基于智能反射面辅助矿井通信系统信道估计

A Channel Estimation for Intelligent Reflecting Surface Aided Mine Wireless Communications

针对矿井下复杂、随机的无线信道特性所导致的信道估计准确度低的问题,结合智能反射面IRS技术,提出了井下IRS辅助多用户通信系统模型,通过优化传输路径、重新配置无线传输环境,提高井下信道估计准确度。首先,结合IRS技术,建立了井下IRS辅助多用户信号传输模型,基于该模型推导了平行因子分解信道估计算法,并仿真了该算法在IRS辅助矿井通信系统中的性能。仿真结果表明,与传统的最小二乘(LS)算法和正交匹配追踪算法相比,在归一化均方误差为10^(-2)时,PARAFAC分解算法信噪比可降低约8 dB,且算法执行时间略小于LS算法。

Aimed at the problems that mine wireless communications are low-accuracy in channel estimation caused by complicated and random characteristics, a multi-user mine communication system assisted by intelligent reflecting surface (IRS) technique is proposed in this paper to improve the accuracy of channel estimation by optimizing the transmission path and reassigning the wireless transmission environment. First, in combination with IRS technique, a model of the IRS-assisted multi-user information transmission in the mine is established. And then, the parallel factor (PARAFAC) decomposition channel estimation algorithm is derived, and the performances of PARAFAC algorithm in the IRS-assisted mine wireless communications is simulated based on the model. The simulation results show that compared with the traditional least-square (LS) algorithm and orthogonal matching pursuit algorithm, the proposed PARAFAC algorithm can obtain about 8 dB signal-to-noise ratio (SNR) gain at the same normalized mean-square error 10^(-2) while the execution time is slightly less than that of LS algorithm.