# Uplink Assisted Joint Channel Estimation and CSI Feedback: An Approach Based on Deep Joint Source-Channel Coding

arXiv: https://arxiv.org/abs/2504.10836 · HTML: https://mlsanity.com/paper/2504.10836/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 39 references that are in the map.
- First posted on arXiv 15 Apr 2025.

## Builds on (most cited first, 20 of 20)

- [Sionna: An Open-Source Library for Next-Generation Physical Layer Research](https://mlsanity.com/paper/2203.11854/) (2022)
- [On the Road to 6G: Visions, Requirements, Key Technologies and Testbeds](https://mlsanity.com/paper/2302.14536/) (2023)
- [Overview of Deep Learning-based CSI Feedback in Massive MIMO Systems](https://mlsanity.com/paper/2206.14383/) (2022)
- [Convolutional Neural Network based Multiple-Rate Compressive Sensing for Massive MIMO CSI Feedback: Design, Simulation, and Analysis](https://mlsanity.com/paper/1906.06007/) (2019)
- [Deep Learning for Distributed Channel Feedback and Multiuser Precoding in FDD Massive MIMO](https://mlsanity.com/paper/2007.06512/) (2020)
- [Pruning the Pilots: Deep Learning-Based Pilot Design and Channel Estimation for MIMO-OFDM Systems](https://mlsanity.com/paper/2006.11796/) (2020)
- [Deep Joint Source-Channel Coding for CSI Feedback: An End-to-End Approach](https://mlsanity.com/paper/2203.16005/) (2022)
- [Deep Learning based Downlink Channel Prediction for FDD Massive MIMO System](https://mlsanity.com/paper/1908.03360/) (2019)
- [Data-Driven Deep Learning to Design Pilot and Channel Estimator For Massive MIMO](https://mlsanity.com/paper/2003.05875/) (2020)
- [CAnet: Uplink-aided Downlink Channel Acquisition in FDD Massive MIMO using Deep Learning](https://mlsanity.com/paper/2101.04377/) (2021)
- [Learning-Based Phase Compression and Quantization for Massive MIMO CSI Feedback with Magnitude-Aided Information](https://mlsanity.com/paper/2103.00432/) (2021)
- [CSI-GPT: Integrating Generative Pre-Trained Transformer with Federated-Tuning to Acquire Downlink Massive MIMO Channels](https://mlsanity.com/paper/2406.03438/) (2024)
- [AI for CSI Feedback Enhancement in 5G-Advanced](https://mlsanity.com/paper/2206.15132/) (2022)
- [Knowledge-driven Meta-learning for CSI Feedback](https://mlsanity.com/paper/2310.15548/) (2023)
- [Swin Transformer-Based CSI Feedback for Massive MIMO](https://mlsanity.com/paper/2401.06435/) (2024)
- [A Versatile Low-Complexity Feedback Scheme for FDD Systems via Generative Modeling](https://mlsanity.com/paper/2304.14373/) (2023)
- [Deep Joint CSI Feedback and Multiuser Precoding for MIMO OFDM Systems](https://mlsanity.com/paper/2404.16289/) (2024)
- [A Partial Reciprocity-based Channel Prediction Framework for FDD Massive MIMO with High Mobility](https://mlsanity.com/paper/2202.05564/) (2022)
- [Precoding-oriented Massive MIMO CSI Feedback Design](https://mlsanity.com/paper/2302.11526/) (2023)
- [Exploiting Partial FDD Reciprocity for Beam Based Pilot Precoding and CSI Feedback in Deep Learning](https://mlsanity.com/paper/2205.14558/) (2022)

## Abstract (arXiv)

In frequency division duplex (FDD) multiple-input multiple-output (MIMO) wireless communication systems, the acquisition of downlink channel state information (CSI) is essential for maximizing spatial resource utilization and improving system spectral efficiency. The separate design of modules in AI-based CSI feedback architectures under traditional modular communication frameworks, including channel estimation (CE), CSI compression and feedback, leads to sub-optimal performance. In this paper, we propose an uplink assisted joint CE and and CSI feedback approach via deep learning for downlink CSI acquisition, which mitigates performance degradation caused by distribution bias across separately trained modules in traditional modular communication frameworks. The proposed network adopts a deep joint source-channel coding (DJSCC) architecture to mitigate the cliff effect encountered in the conventional separate source-channel coding. Furthermore, we exploit the uplink CSI as auxiliary information to enhance CSI reconstruction accuracy by leveraging the partial reciprocity between the uplink and downlink channels in FDD systems, without introducing additional overhead. The effectiveness of uplink CSI as assisted information and the necessity of an end-toend multi-module joint training architecture is validated through comprehensive ablation and scalability experiments.