Generating Mimo Channels for 6G Virtual Worlds Using Ray-Tracing Simulations

@article{Klautau2021GeneratingMC,
  title={Generating Mimo Channels for 6G Virtual Worlds Using Ray-Tracing Simulations},
  author={A. Klautau and Ailton de Oliveira and Isabela Pamplona-Trindade and Wesin Alves},
  journal={2021 IEEE Statistical Signal Processing Workshop (SSP)},
  year={2021},
  pages={595-599}
}
  • A. Klautau, Ailton de Oliveira, +1 author Wesin Alves
  • Published 2021
  • Computer Science, Engineering
  • 2021 IEEE Statistical Signal Processing Workshop (SSP)
Some 6G use cases include augmented reality and high-fidelity holograms, with this information flowing through the network. Hence, it is expected that 6G systems can feed machine learning algorithms with such context information to optimize communication performance. This paper focuses on the simulation of 6G MIMO systems that rely on a 3-D representation of the environment as captured by cameras and eventually other sensors. We present new and improved Raymobtime datasets, which consist of… Expand

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References

SHOWING 1-10 OF 21 REFERENCES
Accuracy Analysis of the Geometrical Approximation of MIMO Channels Using Ray-Tracing
TLDR
This work obtains SISO channel responses, and then uses the geometric channel model to generate the full MIMO channel for uniform linear or planar antenna arrays, and analyses the impact of this approximation in the estimated channel capacity and NMSE. Expand
Deep Transfer Learning for Site-Specific Channel Estimation in Low-Resolution mmWave MIMO
We consider the problem of channel estimation in low-resolution multiple-input multiple-output (MIMO) systems operating at millimeter wave (mmWave) and present a deep transfer learning (DTL) approachExpand
5G MIMO Data for Machine Learning: Application to Beam-Selection Using Deep Learning
TLDR
A methodology is presented that combines a vehicle traffic simulator with a ray-tracing simulator, to generate channel realizations representing 5G scenarios with mobility of both transceivers and objects to investigate beam-selection techniques on vehicle-to-infrastructure using millimeter waves. Expand
A General 3-D Non-Stationary 5G Wireless Channel Model
A novel unified framework of geometry-based stochastic models for the fifth generation (5G) wireless communication systems is proposed in this paper. The proposed general 5G channel model aims atExpand
A General 3D Non-Stationary Wireless Channel Model for 5G and Beyond
TLDR
The proposed B5G channel model (B5GCM) is designed to capture various channel characteristics in (B)5G systems such as space-time-frequency (STF) non-stationarity, spherical wavefront (SWF), high delay resolution, time-variant velocities and directions of motion of the transmitter, receiver, and scatterers, spatial consistency, etc. Expand
Deconstructing multiantenna fading channels
  • A. Sayeed
  • Computer Science
  • IEEE Trans. Signal Process.
  • 2002
TLDR
This work proposes an intermediate virtual channel representation that captures the essence of physical modeling and provides a simple geometric interpretation of the scattering environment and shows that in an uncorrelated scattering environment, the elements of the channel matrix form a segment of a stationary process and that the virtual channel coefficients are approximately uncor related samples of the underlying spectral representation. Expand
Channel Estimation for One-Bit Multiuser Massive MIMO Using Conditional GAN
TLDR
A conditional generative adversarial networks (cGAN) is developed to predict more realistic channels by adversarially training two DL networks and achieves high robustness in massive MIMO systems. Expand
6G Wireless Channel Measurements and Models: Trends and Challenges
TLDR
This article comprehensively survey 6G wireless channel measurements, characteristics, and models for all frequency bands and all scenarios, focusing on millimeter-wave, terahertz, and optical wireless communication channels under all spectra. Expand
Deep Learning for Massive MIMO With 1-Bit ADCs: When More Antennas Need Fewer Pilots
TLDR
This letter considers uplink massive MIMO systems with 1-bit analog-to-digital converters (ADCs) and develops a deep-learning based channel estimation framework and observation that when more base-station antennas are employed, the proposed deep learning approach achieves better channel estimation performance, for the same pilot sequence length. Expand
Channel Estimation for mmWave Massive MIMO With Convolutional Blind Denoising Network
TLDR
Numerical results demonstrate that the proposed CBDNet-based channel estimator can outperform the traditional channel estimators, traditional compressive sensing techniques and deep CNNs in terms of the normalized mean squared error. Expand
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