| 冯雪林* ** ***,石晶林* **,戴曼*,刘林*.基于相关矩阵Toeplitz特性的5G终端基带信道估计算法设计[J].高技术通讯(中文),2025,35(1):9~19 |
| 基于相关矩阵Toeplitz特性的5G终端基带信道估计算法设计 |
| A novel channel estimation algorithm based on the Toeplitz correlation matrices for 5G terminal baseband processing |
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| DOI：10. 3772 / j. issn. 1002-0470. 2025. 01. 002 |
| 中文关键词: 5G 终端基带; 信道估计; 均方根; 托普利兹; 动态范围 |
| 英文关键词: 5G terminal baseband processing, channel estimation, root mean-square (RMS), Toeplitz, dynamic range |
| 基金项目: |
| 作者 | 单位 | | 冯雪林* ** *** | (* 中国科学院计算技术研究所移动计算与新型终端北京市重点实验室 北京 100080)
(** 中国科学院大学 北京 100049)
(*** 北京中科晶上科技股份有限公司 北京 100190) | | 石晶林* ** | | | 戴曼* | | | 刘林* | |
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| 中文摘要: |
| 第五代移动通信技术(5th-generation mobile communication technology,5G)网络对高速率、低时延、高可靠性的移动通信处理需求不断增加,对终端基带信道估计算法的高性能和低复杂度设计、矩阵处理动态范围提出挑战。 针对上述问题,本文提出一种基于相关矩阵托普利兹(Toeplitz)特性的信道估计算法。依据信道的相干带宽特性计算信道相关矩阵并保留必要的较低矩阵阶数;基于相关矩阵的 Toeplitz 特性设计低复杂度的递归求逆算法,并针对加权矩阵乘法的元素重复性将矩阵乘法化简为矩阵点乘,简化加权矩阵运算;同时引入跟踪信噪比变化的缩放补偿因子对计算过程和结果分别进行缩放和补偿。 理论分析和仿真结果显示,本文所提算法可在达到优异的信道估计性能条件下,有效降低运算复杂度,并极大降低算法矩阵处理的动态范围。 |
| 英文摘要: |
| The increasing demand for high-speed, low-latency, and high-reliability mobile communication processing in 5-th generation mobile communication technology (5G) networks poses challenges to the design of high-performance and low-complexity for terminal baseband channel estimation algorithms, as well as to the dynamic range of matrix processing. To solve this problem, a channel estimation algorithm based on the Toeplitz feature of channel correlation matrix is proposed. Calculate the channel correlation matrix according to the coherent bandwidth characteristics of the channel and retain the necessary lower matrix order. Design a low-complexity recursive matrix inversion algorithm based on the Toeplitz property of the correlation matrix. In view of the element repetition in the weighted matrix multiplication, simplify the matrix multiplication to element-wise multiplication to streamline the weighted matrix operation. Furthermore, a factor tracking the change of the signal-to-noise ratio is introduced to scale and compensate the process and the result of matrix operation respectively. Theoretical analysis and experimental results indicate that the proposed algorithm not only reduces the overall computational resources consumption, but also dramatically reduces the dynamic range of matrix-processing, while keeping perfect channel estimation performance. |
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