论文标题
毫米波MIMO通道估计的顺序子空间方法
A Sequential Subspace Method for Millimeter Wave MIMO Channel Estimation
论文作者
论文摘要
第五代无线网络中MMWave上的数据传输旨在支持非常高速的无线通信。通过使用先进的混合预编码，可以实现MMWave传输的频谱效率的大幅提高，而准确的通道状态信息是关键。与其估算整个通道矩阵，不如直接估计包含较少参数的子空间信息，而是具有足够的信息来设计收发器。但是，现有通道子空间估计技术中的大信道使用开销和相关的计算复杂性是部署子空间方法进行通道估计的主要障碍。在本文中，我们提出了一种顺序的两阶段子空间估计方法，该方法可以解决高架问题并提供准确的子空间信息。利用顺序方法使我们能够避免操纵整个高维训练信号，从而大大降低了复杂性。具体而言，在第一阶段，提出的方法采样了通道矩阵的列来估计其列子空间。然后，基于获得的列子空间，它优化了训练信号以估计行子空间。对于具有$ n_r $的频道接收天线和$ n_t $传输天线，我们的分析表明，所提出的技术仅需要$ o（n_t）$ channel使用，同时提供子空间估计精度的保证。通过理论分析，结果表明，估计的子空间和真实子空间之间的相似性与信噪比（SNR）（即$ o（\ text {snr}）$，高snr处于线性关系，而$ o（\ text {snr}）$，同时与SNR四倍有关仿真结果表明，所提出的顺序子空间方法可以提供提高的子空间精度，归一化平方误差以及对现有方法的频谱效率。
Data transmission over the mmWave in fifth-generation wireless networks aims to support very high speed wireless communications. A substantial increase in spectrum efficiency for mmWave transmission can be achieved by using advanced hybrid precoding, for which accurate channel state information is the key. Rather than estimating the entire channel matrix, directly estimating subspace information, which contains fewer parameters, does have enough information to design transceivers. However, the large channel use overhead and associated computational complexity in the existing channel subspace estimation techniques are major obstacles to deploy the subspace approach for channel estimation. In this paper, we propose a sequential two-stage subspace estimation method that can resolve the overhead issues and provide accurate subspace information. Utilizing a sequential method enables us to avoid manipulating the entire high-dimensional training signal, which greatly reduces the complexity. Specifically, in the first stage, the proposed method samples the columns of channel matrix to estimate its column subspace. Then, based on the obtained column subspace, it optimizes the training signals to estimate the row subspace. For a channel with $N_r$ receive antennas and $N_t$ transmit antennas, our analysis shows that the proposed technique only requires $O(N_t)$ channel uses, while providing a guarantee of subspace estimation accuracy. By theoretical analysis, it is shown that the similarity between the estimated subspace and the true subspace is linearly related to the signal-to-noise ratio (SNR), i.e., $O(\text{SNR})$, at high SNR, while quadratically related to the SNR, i.e., $O(\text{SNR}^2)$, at low SNR. Simulation results show that the proposed sequential subspace method can provide improved subspace accuracy, normalized mean squared error, and spectrum efficiency over existing methods.
