# Deep Generative Model for Simultaneous Range Error Mitigation and Environment Identification

arXiv: https://arxiv.org/abs/2305.18206 · HTML: https://mlsanity.com/paper/2305.18206/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 15 references that are in the map.
- First posted on arXiv 23 May 2023.

## Builds on (most cited first, 3 of 3)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Robust Ultra-wideband Range Error Mitigation with Deep Learning at the Edge](https://mlsanity.com/paper/2011.14684/) (2020)
- [CSI-based Outdoor Localization for Massive MIMO: Experiments with a Learning Approach](https://mlsanity.com/paper/1806.07447/) (2018)

## Built on by (most cited first, 2 of 2)

- [A Deep Generative Model for Synthesizing Labeled Wireless Signals](https://mlsanity.com/paper/2609.05396/) (2026)
- [IIns-VAE+: A Robust Transfer Learning Framework for Environmental Identification in Wireless Sensing](https://mlsanity.com/paper/2609.06131/) (2026)

## Abstract (arXiv)

Received waveforms contain rich information for both range information and environment semantics. However, its full potential is hard to exploit under multipath and non-line-of-sight conditions. This paper proposes a deep generative model (DGM) for simultaneous range error mitigation and environment identification. In particular, we present a Bayesian model for the generative process of the received waveform composed by latent variables for both range-related features and environment semantics. The simultaneous range error mitigation and environment identification is interpreted as an inference problem based on the DGM, and implemented in a unique end-to-end learning scheme. Comprehensive experiments on a general Ultra-wideband dataset demonstrate the superior performance on range error mitigation, scalability to different environments, and novel capability on simultaneous environment identification.