# Conditional Diffusion Model-Enabled Scenario-Specific Neural Receivers for Superimposed Pilot Schemes

arXiv: https://arxiv.org/abs/2511.01173 · HTML: https://mlsanity.com/paper/2511.01173/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Diffusion for compression and wireless, one of 14 directions in Diffusion models.
- Builds on 26 of its 40 references that are in the map.
- First posted on arXiv 3 Nov 2025.

## Placed in

- Diffusion models / [Diffusion for compression and wireless](https://mlsanity.com/diffusion/diffusion-for-compression-and-wireless/)

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Elucidating the Design Space of Diffusion-Based Generative Models](https://mlsanity.com/paper/2206.00364/) (2022)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [Consistency Models](https://mlsanity.com/paper/2303.01469/) (2023)
- [How Does Mixup Help With Robustness and Generalization?](https://mlsanity.com/paper/2010.04819/) (2020)
- [Large Generative AI Models for Telecom: The Next Big Thing?](https://mlsanity.com/paper/2306.10249/) (2023)
- [RF-Diffusion: Radio Signal Generation via Time-Frequency Diffusion](https://mlsanity.com/paper/2404.09140/) (2024)
- [End-to-end Learning for OFDM: From Neural Receivers to Pilotless Communication](https://mlsanity.com/paper/2009.05261/) (2020)
- [A Tutorial on Extremely Large-Scale MIMO for 6G: Fundamentals, Signal Processing, and Applications](https://mlsanity.com/paper/2307.07340/) (2023)
- [Generative Diffusion Models for High Dimensional Channel Estimation](https://mlsanity.com/paper/2408.10501/) (2024)
- [Diffusion-based Generative Prior for Low-Complexity MIMO Channel Estimation](https://mlsanity.com/paper/2403.03545/) (2024)
- [Generative Diffusion Models for Radio Wireless Channel Modelling and Sampling](https://mlsanity.com/paper/2308.05583/) (2023)
- [Generating High Dimensional User-Specific Wireless Channels using Diffusion Models](https://mlsanity.com/paper/2409.03924/) (2024)
- [Latent Diffusion Model-Enabled Low-Latency Semantic Communication in the Presence of Semantic Ambiguities and Wireless Channel Noises](https://mlsanity.com/paper/2406.06644/) (2024)
- [Evaluation Metrics and Methods for Generative Models in the Wireless PHY Layer](https://mlsanity.com/paper/2408.00634/) (2024)
- [Multi-task Learning-based CSI Feedback Design in Multiple Scenarios](https://mlsanity.com/paper/2204.12698/) (2022)
- [Bayesian Learning for Double-RIS Aided ISAC Systems with Superimposed Pilots and Data](https://mlsanity.com/paper/2402.10593/) (2024)
- [Interference Cancellation Based Neural Receiver for Superimposed Pilot in Multi-Layer Transmission](https://mlsanity.com/paper/2406.18993/) (2024)
- [Spectral and Energy Efficiency of Superimposed Pilots in Uplink Massive MIMO](https://mlsanity.com/paper/1709.07722/) (2017)

## Abstract (arXiv)

Neural receivers have demonstrated strong performance in wireless communication systems. However, their effectiveness typically depends on access to large-scale, scenario-specific channel data for training, which is often difficult to obtain in practice. Recently, generative artificial intelligence (AI) models, particularly diffusion models (DMs), have emerged as effective tools for synthesizing high-dimensional data. This paper presents a scenario-specific channel generation method based on conditional DMs, which accurately model channel distributions conditioned on user location and velocity information. The generated synthetic channel data are then employed for data augmentation to improve the training of a neural receiver designed for superimposed pilot-based transmission. Experimental results show that the proposed method generates high-fidelity channel samples and significantly enhances neural receiver performance in the target scenarios, outperforming conventional data augmentation and generative adversarial network-based techniques.