Blind Integrated Sensing Algorithm for Near Field Communication Using Bayesian Method
袁正道 1崔建华 2刘飞 3孙鹏 3王忠勇3
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作者信息
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
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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 pro-posed 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 com-munication.In addition,since adopting differential modulation,the proposed algorithm can realizes blind ISAC without pi-lot 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
近场通信/通信感知一体化/非均匀网格模型/稀疏估计/贝叶斯方法
Key words
near field communication/integrated sensing and communication/non-uniform meshing model/sparse estimation/Bayesian method
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