# Deep Joint Source-Channel Coding for Adaptive Image Transmission over MIMO Channels

arXiv: https://arxiv.org/abs/2309.00470 · HTML: https://mlsanity.com/paper/2309.00470/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2023.

## Built on by (most cited first, 28 of 28)

- [Joint Source-Channel Coding: Fundamentals and Recent Progress in Practical Designs](https://mlsanity.com/paper/2409.17557/) (2024)
- [High Perceptual Quality Wireless Image Delivery with Denoising Diffusion Models](https://mlsanity.com/paper/2309.15889/) (2023)
- [Diffusion-Driven Semantic Communication for Generative Models with Bandwidth Constraints](https://mlsanity.com/paper/2407.18468/) (2024)
- [Semantics-Guided Diffusion for Deep Joint Source-Channel Coding in Wireless Image Transmission](https://mlsanity.com/paper/2501.01138/) (2025)
- [Applications of Generative AI (GAI) for Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/2405.20024/) (2024)
- [Robust Image Semantic Coding with Learnable CSI Fusion Masking over MIMO Fading Channels](https://mlsanity.com/paper/2406.07389/) (2024)
- [Deep Learning in Physical Layer: Review on Data Driven End-to-End Communication Systems and their Enabling Semantic Applications](https://mlsanity.com/paper/2401.12800/) (2024)
- [DM-MIMO: Diffusion Models for Robust Semantic Communications over MIMO Channels](https://mlsanity.com/paper/2407.05289/) (2024)
- [SING: Semantic Image Communications using Null-Space and INN-Guided Diffusion Models](https://mlsanity.com/paper/2503.12484/) (2025)
- [Latent Space Alignment for AI-Native MIMO Semantic Communications](https://mlsanity.com/paper/2507.16680/) (2025)
- [MambaJSCC: Adaptive Deep Joint Source-Channel Coding with Generalized State Space Model](https://mlsanity.com/paper/2409.16592/) (2024)
- [RIS-aided Latent Space Alignment for Semantic Channel Equalization](https://mlsanity.com/paper/2507.16450/) (2025)
- [Importance-Aware Semantic Communication in MIMO-OFDM Systems Using Vision Transformer](https://mlsanity.com/paper/2508.07696/) (2025)
- [Semantic Channel Equalization Strategies for Deep Joint Source-Channel Coding](https://mlsanity.com/paper/2510.04674/) (2025)
- [Land-then-transport: A Flow Matching-Based Generative Decoder for Wireless Image Transmission](https://mlsanity.com/paper/2601.07512/) (2026)
- [Diffusion-aided Task-oriented Semantic Communications with Model Inversion Attack](https://mlsanity.com/paper/2506.19886/) (2025)
- [SecDiff: Diffusion-Aided Secure Deep Joint Source-Channel Coding Against Adversarial Attacks](https://mlsanity.com/paper/2511.01466/) (2025)
- [Scenario-Adaptive MU-MIMO OFDM Semantic Communication With Asymmetric Neural Network](https://mlsanity.com/paper/2602.13557/) (2026)
- [Bridging Neural Networks and Wireless Systems with MIMO-OFDM Semantic Communications](https://mlsanity.com/paper/2501.16726/) (2025)
- [Way to Build Native AI-driven 6G Air Interface: Principles, Roadmap, and Outlook](https://mlsanity.com/paper/2508.15277/) (2025)
- [Synesthesia of Machines (SoM)-Based Task-Driven MIMO System for Image Transmission](https://mlsanity.com/paper/2509.02031/) (2025)
- [Through-the-Earth Magnetic Induction Communication and Networking: A Comprehensive Survey](https://mlsanity.com/paper/2510.14854/) (2025)
- [Semantic Multiplexing](https://mlsanity.com/paper/2511.13779/) (2025)
- [Robust Nonlinear Transform Coding: A Framework for Generalizable Joint Source-Channel Coding](https://mlsanity.com/paper/2511.18884/) (2025)
- [Selective Depthwise Separable Convolution for Lightweight Joint Source-Channel Coding in Wireless Image Transmission](https://mlsanity.com/paper/2604.22338/) (2026)
- [Restoration Flow Matching-Based Channel Refinement and Equalization Correction for MIMO Semantic Communications](https://mlsanity.com/paper/2607.23615/) (2026)
- [From Semantic to Token Communication: The Next Paradigm for Large-Model-Driven 6G Intelligent Connectivity](https://mlsanity.com/paper/2609.10714/) (2026)
- [Beyond Token Accuracy: Prioritizing What Matters for Visual Reconstruction](https://mlsanity.com/paper/2610.03822/) (2026)

## Abstract (arXiv)

This paper introduces a vision transformer (ViT)-based deep joint source and channel coding (DeepJSCC) scheme for wireless image transmission over multiple-input multiple-output (MIMO) channels, denoted as DeepJSCC-MIMO. We consider DeepJSCC-MIMO for adaptive image transmission in both open-loop and closed-loop MIMO systems. The novel DeepJSCC-MIMO architecture surpasses the classical separation-based benchmarks with robustness to channel estimation errors and showcases remarkable flexibility in adapting to diverse channel conditions and antenna numbers without requiring retraining. Specifically, by harnessing the self-attention mechanism of ViT, DeepJSCC-MIMO intelligently learns feature mapping and power allocation strategies tailored to the unique characteristics of the source image and prevailing channel conditions. Extensive numerical experiments validate the significant improvements in transmission quality achieved by DeepJSCC-MIMO for both open-loop and closed-loop MIMO systems across a wide range of scenarios. Moreover, DeepJSCC-MIMO exhibits robustness to varying channel conditions, channel estimation errors, and different antenna numbers, making it an appealing solution for emerging semantic communication systems.