MIMO-GAN: Generative MIMO Channel Modeling

• URL: http://arxiv.org/abs/2203.08588v1
• Date: Wed, 16 Mar 2022 12:36:38 GMT
• Title: MIMO-GAN: Generative MIMO Channel Modeling
• Authors: Tribhuvanesh Orekondy, Arash Behboodi, Joseph B. Soriaga
• Abstract summary: We propose generative channel modeling to learn statistical channel models from channel input-output measurements. We leverage advances in GAN, which helps us learn an implicit distribution over channels from observed measurements.
• Score: 13.277946558463201
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• How Critical is Site-Specific RAN Optimization? 5G Open-RAN Uplink Air Interface Performance Test and Optimization from Macro-Cell CIR Data [0.6753334733130354]
  We consider the importance of channel measurement data from specific sites and its impact on air interface optimization and test. We leverage our OmniPHY-5G neural receiver for NR PUSCH uplink simulation, with a training procedure that uses statistical TDL channel models for pre-training. The proposed fine-tuning method achieves a 10% block error rate (BLER) at a 1.85 dB lower signal-to-noise ratio (SNR) compared to pre-training.
  arXiv  Detail & Related papers  (2024-10-25T13:57:48Z)
• SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion [59.96233305733875]
  Time series forecasting plays a crucial role in various fields such as finance, traffic management, energy, and healthcare. Several methods utilize mechanisms like attention or mixer to address this by capturing channel correlations. This paper presents an efficient-based model, the Series-cOre Fused Time Series forecaster (SOFTS)
  arXiv  Detail & Related papers  (2024-04-22T14:06:35Z)
• Diffusion Models for Accurate Channel Distribution Generation [19.80498913496519]
  Strong generative models can accurately learn channel distributions. This could save recurring costs for physical measurements of the channel. The resulting differentiable channel model supports training neural encoders by enabling gradient-based optimization.
  arXiv  Detail & Related papers  (2023-09-19T10:35:54Z)
• Generative Diffusion Models for Radio Wireless Channel Modelling and Sampling [11.09458914721516]
  The complexity of channel modelling and the cost of collecting high-quality wireless channel data have become major challenges. We propose a diffusion model based channel sampling approach for rapidly realizations from limited data. We show that, compared to existing GAN based approaches which suffer from mode collapse and unstable training, our diffusion based approach trains synthesizingly and generates diverse and high-fidelity samples.
  arXiv  Detail & Related papers  (2023-08-10T13:49:26Z)
• Diffusion with Forward Models: Solving Stochastic Inverse Problems Without Direct Supervision [76.32860119056964]
  We propose a novel class of denoising diffusion probabilistic models that learn to sample from distributions of signals that are never directly observed. We demonstrate the effectiveness of our method on three challenging computer vision tasks.
  arXiv  Detail & Related papers  (2023-06-20T17:53:00Z)
• Fast Sampling of Diffusion Models via Operator Learning [74.37531458470086]
  We use neural operators, an efficient method to solve the probability flow differential equations, to accelerate the sampling process of diffusion models. Compared to other fast sampling methods that have a sequential nature, we are the first to propose a parallel decoding method. We show our method achieves state-of-the-art FID of 3.78 for CIFAR-10 and 7.83 for ImageNet-64 in the one-model-evaluation setting.
  arXiv  Detail & Related papers  (2022-11-24T07:30:27Z)
• Random Orthogonalization for Federated Learning in Massive MIMO Systems [85.71432283670114]
  We propose a novel communication design for federated learning (FL) in a massive multiple-input and multiple-output (MIMO) wireless system. Key novelty of random orthogonalization comes from the tight coupling of FL and two unique characteristics of massive MIMO -- channel hardening and favorable propagation. We extend this principle to the downlink communication phase and develop a simple but highly effective model broadcast method for FL.
  arXiv  Detail & Related papers  (2022-10-18T14:17:10Z)
• MIMO Channel Estimation using Score-Based Generative Models [1.6752182911522517]
  We introduce a novel approach for channel estimation using deep score-based generative models. These models are trained to estimate the gradient of the log-prior distribution, and can be used to iteratively refine estimates, given observed measurements of a signal.
  arXiv  Detail & Related papers  (2022-04-14T17:23:58Z)
• Deep Generative Models for Downlink Channel Estimation in FDD Massive MIMO Systems [13.267048706241157]
  We propose a deep generative model (DGM)-based technique to address this challenge. Exploiting the partial reciprocity of uplink and downlink channels, we first estimate the frequency-independent underlying channel parameters. Then, the frequency-specific underlying channel parameters, namely, the phase of each propagation path, are estimated via downlink training.
  arXiv  Detail & Related papers  (2022-03-09T18:32:10Z)
• Learning to Perform Downlink Channel Estimation in Massive MIMO Systems [72.76968022465469]
  We study downlink (DL) channel estimation in a Massive multiple-input multiple-output (MIMO) system. A common approach is to use the mean value as the estimate, motivated by channel hardening. We propose two novel estimation methods.
  arXiv  Detail & Related papers  (2021-09-06T13:42:32Z)
• Model-Driven Deep Learning Based Channel Estimation and Feedback for Millimeter-Wave Massive Hybrid MIMO Systems [61.78590389147475]
  This paper proposes a model-driven deep learning (MDDL)-based channel estimation and feedback scheme for millimeter-wave (mmWave) systems. To reduce the uplink pilot overhead for estimating the high-dimensional channels from a limited number of radio frequency (RF) chains, we propose to jointly train the phase shift network and the channel estimator as an auto-encoder. Numerical results show that the proposed MDDL-based channel estimation and feedback scheme outperforms the state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-04-22T13:34:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.