mlsanityData to 7 Oct 2026

MIMO-GAN: Generative MIMO Channel Modeling

arXiv 2203.08588 ↗First posted 16 Mar 2022cs.IT cs.AI cs.LGcited by 9

Not on the map. Why?

Abstract (arXiv)

We propose generative channel modeling to learn statistical channel models from channel input-output measurements. Generative channel models can learn more complicated distributions and represent the field data more faithfully. They are tractable and easy to sample from, which can potentially speed up the simulation rounds. To achieve this, we leverage advances in GAN, which helps us learn an implicit distribution over stochastic MIMO channels from observed measurements. In particular, our approach MIMO-GAN implicitly models the wireless channel as a distribution of time-domain band-limited impulse responses. We evaluate MIMO-GAN on 3GPP TDL MIMO channels and observe high-consistency in capturing power, delay and spatial correlation statistics of the underlying channel. In particular, we observe MIMO-GAN achieve errors of under 3.57 ns average delay and -18.7 dB power.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 12 of its 21 references that are in the map. First posted on arXiv 16 Mar 2022.

Builds on

12 of 21 references.

References · 12

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  2. 2013Auto-Encoding Variational Bayescited by 3,980
  3. 2017Improved Training of Wasserstein GANscited by 2,223
  4. 2016Adversarial Feature Learningcited by 350
  5. 2017Compressed Sensing using Generative Modelscited by 337
  6. 2018Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problemcited by 323
  7. 2017An Introduction to Deep Learning for the Physical Layercited by 227
  8. 2017Deep Learning-Based Communication Over the Aircited by 72
  9. 2020Modeling Continuous Stochastic Processes with Dynamic Normalizing Flowscited by 37
  10. 2018Approximating the Void: Learning Stochastic Channel Models from Observation with Variational Generative Adversarial Networkscited by 24
  11. 2018End-to-End Learning of Communications Systems Without a Channel Modelcited by 19
  12. 2020WGAN-based Autoencoder Training Over-the-aircited by 11

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 3; 2024: 1; 2025: 0; 2026: 3.

9 papers.

  1. 2023Generative Diffusion Models for Radio Wireless Channel Modelling and Samplingcited by 15
  2. 2022Robust Bayesian Learning for Reliable Wireless AI: Framework and Applicationscited by 9
  3. 2022Over-the-Air Design of GAN Training for mmWave MIMO Channel Estimationcited by 5
  4. 2023Diffusion Models for Accurate Channel Distribution Generationcited by 5
  5. 2024Differentiable and Learnable Wireless Simulation with Geometric Transformerscited by 5
  6. 2023Transformer-Based Neural Surrogate for Link-Level Path Loss Prediction from Variable-Sized Mapscited by 3
  7. 2026Site-Specific MIMO Channel Generation via Diffusion and Flow Matching: Fidelity, Efficiency, and Downstream Utilitycited by 0
  8. 2026Physics-Aware Conditional SetGAN for Spatially Consistent Multi-User TR 38.901 Channel Generationcited by 0
  9. 2026Generative Models for Modeling and Synthesizing MIMO Channels in Adverse Weather Conditionscited by 0