Blind Integrated Sensing Algorithm for Near Field Communication Using Bayesian Method

YUAN Zheng-dao; CUI Jian-hua; LIU Fei; SUN Peng; WANG Zhong-yong

doi:10.12263/DZXB.20230975

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Blind Integrated Sensing Algorithm for Near Field Communication Using Bayesian Method

PAPERS | 更新时间：2026-05-07

- Blind Integrated Sensing Algorithm for Near Field Communication Using Bayesian Method
- ACTA ELECTRONICA SINICA Vol. 52, Issue 10, Pages: 3507-3516(2024)
- 作者机构：
  
  1.河南开放大学信息工程与人工智能学院，河南郑州 450008
  2.洛阳师范学院物理与电子信息学院，河南洛阳 471934
  3.郑州大学电气与信息工程学院，河南郑州 450001
- 作者简介：
- 基金信息：
  
  National Key Research and Development Program of China(2019QY0302);National Natural Science Foundation of China(61901417);Science and Technology Research Project of Henan Province(222102210181);Young Backbone Teachers Training Program of Henan Province(2020GGJS195);Young Backbone Teachers Training Program of Luoyang Normal University(2019XJGGJS-04)
- DOI：10.12263/DZXB.20230975
  CLC： TN929.51;TN911.72
- Received：19 October 2023，
  
  Revised：2024-04-25，
  
  Published：25 October 2024
- 稿件说明：
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袁正道, 崔建华, 刘飞, 等. 应用贝叶斯模型的盲近场通信感知一体化算法[J]. 电子学报, 2024, 52(10): 3507-3516.

YUAN Zheng-dao, CUI Jian-hua, LIU Fei, et al. Blind Integrated Sensing Algorithm for Near Field Communication Using Bayesian Method[J]. Acta Electronica Sinica, 2024, 52(10): 3507-3516.
袁正道, 崔建华, 刘飞, 等. 应用贝叶斯模型的盲近场通信感知一体化算法[J]. 电子学报, 2024, 52(10): 3507-3516. DOI：10.12263/DZXB.20230975

YUAN Zheng-dao, CUI Jian-hua, LIU Fei, et al. Blind Integrated Sensing Algorithm for Near Field Communication Using Bayesian Method[J]. Acta Electronica Sinica, 2024, 52(10): 3507-3516. DOI：10.12263/DZXB.20230975

摘要

在6G通信系统中，随着天线规模的增大，菲涅尔区逐步扩展，现有的远场通信假设会引入严重的能量扩散，即角度域不再稀疏.近场通信利用球面波前进行建模，其信道模型与用户到达基站的角度和距离相关，在通信的同时可以估计角度和距离，实现通信感知一体化（Integrated Sensing And Communication，ISAC）.本文针对近场环境下ISAC问题，提出了基于极坐标的近场模型，通过非均匀网格划分将ISAC转化为稀疏估计问题，进而提出基于稀疏贝叶斯学习模型和消息传递算法的ISAC算法，同时完成活跃用户检测、位置感知和通信.此外，所提算法采用差分调制，在通信和感知中无需利用导频，即可实现盲ISAC，有效提升通信系统的频谱效率.仿真结果表明，相对于均匀区域划分和文献现有方法，本文提出的ISAC算法可获得更高的感知精度和误码率性能.

Abstract

In 6G communication system

the Fresnel region gradually expands with the increase of the antenna size

and the existing far-field hypothesis will introduce serious energy diffusion

that is

the angle domain will no longer be sparse. Near field communication uses spherical wave front for modeling

and the channel model is related to the angle and distance from the user to the base station

which makes it possible to estimate angles and distances while communicating

enabling integrated sensing and communication (ISAC). In this paper

a near-field model based on polar coordinates is proposed to solve the ISAC problem in near-field environment. We transform ISAC into a sparse estimation problem through non-uniform meshing and then use sparse Bayesian learning models for active user detection

location awareness

and communication. In addition

since adopting differential modulation

the proposed algorithm can realizes blind ISAC without pilot frequency

and effectively improves the spectral efficiency of the communication system. Simulation results show that the proposed ISAC algorithm can achieve higher sensing accuracy and BER performance compared with the uniform region partitioning and the existing methods in the literature.

关键词

Keywords

references

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