标题： 基于正交时延多普勒分复用的大规模MIMO系统信道估计 显示英文标题

标题： Channel Estimation in Massive MIMO Systems with Orthogonal Delay-Doppler Division Multiplexing

摘要： 正交时延-多普勒分复用（ODDM）调制最近被认为是一种有前景的技术，可以在高移动性情况下提供可靠的通信。 准确且低复杂度的信道估计是大规模多输入多输出（MIMO）ODDM系统中最关键的挑战之一，主要是由于天线阵列非常大和高移动性环境。 为克服这些挑战，本文研究了下行链路大规模MIMO-ODDM系统中的信道估计问题，并提出了一种基于记忆近似消息传递（MAMP）的低复杂度算法来估计信道状态信息（CSI）。 具体而言，我们首先建立了大规模MIMO-ODDM系统的有效信道模型，其中等效信道向量中元素的幅度服从伯努利-高斯分布。 进一步地，随着天线数量的增长，等效系数矩阵中的元素趋于完全随机。 利用这些特性，我们使用MAMP方法来确定多径信道的增益、时延和多普勒效应，而信道角度则通过离散傅里叶变换方法进行估计。 最后，数值结果表明，当天线数量趋于无穷大时，所提出的信道估计算法接近贝叶斯最优结果，并且在归一化均方误差方面相比现有算法提高了约30%的信道估计精度。 显示英文摘要

摘要： Orthogonal delay-Doppler division multiplexing~(ODDM) modulation has recently been regarded as a promising technology to provide reliable communications in high-mobility situations. Accurate and low-complexity channel estimation is one of the most critical challenges for massive multiple input multiple output~(MIMO) ODDM systems, mainly due to the extremely large antenna arrays and high-mobility environments. To overcome these challenges, this paper addresses the issue of channel estimation in downlink massive MIMO-ODDM systems and proposes a low-complexity algorithm based on memory approximate message passing~(MAMP) to estimate the channel state information~(CSI). Specifically, we first establish the effective channel model of the massive MIMO-ODDM systems, where the magnitudes of the elements in the equivalent channel vector follow a Bernoulli-Gaussian distribution. Further, as the number of antennas grows, the elements in the equivalent coefficient matrix tend to become completely random. Leveraging these characteristics, we utilize the MAMP method to determine the gains, delays, and Doppler effects of the multi-path channel, while the channel angles are estimated through the discrete Fourier transform method. Finally, numerical results show that the proposed channel estimation algorithm approaches the Bayesian optimal results when the number of antennas tends to infinity and improves the channel estimation accuracy by about 30% compared with the existing algorithms in terms of the normalized mean square error.