基于MIMO-CSI预测的轻量化信源信道联合编码方法

于创宇; 徐彦彦; 潘少明

doi:10.11959/j.issn.1000-0801.2025126

您当前的位置：

首页 >

文章列表页 >

基于MIMO-CSI预测的轻量化信源信道联合编码方法

研究与开发 | 更新时间：2025-06-28

- 基于MIMO-CSI预测的轻量化信源信道联合编码方法
- Lightweight joint source-channel coding method based on MIMO-CSI prediction
- 电信科学 2025年41卷第6期页码：29-47
- 作者机构：
  
  武汉大学测绘遥感信息工程全国重点实验室，湖北武汉 430079
- 作者简介：
  
  [ "于创宇（2000- ），男，武汉大学测绘遥感信息工程全国重点实验室硕士生，主要研究方向为语义通信与联邦学习。" ]
  [ "徐彦彦（1974- ），女，博士，武汉大学测绘遥感信息工程全国重点实验室教授，主要研究方向为云计算安全与智能网络通信。" ]
  [ "潘少明（1972- ），男，博士，武汉大学测绘遥感信息工程全国重点实验室副教授，主要研究方向为多媒体网络与通信技术和数据挖掘与建模。" ]
- 基金信息：
  
  国家重点研发计划项目(2022YFB3903404);湖北省技术创新计划科技重大项目(2024BAA011)
- DOI：10.11959/j.issn.1000-0801.2025126
  中图分类号： TN929.5
- 收稿日期：2024-12-12，
  
  修回日期：2025-04-24，
  
  纸质出版日期：2025-06-20
- 稿件说明：
移动端阅览
于创宇,徐彦彦,潘少明.基于MIMO-CSI预测的轻量化信源信道联合编码方法[J].电信科学,2025,41(06):29-47.

YU Chuangyu,XU Yanyan,PAN Shaoming.Lightweight joint source-channel coding method based on MIMO-CSI prediction[J].Telecommunications Science,2025,41(06):29-47.
于创宇,徐彦彦,潘少明.基于MIMO-CSI预测的轻量化信源信道联合编码方法[J].电信科学,2025,41(06):29-47. DOI： 10.11959/j.issn.1000-0801.2025126.

YU Chuangyu,XU Yanyan,PAN Shaoming.Lightweight joint source-channel coding method based on MIMO-CSI prediction[J].Telecommunications Science,2025,41(06):29-47. DOI： 10.11959/j.issn.1000-0801.2025126.

摘要

高效的深度信源信道联合编码（deep joint source-channel coding，DeepJSCC）是实现带宽受限场景下语义通信的关键技术，然而在车联网或无人机等终端资源受限的场景中，现有方法难以适应多输入多输出（multiple-input multiple-output，MIMO）信道的动态变化，且模型庞大难以部署。为此，提出一种轻量化DeepJSCC框架（VxLJSCC）。首先，基于扩展长短期记忆网络的语义提取网络实现轻量化的高质量语义特征提取；然后，采用MIMO-信道状态信息（channel state information，CSI）预测来解决语义通信系统使用过时CSI而导致系统性能下降的问题；最后，为使语义信息充分适应时变MIMO信道质量，设计了基于信道预测的特征分配与自适应模块，结合语义特征的重要性，为不同特征分配合适的传输信道和时隙，并对特征进行调整，从而提升图像重建的语义精度。实验表明，相较于先进的DeepJSCC-MIMO方法，VxLJSCC在节省最多61.67%模型存储和77.86%计算量的情况下，仍能提供高达2.972 dB的信道增益。

Abstract

Efficient deep joint source-channel coding (DeepJSCC) is a key technology for enabling semantic communication in band-width-constrained scenarios. However

in resource-limited environments such as vehicular networks or unmanned aerial vehicles

existing methods struggle to adapt to the dynamic characteristics of multiple-input multiple-output (MIMO) channels and are challenging to deploy due to their large model size. To address these issues

a lightweight DeepJSCC framework (VxLJSCC) was proposed. A semantic extraction network based on extended long short-term memory was proposed to achieve a lightweight

high quality semantic feature extraction. Then

MIMO-channel state information (CSI) prediction was employed to address the performance degradation caused by CSI aging in semantic communication systems. Finally

to adapt the semantic information to the time-varying quality of MIMO channels

a channel adaptive module was designed. This module assigns appropriate transmission subchannels and time slots to different features based on their importance

thereby enhancing the semantic accuracy of image reconstruction. Simulation results show that

compared to the best method DeepJSCC-MIMO

VxLJSCC saves up to 61.67% in model storage and 77.86% in computational cost

while still providing up to 2.972 dB channel gain.

关键词

Keywords

references

江沸菠 , 彭于波 , 董莉 . 面向6G的深度图像语义通信模型 [J ] . 通信学报 , 2023 , 44 ( 3 ): 198 - 208 .

JIANG F B , PENG Y B , DONG L . Deep image semantic communication model for 6G [J ] . Journal on Communications , 2023 , 44 ( 3 ): 198 - 208 .

CALVANESE STRINATI E , BARBAROSSA S . 6G networks: Beyond Shannon towards semantic and goal-oriented communications [J ] . Computer Networks , 2021 , 190 : 107930 .

SHI G M , XIAO Y , LI Y Y , et al . From semantic communication to semantic-aware networking: model, architecture, and open problems [J ] . IEEE Communications Magazine , 2021 , 59 ( 8 ): 44 - 50 .

BOURTSOULATZE E , BURTH KURKA D , GÜNDÜZ D . Deep joint source-channel coding for wireless image transmission [J ] . IEEE Transactions on Cognitive Communications and Networking , 2019 , 5 ( 3 ): 567 - 579 .

YANG K , WANG S X , DAI J C , et al . SwinJSCC: taming swin transformer for deep joint source-channel coding [J ] . IEEE Transactions on Cognitive Communications and Networking , 2025 , 11 ( 1 ): 90 - 104 .

WU T , CHEN Z Y , TAO M X , et al . MambaJSCC: deep joint source-channel coding with visual state space model [J ] . arXiv preprint , 2024 ,arXiv:2405. 03125 .

YANG M Y , BIAN C H , KIM H S . OFDM-guided deep joint source channel coding for wireless multipath fading channels [J ] . IEEE Transactions on Cognitive Communications and Networking , 2022 , 8 ( 2 ): 584 - 599 .

SUN Q Z , GUO C L , YANG Y , et al . Deep joint source-channel coding for wireless image transmission with semantic importance [C ] // Proceedings of the 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) . Piscataway : IEEE Press , 2022 : 1 - 7 .

XIAO Z X , YAO S S , DAI J C , et al . Wireless deep speech semantic transmission [C ] // Proceedings of the ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . Piscataway : IEEE Press , 2023 : 1 - 5 .

WU H T , SHAO Y L , BIAN C H , et al . Deep joint source-channel coding for adaptive image transmission over MIMO channels [J ] . IEEE Transactions on Wireless Communications , 2024 , 23 ( 10 ): 15002 - 15017 .

PENG J C , XING H L , LI Y , et al . Task-oriented multi-user semantic communication with lightweight semantic encoder and fast training for resource-constrained terminal devices [J ] . IEEE Wireless Communications Letters , 2024 , 13 ( 9 ): 2427 - 2431 .

BURGHAL D , LI Y , MADADI P , et al . Enhanced AI-based CSI prediction solutions for massive MIMO in 5G and 6G systems [J ] . IEEE Access , 2023 , 11 : 117810 - 117825 .

ALKIN B , BECK M , PÖPPEL K , et al . Vision-LSTM: xLSTM as generic vision backbone [J ] . arXiv preprint , 2024 ,arXiv: 2406.04303 .

BECK M , PÖPPEL K , SPANRING M , et al . xLSTM: extended long short-term memory [J ] . arXiv preprint , 2024 ,arXiv: 2405.04517 .

KANG H , HU Q Y , CHEN H X , et al . Cross-shaped separated spatial-temporal UNet transformer for accurate channel prediction [C ] // Proceedings of the IEEE INFOCOM 2024 - IEEE Conference on Computer Communications . Piscataway : IEEE Press , 2024 : 2079 - 2088 .

YANG S S , HANZO L . Fifty years of MIMO detection: the road to large-scale MIMOs [J ] . IEEE Communications Surveys & Tutorials , 2015 , 17 ( 4 ): 1941 - 1988 .

SELVARAJU R R , COGSWELL M , DAS A , et al . Grad-CAM: visual explanations from deep networks via gradient-based localization [C ] // Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV) . Piscataway : IEEE Press , 2017 : 618 - 626 .

KURKA D B , GÜNDÜZ D . DeepJSCC-f: deep joint source-channel coding of images with feedback [J ] . IEEE Journal on Selected Areas in Information Theory , 2020 , 1 ( 1 ): 178 - 193 .

AGUSTSSON E , TIMOFTE R . NTIRE 2017 challenge on single image super-resolution: dataset and study [C ] // Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) . Piscataway : IEEE Press , 2017 : 1122 - 1131 .

RICHARDSON T , KUDEKAR S . Design of low-density parity check codes for 5G new radio [J ] . IEEE Communications Magazine , 2018 , 56 ( 3 ): 28 - 34 .

FRESIA M , PERÉZ-CRUZ F , POOR H V , et al . Joint source and channel coding [J ] . IEEE Signal Processing Magazine , 2010 , 27 ( 6 ): 104 - 113 .

HORÉ A , ZIOU D . Image quality metrics: PSNR vs. SSIM [C ] // Proceedings of the 2010 20th International Conference on Pattern Recognition . Piscataway : IEEE Press , 2010 : 2366 - 2369 .

WANG Z , SIMONCELLI E P , BOVIK A C . Multiscale structural similarity for image quality assessment [C ] // Proceedings of the Thrity-Seventh Asilomar Conference on Signals , Systems & Computers, 2003 . Piscataway : IEEE Press , 2003 : 1398 - 1402 .

ALKHATEEB A . DeepMIMO: a generic deep learning dataset for millimeter wave and massive MIMO applications [J ] . arXiv preprint , 2019 ,arXiv: 1902.06435 .

LIU Y , HU T G , ZHANG H R , et al . iTransformer: inverted transformers are effective for time series forecasting [J ] . arXiv preprint , 2023 , arXiv: 2310.06625 .

ZHANG Y B , WANG J , SUN J L , et al . CV-3DCNN: complex-valued deep learning for CSI prediction in FDD massive MIMO systems [J ] . IEEE Wireless Communications Letters , 2021 , 10 ( 2 ): 266 - 270 .

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于K-Medoids提取信道状态特征的无人机探测方法

面向mMIMO系统的模式分割随机接入方案

语义在通信中的应用综述

基于信道状态信息的室内感知技术

基于Wi-Fi的烟雾实时检测方法