The Experts below are selected from a list of 146229 Experts worldwide ranked by ideXlab platform
Henk Wymeersch - One of the best experts on this subject based on the ideXlab platform.
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Spatial Wireless Channel Prediction under Location Uncertainty
IEEE Transactions on Wireless Communications, 2016Co-Authors: Srikar L. Muppirisetty, Tommy Svensson, Henk WymeerschAbstract:Spatial Wireless Channel prediction is important for future Wireless networks, and in particular, for proactive resource allocation at different layers of the protocol stack. Various sources of uncertainty must be accounted for during modeling and to provide robust predictions. We investigate two Channel prediction frameworks, classical Gaussian processes (cGP), and uncertain Gaussian processes (uGP), and analyze the impact of location uncertainty during learning/training and prediction/testing, for scenarios where measurements uncertainty are dominated by large-scale fading. We observe that cGP generally fails both in terms of learning the Channel parameters and in predicting the Channel in the presence of location uncertainties. In contrast, uGP explicitly considers the location uncertainty. Using simulated data, we show that uGP is able to learn and predict the Wireless Channel.
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spatial Wireless Channel prediction under location uncertainty
arXiv: Information Theory, 2015Co-Authors: Srikar L. Muppirisetty, Tommy Svensson, Henk WymeerschAbstract:Spatial Wireless Channel prediction is important for future Wireless networks, and in particular for proactive resource allocation at different layers of the protocol stack. Various sources of uncertainty must be accounted for during modeling and to provide robust predictions. We investigate two Channel prediction frameworks, classical Gaussian processes (cGP) and uncertain Gaussian processes (uGP), and analyze the impact of location uncertainty during learning/training and prediction/testing, for scenarios where measurements uncertainty are dominated by large-scale fading. We observe that cGP generally fails both in terms of learning the Channel parameters and in predicting the Channel in the presence of location uncertainties.\textcolor{blue}{{} }In contrast, uGP explicitly considers the location uncertainty. Using simulated data, we show that uGP is able to learn and predict the Wireless Channel.
Srikar L. Muppirisetty - One of the best experts on this subject based on the ideXlab platform.
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Spatial Wireless Channel Prediction under Location Uncertainty
IEEE Transactions on Wireless Communications, 2016Co-Authors: Srikar L. Muppirisetty, Tommy Svensson, Henk WymeerschAbstract:Spatial Wireless Channel prediction is important for future Wireless networks, and in particular, for proactive resource allocation at different layers of the protocol stack. Various sources of uncertainty must be accounted for during modeling and to provide robust predictions. We investigate two Channel prediction frameworks, classical Gaussian processes (cGP), and uncertain Gaussian processes (uGP), and analyze the impact of location uncertainty during learning/training and prediction/testing, for scenarios where measurements uncertainty are dominated by large-scale fading. We observe that cGP generally fails both in terms of learning the Channel parameters and in predicting the Channel in the presence of location uncertainties. In contrast, uGP explicitly considers the location uncertainty. Using simulated data, we show that uGP is able to learn and predict the Wireless Channel.
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spatial Wireless Channel prediction under location uncertainty
arXiv: Information Theory, 2015Co-Authors: Srikar L. Muppirisetty, Tommy Svensson, Henk WymeerschAbstract:Spatial Wireless Channel prediction is important for future Wireless networks, and in particular for proactive resource allocation at different layers of the protocol stack. Various sources of uncertainty must be accounted for during modeling and to provide robust predictions. We investigate two Channel prediction frameworks, classical Gaussian processes (cGP) and uncertain Gaussian processes (uGP), and analyze the impact of location uncertainty during learning/training and prediction/testing, for scenarios where measurements uncertainty are dominated by large-scale fading. We observe that cGP generally fails both in terms of learning the Channel parameters and in predicting the Channel in the presence of location uncertainties.\textcolor{blue}{{} }In contrast, uGP explicitly considers the location uncertainty. Using simulated data, we show that uGP is able to learn and predict the Wireless Channel.
Tommy Svensson - One of the best experts on this subject based on the ideXlab platform.
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Spatial Wireless Channel Prediction under Location Uncertainty
IEEE Transactions on Wireless Communications, 2016Co-Authors: Srikar L. Muppirisetty, Tommy Svensson, Henk WymeerschAbstract:Spatial Wireless Channel prediction is important for future Wireless networks, and in particular, for proactive resource allocation at different layers of the protocol stack. Various sources of uncertainty must be accounted for during modeling and to provide robust predictions. We investigate two Channel prediction frameworks, classical Gaussian processes (cGP), and uncertain Gaussian processes (uGP), and analyze the impact of location uncertainty during learning/training and prediction/testing, for scenarios where measurements uncertainty are dominated by large-scale fading. We observe that cGP generally fails both in terms of learning the Channel parameters and in predicting the Channel in the presence of location uncertainties. In contrast, uGP explicitly considers the location uncertainty. Using simulated data, we show that uGP is able to learn and predict the Wireless Channel.
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spatial Wireless Channel prediction under location uncertainty
arXiv: Information Theory, 2015Co-Authors: Srikar L. Muppirisetty, Tommy Svensson, Henk WymeerschAbstract:Spatial Wireless Channel prediction is important for future Wireless networks, and in particular for proactive resource allocation at different layers of the protocol stack. Various sources of uncertainty must be accounted for during modeling and to provide robust predictions. We investigate two Channel prediction frameworks, classical Gaussian processes (cGP) and uncertain Gaussian processes (uGP), and analyze the impact of location uncertainty during learning/training and prediction/testing, for scenarios where measurements uncertainty are dominated by large-scale fading. We observe that cGP generally fails both in terms of learning the Channel parameters and in predicting the Channel in the presence of location uncertainties.\textcolor{blue}{{} }In contrast, uGP explicitly considers the location uncertainty. Using simulated data, we show that uGP is able to learn and predict the Wireless Channel.
M A Jensen - One of the best experts on this subject based on the ideXlab platform.
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secret key establishment using temporally and spatially correlated Wireless Channel coefficients
IEEE Transactions on Mobile Computing, 2011Co-Authors: Chan Chen, M A JensenAbstract:When implementing data encryption and decryption in a symmetric cryptosystem, secure distribution of the secret key to legitimate nodes can be a challenge. In this paper, we consider establishing secret keys using the common Wireless Channel, with particular emphasis on the spatial and temporal correlations of the Channel coefficients. Specifically, we investigate the influence of Channel correlation on the bound of the key size generated from the common Channel using a simple single-input single-output Channel model, and we verify the existence of a sampling approach able to generate a key using the minimum possible sampling window. We also explore decorrelation of the Channel coefficients in a multiple-input multiple-output Channel, and we use a statistical independence test to demonstrate that this process cannot be separated into spatial and temporal decorrelation processes. The insights gained from these studies assist in the development of a practical key generation protocol based on a published Channel coefficient quantization method and incorporating flexible quantization levels, transmission of the correlation eigenvector matrix, and LDPC coding to improve key agreement in an authenticated public Channel. Finally, we present simulations with real Channel measurements that solidify the fundamental conclusions.
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experimental characterization of the mimo Wireless Channel data acquisition and analysis
IEEE Transactions on Wireless Communications, 2003Co-Authors: Jon W Wallace, M A Jensen, A L Swindlehurst, B D JeffsAbstract:Detailed performance assessment of space-time coding algorithms in realistic Channels is critically dependent upon accurate knowledge of the Wireless Channel spatial characteristics. This paper presents an experimental measurement platform capable of providing the narrowband Channel transfer matrix for Wireless communications scenarios. The system is used to directly measure key multiple-input-multiple-output parameters in an indoor environment at 2.45 GHz. Linear antenna arrays of different sizes and construction with up to ten elements at transmit and receive are utilized in the measurement campaign. This data is analyzed to reveal Channel properties such as transfer matrix element statistical distributions and temporal and spatial correlation. Additionally, the impact of parameters such as antenna element polarization, directivity, and array size on Channel capacity are highlighted. The paper concludes with a discussion of the relationship between multipath richness and path loss, as well as their joint role in determining Channel capacity.
Thomas Zemen - One of the best experts on this subject based on the ideXlab platform.
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real time geometry based Wireless Channel emulation
IEEE Transactions on Vehicular Technology, 2019Co-Authors: Markus Hofer, Dimitrios Vlastaras, Bernhard Schrenk, David Loschenbrand, Fredrik Tufvesson, Thomas ZemenAbstract:Connected autonomous vehicles and industry 4.0 production scenarios require ultrareliable low-latency communication links. The varying positions of transmitter, reflecting objects, and receiver cause a nonstationary time- and frequency-selective fading process. In this paper, we present the necessary hardware architecture and signal processing algorithms for a real-time geometry-based Channel emulator, that is needed for testing of Wireless control systems. We partition the nonstationary fading process into a sequence of local stationarity regions and model the Channel impulse response as sum of propagation paths with time-varying attenuation, delay, and Doppler shift. We implement a subspace projection of the propagation path parameters, to compress the time-variant Channel impulse response. This enables a low data-rate link from the host computer, which computes the geometry-based propagation paths, to the software defined radio unit, that implements the convolution on a field programmable gate array (FPGA). With our new architecture, the complexity of the FPGA implementation becomes independent of the number of propagation paths. Our Channel emulator can be parametrized by all known Channel models. Without loss of generality, we use a parameterization by a geometry-based stochastic Channel model, due to its nonstationary nature. We provide Channel impulse response measurements of the Channel emulator, using the RUSK Lund Channel sounder for a vehicular scenario with 617 propagation paths. A comparison of the time-variant power delay profile and Doppler spectral density of simulated and emulated Channel impulse response showed a close match with an error smaller than $-35$ dB. The results demonstrate that our Channel emulator is able to accurately emulate nonstationary fading Channels with continuously changing path delays and Doppler shifts.
