# Importance-Aware Semantic Communication in MIMO-OFDM Systems Using Vision Transformer

arXiv: https://arxiv.org/abs/2508.07696 · HTML: https://mlsanity.com/paper/2508.07696/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 36 references that are in the map.
- First posted on arXiv 11 Aug 2025.

## Builds on (most cited first, 24 of 24)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Deep Learning Enabled Semantic Communication Systems](https://mlsanity.com/paper/2006.10685/) (2020)
- [Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications](https://mlsanity.com/paper/2207.09353/) (2022)
- [Semantic Communication Systems for Speech Transmission](https://mlsanity.com/paper/2102.12605/) (2021)
- [Generative Joint Source-Channel Coding for Semantic Image Transmission](https://mlsanity.com/paper/2211.13772/) (2022)
- [Task-Oriented Communication for Multi-Device Cooperative Edge Inference](https://mlsanity.com/paper/2109.00172/) (2021)
- [Task-Oriented Multi-User Semantic Communications for VQA Task](https://mlsanity.com/paper/2108.07357/) (2021)
- [Bandwidth-Agile Image Transmission with Deep Joint Source-Channel Coding](https://mlsanity.com/paper/2009.12480/) (2020)
- [Deep Joint Source-Channel Coding for Adaptive Image Transmission over MIMO Channels](https://mlsanity.com/paper/2309.00470/) (2023)
- [OFDM-guided Deep Joint Source Channel Coding for Wireless Multipath Fading Channels](https://mlsanity.com/paper/2109.05194/) (2021)
- [Resource allocation for text semantic communications](https://mlsanity.com/paper/2201.06023/) (2022)
- [D2-JSCC: Digital Deep Joint Source-channel Coding for Semantic Communications](https://mlsanity.com/paper/2403.07338/) (2024)
- [Joint Coding-Modulation for Digital Semantic Communications via Variational Autoencoder](https://mlsanity.com/paper/2310.06690/) (2023)
- [Vector Quantized Semantic Communication System](https://mlsanity.com/paper/2209.11519/) (2022)
- [Block Coordinate Descent on Smooth Manifolds: Convergence Theory and Twenty-One Examples](https://mlsanity.com/paper/2305.14744/) (2023)
- [Joint Source-Channel Coding for Channel-Adaptive Digital Semantic Communications](https://mlsanity.com/paper/2311.08146/) (2023)
- [Digital-SC: Digital Semantic Communication with Adaptive Network Split and Learned Non-Linear Quantization](https://mlsanity.com/paper/2305.13553/) (2023)
- [Goal-Oriented Communications for the IoT and Application to Data Compression](https://mlsanity.com/paper/2211.05378/) (2022)
- [From Analog to Digital: Multi-Order Digital Joint Coding-Modulation for Semantic Communication](https://mlsanity.com/paper/2406.05437/) (2024)
- [Attention-aware Semantic Communications for Collaborative Inference](https://mlsanity.com/paper/2404.07217/) (2024)
- [Feature Allocation for Semantic Communication with Space-Time Importance Awareness](https://mlsanity.com/paper/2401.14614/) (2024)
- [Vision Transformer-based Semantic Communications With Importance-Aware Quantization](https://mlsanity.com/paper/2412.06038/) (2024)
- [Blind Training for Channel-Adaptive Digital Semantic Communications](https://mlsanity.com/paper/2501.02273/) (2025)

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

- [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)

## Abstract (arXiv)

This paper presents a novel importance-aware quantization, subcarrier mapping, and power allocation (IA-QSMPA) framework for semantic communication in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, empowered by a pretrained Vision Transformer (ViT). The proposed framework exploits attention-based importance extracted from a pretrained ViT to jointly optimize quantization levels, subcarrier mapping, and power allocation. Specifically, IA-QSMPA maps semantically important features to high-quality subchannels and allocates resources in accordance with their contribution to task performance and communication latency. To efficiently solve the resulting nonconvex optimization problem, a block coordinate descent algorithm is employed. The framework is further extended to operate under finite blocklength transmission, where communication errors may occur. In this setting, a segment-wise linear approximation of the channel dispersion penalty is introduced to enable efficient joint optimization under practical constraints. Simulation results on a multi-view image classification task using the MVP-N dataset demonstrate that IA-QSMPA significantly outperforms conventional methods in both ideal and finite blocklength transmission scenarios, achieving superior task performance and communication efficiency.