Differentiable and Learnable Wireless Simulation with Geometric Transformers
inverse problems;learning to simulate;wireless channel modeling;geometric deep learning;equivariance;inverse problems;electromagnetic signals;diffusion models
Yes
No ethics review needed.
- Why does data augmentation lead to poorer results in some cases in table 2 for the transformer baseline ?
- Are the input coordinates of the transmitter/receiver 2D or 3D for the proposed model ?
- For Rx interpolation in in-distributions experiments (l. ~329) in table 1, have the floor layouts been seen by the model during training ? If so, this should be explained more clearly, and why this setting is relevant.

Minor remarks:
- l. 297: |ap|^2 -> ap^2 ?
- l. 315: while -> While
- l. 789: The The -> The
- The presentation is clear and the paper is well-written. The problem at hand and coresponding challenges are well introduced to the reader.
- The quantitative and qualitative results show the superiority of the method in multiple settings, and with regards to multiple variables (number of training samples, number of training rooms / transmitters, etc..
- The versatility of the model is underlined by its adaptation to the inverse problem setting.
The paper presents a learnable approach to tackle the problem of indoor wireless simulation. The proposed architecture is based on a Geometric Algebra Transformer, and a new tokenizer is introduced, allowing to leverage a 3D representation of the scene by taking 3D primitives as input. The model can also be integrated into an inverse problem framework based on diffusion, allowing to retrieve the position of the transmitter, the receiver or the geometry of the scene. Two new datasets for wireless simulation are also presented. Experiments are conducted in synthetic and real settings.
- Although the results on synthetic data are convincing regarding the contribution of the proposed architecture, the impact of the proposed architecture w.r.t. the transformer is not so clear on real data, although the authors explain this by the simplicity of the scene. 
- The most competitive baseline (SEGNN) is not evaluated on the WiPTR dataset.
Yes
No ethics review needed.
(1) The novelty in the machine learning aspect of this paper is unclear. It appears that the work mainly leverages the equivariant Geometric Algebra Transformer for channel prediction. The authors should clarify which components in the machine learning section present new contributions.

(2) The definitions of inverse problems lack clarity. For instance, the authors should provide a more detailed discussion and formulation for receiver localization. Additionally, the explanations of probabilistic inference with diffusion models on Page 5 are inconsistent with the discussion of diffusion models on Page 13, as the diffusion models used do not seem to follow the standard DDPM framework. The authors should include the training and sampling algorithms for the diffusion model utilized, as well as discuss the model's input.

(3) It would be beneficial to compare the proposed method on public datasets and with other models related to channel prediction, such as NeRF and diffusion models.

(4) Regarding generalization, the authors primarily validate their approach on two different datasets. It would be helpful to consider cross-dataset scenarios to assess performance in unseen conditions. Additionally, the authors should discuss the impact of parameters, such as varying simulated frequencies and the number of paths, on prediction performance.

(5) The authors should proofread the paper to correct typographical errors, such as “The The Tx and Rx locations are sampled uniformly within the bounds of the floor layouts.”
(1) Wireless channel prediction is essential in wireless systems, and developing a fully learnable neural simulation surrogate for predicting wireless channels is an emerging topic.

(2) The techniques and experimental results in this paper are solid. The authors apply their proposed model not only to channel prediction but also to two inverse problems: receiver localization and scenario generation. Various simulation results further validate the effectiveness of the proposed method.

(3) The authors have developed two new 3D wireless datasets to validate their model, which would be valuable resources for the wireless research community if published.
In this paper, the authors introduce Wi-GATr, a fully learnable neural simulation surrogate designed to predict wireless channels based on indoor scene elements, including surface mesh, antenna position, and orientation. They employ an equivariant Geometric Algebra Transformer with a tokenizer for wireless simulation. The proposed method is validated using two distinct simulated datasets.
(1) The novelty of this paper in the machine learning component is unclear.

(2) The definitions of inverse problems lack clarity.

(3) This paper lacks of comparisons with public datasets for channel prediction and other state-of-the-art channel prediction models with NeRF and diffusion models.
Yes
No ethics review needed.
1. How were the two datasets generated? Were they extracted from other datasets or were they simulated themselves using other tools. Is this sufficient as one of the contributions of the paper?
1. They design a new Wireless Geometric Algebra Transformer (Wi-GATr) backbone, which embeds the information of the wireless scene into geometric algebra while the network learns to model the channel.
2. They develop a learnable forward-model for channel simulation and an inverse-model for receiver localization based on the differentiable properties.
3. They build two new datasets with diverse scene geometry.
The paper introduces Geometric Algebra Transformer (GATr) into wireless channel observation problem and builds a learnable neural simulation surrogate Wi-GATr to predict channel states based on scene primitives. The authors design a Wi-GATr Backbone to exploit the inherent geometric nature of the propagation of wireless signals. Further, they apply this model to probabilistic inference and receiver localization problems. Experimental results show that Wi-GATr outperforms other methods on the two datasets they constructed.
1. The problem of this work is not well identified. The authors only give the formulation of geometric algebra, but do not give any introduction of the wireless channel model. Wireless channels are complex and consist of many parameters. Authors need to specify what information about the channel they want to simulate and predict.
2. The challenges that need to be addressed are not clearly stated. The authors introduce GATr into this work and build a backbone to make it fit for the wireless channel prediction problem. However, the difficulties and challenges of model transfer are not fully introduced. 
3. The innovation is somewhat limited. In addition to the designed backbone, the rest of the work consists only of two application experiments using the properties of the existing model.  
4. The explanation for some of the pictures is inadequate. For example, Figure 1 shows the geometric surrogates for modeling wireless signal propagation. However, there is not enough explanation of this figure in the paper. It's hard to get the main point of it.
Yes
No ethics review needed.
Listed above.
The paper presents a new approach, Wi-GATr, which is a neural surrogate for wireless channel modeling using Geometric Algebra Transformers, a technique not widely applied in this field. This originality sets it apart from traditional methods by addressing key limitations in differentiability and scalability. The research is supported by thorough empirical evaluations across both simulated and real-world datasets, showing substantial improvements. 
The introduction of two new datasets, Wi3R and WiPTR, further enhances the credibility and reproducibility of the results. The methodology and results are clearly presented. In terms of significance, this work makes contributions to both wireless communication and machine learning.
This paper introduces Wi-GATr, a novel learnable neural surrogate for wireless channel simulation that leverages geometric primitives such as 3D surfaces, antenna positions, and orientations. The primary focus is on addressing limitations in wireless signal propagation modeling by integrating geometric algebra transformers (GATr), which enhance efficiency and accuracy. Wi-GATr is shown to outperform existing models in tasks like signal strength prediction and receiver localization, achieving significant error reductions compared to existing methods.
1. Adam is commonly used in deep learning applications, particularly image-processing tasks. However, wireless signal modeling involves different characteristics and challenges than image data. The authors would benefit from a more detailed discussion on why Adam was chosen, especially considering the fundamental differences between wireless signal modeling and typical image tasks.

2. Wi-GATr’s generalization capabilities come from the E(3)-equivariant design of the Geometric Algebra Transformer (GATr). It would be valuable for the authors to provide more justification or discussion regarding their contribution and novelty in implementing or improving such a design.

3. While the authors introduced their own datasets (Wi3R and WiPTR), the authors should provide a clearer justification for their choice of benchmarks, and using more widely recognized simulators such as WinProp or Wireless InSite could strengthen their work.
We solve forward and inverse problems on simulated and real-world data using equivariant neural simulation surrogates that exploit geometric symmetries found in wireless signal propagation..
@inproceedings{
anonymous2024differentiable,
title={Differentiable and Learnable Wireless Simulation with Geometric Transformers},
author={Anonymous},
booktitle={Submitted to The Thirteenth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=9TClCDZXeh},
note={under review}
}
Modelling the propagation of electromagnetic wireless signals is critical for designing modern communication systems. Wireless ray tracing simulators model signal propagation based on the 3D geometry and other scene parameters, but their accuracy is fundamentally limited by underlying modelling assumptions and correctness of parameters. In this work, we introduce Wi-GATr, a fully-learnable neural simulation surrogate designed to predict the channel observations based on scene primitives (e. g., surface mesh, antenna position and orientation). Recognizing the inherently geometric nature of these primitives, Wi-GATr leverages an equivariant Geometric Algebra Transformer that operates on a tokenizer specifically tailored for wireless simulation. We evaluate our approach on a range of tasks (i. e., signal strength and delay spread prediction, receiver localization, and geometry reconstruction) and find that Wi-GATr is accurate, fast, sample-efficient, and robust to symmetry-induced transformations. Remarkably, we find our results also translate well to the real world: Wi-GATr demonstrates more than 35% lower error than hybrid techniques, and 70% lower error than a calibrated wireless tracer.
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inverse problems learning to simulate wireless channel modeling geometric deep learning equivariance inverse problems electromagnetic signals diffusion models
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Differentiable and Learnable Wireless Simulation with Geometric Transformers
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