range of signal to noise ratio conditions. Our system leverages multiple input multiple output CSI across orthogonal frequency division multiplexing subcarriers using a small percentage of labeled training data
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