Model division multiple access for semantic communications

Ping ZHANG; Xiaodong XU; Chen DONG; Kai NIU; Haotai LIANG; Zijian LIANG; Xiaoqi QIN; Mengying SUN; Hao CHEN; Nan MA; Wenjun XU; Guangyu WANG; Xiaofeng TAO

doi:10.1631/FITEE.2300196

Your Location：

Home >

Browse articles >

Model division multiple access for semantic communications

Regular Papers | Updated：2023-07-03

- Model division multiple access for semantic communications
  Cover Article Enhanced Publication
- 面向语义通信的模分多址技术
- Frontiers of Information Technology & Electronic Engineering Vol. 24, Issue 6, Pages: 801-812(2023)
- Affiliations：
  
  1.State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
  2.Department of Broadband Communication, Peng Cheng Laboratory, Shenzhen 518066, China
  3.ZGC Institute of Ubiquitous-X Innovation and Applications, Beijing 100876, China
  4.School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
- Author bio：
  
  †E-mail: pzhang@bupt.edu.cn;
  xuxiaodong@bupt.edu.cn;
  ‡Corresponding authors
- Funds：
- DOI：10.1631/FITEE.2300196
  CLC： TN92
- Published：0 June 2023，
  
  Published Online：02 June 2023，
  
  Received：21 March 2023，
  
  Accepted：2023-05-15
- Accepted：
Scan QR Code
PING ZHANG, XIAODONG XU, CHEN DONG, et al. Model division multiple access for semantic communications. [J]. Frontiers of information technology & electronic engineering, 2023, 24(6): 801-812.
DOI：

PING ZHANG, XIAODONG XU, CHEN DONG, et al. Model division multiple access for semantic communications. [J]. Frontiers of information technology & electronic engineering, 2023, 24(6): 801-812. DOI： 10.1631/FITEE.2300196.

摘要

在多用户系统中，系统资源应分配给不同用户。在传统通信系统中，系统资源通常包括时间、频率、空间和功率，因此广泛使用诸如时分多址（TDMA）、频分多址（FDMA）、空分多址（SDMA）、码分多址（CDMA）、非正交多址（NOMA）之类多址技术。在被认为是下一代通信系统新范式的语义通信中，我们从语义角度，以基于模型的人工智能方法，从信源中提取高维语义域特征，并针对信源和信道特征联合构建模型信息空间。从模型信息空间中挖掘语义信息的共性化和个性化信息，提出一种新的基于语义域资源的多址技术，称为模分多址（MDMA）。从信息论角度，证明模分多址比传统多址技术获得更多性能提升。仿真结果表明，模分多址比传统多址技术节省更多带宽资源，并且在低信噪比条件下，在加性高斯白噪声（AWGN）信道中，相比非正交多址具有至少5 dB的性能优势。

Abstract

In a multi-user system

system resources should be allocated to different users. In traditional communication systems

system resources generally include time

frequency

space

and power

so multiple access technologies such as time division multiple access (TDMA)

frequency division multiple access (FDMA)

space division multiple access (SDMA)

code division multiple access (CDMA)

and non-orthogonal multiple access (NOMA) are widely used. In semantic communication

which is considered a new paradigm of the next-generation communication system

we extract high-dimensional features from signal sources in a model-based artificial intelligence approach from a semantic perspective and construct a model information space for signal sources and channel features. From the high-dimensional semantic space

we excavate the shared and personalized information of semantic information and propose a novel multiple access technology

named model division multiple access (MDMA)

which is based on the resource of the semantic domain. From the perspective of information theory

we prove that MDMA can attain more performance gains than traditional multiple access technologies. Simulation results show that MDMA saves more bandwidth resources than traditional multiple access technologies

and that MDMA has at least a 5-dB advantage over NOMA in the additive white Gaussian noise (AWGN) channel under the low signal-to-noise (SNR) condition.

关键词

模分多址（MDMA）语义通信多址技术

Keywords

Model division multiple access (MDMA)Semantic communicationMultiple access

references

Cover TM, Thomas JA, 2001. Elements of Information Theory. John Wiley & Sons, Ltd., New York, USA, p.374-458. doi: 10.1002/0471200611.ch14http://doi.org/10.1002/0471200611.ch14

Dai JC, Wang SX, Tan KL, et al., 2022. Nonlinear transform source-channel coding for semantic communications. IEEE J Sel Areas Commun, 40(8):2300-2316. doi: 10.1109/JSAC.2022.3180802http://doi.org/10.1109/JSAC.2022.3180802

Dai LL, Wang BC, Yuan YF, et al., 2015. Non-orthogonal multiple access for 5G: solutions, challenges, opportunities, and future research trends. IEEE Commun Mag, 53(9):74-81. doi: 10.1109/MCOM.2015.7263349http://doi.org/10.1109/MCOM.2015.7263349

Ding ZG, Adachi F, Poor HV, 2016. The application of MIMO to non-orthogonal multiple access. IEEE Trans Wirel Commun, 15(1):537-552. doi: 10.1109/TWC.2015.2475746http://doi.org/10.1109/TWC.2015.2475746

Dong C, Liang HT, Xu XD, et al., 2023. Semantic communication system based on semantic slice models propagation. IEEE J Sel Areas Commun, 41(1):202-213. doi: 10.1109/JSAC.2022.3221948http://doi.org/10.1109/JSAC.2022.3221948

Gao S, Zhang M, Cheng X, 2018. Precoded index modulation for multi-input multi-output OFDM. IEEE Trans Wirel Commun, 17(1):17-28. doi: 10.1109/TWC.2017.2760823http://doi.org/10.1109/TWC.2017.2760823

Islam SMR, Avazov N, Dobre OA, et al., 2017. Power-domain non-orthogonal multiple access (NOMA) in 5G systems: potentials and challenges. IEEE J Sel Areas Commun, 19(2):721-742. doi: 10.1109/COMST.2016.2621116http://doi.org/10.1109/COMST.2016.2621116

Jiang PW, Wen CK, Jin S, et al., 2023. Wireless semantic communications for video conferencing. IEEE J Sel Areas Commun, 41(1):230-244. doi: 10.1109/JSAC.2022.3221968http://doi.org/10.1109/JSAC.2022.3221968

Krizhevsky A, 2009. Learning Multiple Layers of Features from Tiny Images. Technical Report, TR-2009. University of Toronto, Toronto, Canada.

Kuznetsova A, Rom H, Alldrin N, et al., 2020. The open images dataset V4: unified image classification, object detection, and visual relationship detection at scale. Int J Comput Vis, 128:1956-1981. doi: 10.1007/s11263-020-01316-zhttp://doi.org/10.1007/s11263-020-01316-z

Li WZ, Liang HT, Dong C, et al., 2023. Non-orthogonal multiple access enhanced multi-user semantic communication. https://arxiv.org/abs/2303.06597https://arxiv.org/abs/2303.06597

Luo XW, Gao RB, Chen HH, et al., 2022. Multi-modal and multi-user semantic communications for channel-level information fusion. IEEE Wirel Commun, early access. doi: 10.1109/MWC.011.2200288http://doi.org/10.1109/MWC.011.2200288

Mao Y, Dizdar O, Clerckx B, et al., 2022. Rate-splitting multiple access: fundamentals, survey, and future research trends. IEEE Commun Surv Tutor, 24(4):2073-2126. doi: 10.1109/COMST.2022.3191937http://doi.org/10.1109/COMST.2022.3191937

Saito Y, Kishiyama Y, Benjebbour A, et al., 2013. Non-orthogonal multiple access (NOMA) for cellular future radio access. IEEE 77th Vehicular Technology Conf, p.1-5. doi: 10.1109/VTCSpring.2013.6692652http://doi.org/10.1109/VTCSpring.2013.6692652

Shannon CE, 1948. A mathematical theory of communication. Bell Syst Tech J, 27(3):379-423. doi: 10.1002/j.1538-7305.1948.tb01338.xhttp://doi.org/10.1002/j.1538-7305.1948.tb01338.x

Shi YX, Shao S, Wu YP, et al., 2023. Excess distortion exponent analysis for semantic-aware MIMO communication systems. https://arxiv.org/abs/2301.04357https://arxiv.org/abs/2301.04357

Wang SX, Dai JC, Liang ZJ, et al., 2023. Wireless deep video semantic transmission. IEEE J Sel Areas Commun, 41(1):214-229. doi: 10.1109/JSAC.2022.3221977http://doi.org/10.1109/JSAC.2022.3221977

Wang Z, Zhang JY, Du HY, et al., 2023. Extremely large-scale MIMO: fundamentals, challenges, solutions, and future directions. IEEE Wirel Commun, early access. doi: 10.1109/MWC.132.2200443http://doi.org/10.1109/MWC.132.2200443

Weng Z, Qin Z, 2021. Semantic communication systems for speech transmission. IEEE J Sel Areas Commun, 39(8):2434-2444. doi: 10.1109/JSAC.2021.3087240http://doi.org/10.1109/JSAC.2021.3087240

Wu Z, Lu K, Jiang C, et al., 2018. Comprehensive study and comparison on 5G NOMA schemes. IEEE Access, 6:18511-18519. doi: 10.1109/ACCESS.2018.2817221http://doi.org/10.1109/ACCESS.2018.2817221

Xie H, Qin Z, Li GY, et al., 2021. Deep learning enabled semantic communication systems. IEEE Trans Signal Process, 69:2663-2675. doi: 10.1109/TSP.2021.3071210http://doi.org/10.1109/TSP.2021.3071210

Zhang P, Xu WJ, Gao H, et al., 2022a. Toward wisdom-evolutionary and primitive-concise 6G: a new paradigm of semantic communication networks. Engineering, 8:60-73. doi: 10.1016/j.eng.2021.11.003http://doi.org/10.1016/j.eng.2021.11.003

Zhang P, Peng MG, Cui SG, et al., 2022b. Theory and techniques for “intellicise” wireless networks. Front Inform Technol Electron Eng, 23(1):1-4. doi: 10.1631/FITEE.2210000http://doi.org/10.1631/FITEE.2210000

Zhang P, Xu XD, Dong C, et al., 2022c. Intellicise communication system: model-driven semantic communications. J China Univ Posts Telecommun, 29(1):2-12. doi: 10.19682/j.cnki.1005-8885.2022.2002http://doi.org/10.19682/j.cnki.1005-8885.2022.2002

Zhang Y, Xu W, Gao H, et al., 2022. Multi-user semantic communications for cooperative object identification. IEEE Int Conf on Communications Workshops, p.157-162. doi: 10.1109/ICCWorkshops53468.2022.9814491http://doi.org/10.1109/ICCWorkshops53468.2022.9814491

Zhu YW, Huang YK, Qiao XQ, et al., 2022. A semantic-aware transmission with adaptive control scheme for volumetric video service. IEEE Trans Multim, early access. doi: 10.1109/TMM.2022.3217928http://doi.org/10.1109/TMM.2022.3217928

Views

316

Downloads

CSCD

Alert me when the article has been cited

Submit

Tools

Publicity Resources

No data

Related Author

No data

Related Institution

No data

Address：Zhejiang University Press, 148 Tianmushan Road, Hangzhou, China Postal code：310028
Tel：+86-571-88273162 Email：fitee@zju.edu.cn
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰