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Downlink MU-MIMO With THP Combined With Pre- and Post-processing and Selection of the Processing Vectors for Maximization of Per-Stream SNR

机译:结合THP的下行MU-MIMO与预处理和后处理相结合,并选择处理矢量以最大化每流SNR

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摘要

In this paper, we consider a downlink multiuser multiple-input–multiple-output (MU-MIMO) system with multiple antennas at the transmitter and multiple antennas at each user, where the transmitter can send one or more data streams to each user. We propose a non-iterative method by combining Tomlinson–Harashima precoding (THP) with pre- and post-processing and selecting processing vectors based on the maximization of instantaneous signal-to-noise ratio (SNR) of the data stream at the input of the detector of each user. The postprocessing vectors for all users are found to be eigenvectors corresponding to the maximum eigenvalue of a certain matrix involving the channel matrix of the user. The transmitter computes the vectors of the linear processing matrix through an orthonormalization procedure in a single step. The feedback matrix at the transmitter is then obtained from the effective channel matrix and the linear processing matrix. We express the instantaneous SNR of all data streams in terms of eigenvalues of a Wishart matrix, obtained from the channel matrix of the user, and find the diversity order for each data stream. Considering multiple scenarios, we find the outage probability of the instantaneous SNR for all data streams and the cumulative distribution function (cdf) of the sum-rate capacity for all users using the proposed method and compare the results with those of recently proposed methods that provide a closed-form solution and with that of block diagonalization, channel inversion, and THP in scenarios where they are applicable.
机译:在本文中,我们考虑一种下行链路多用户多输入多输出(MU-MIMO)系统,该系统在发射机处具有多个天线,在每个用户处具有多个天线,其中发射机可以向每个用户发送一个或多个数据流。我们提出了一种非迭代方法,将Tomlinson-Harashima预编码(THP)与预处理和后处理结合起来,并根据输入端数据流的瞬时信噪比(SNR)的最大化来选择处理向量。每个用户的检测器。发现所有用户的后处理向量是对应于涉及用户信道矩阵的某个矩阵的最大特征值的特征向量。发送器通过正交归一化过程在单个步骤中计算线性处理矩阵的向量。然后,从有效信道矩阵和线性处理矩阵中获得发射机的反馈矩阵。我们根据从用户的信道矩阵获得的Wishart矩阵的特征值来表示所有数据流的瞬时SNR,并找到每个数据流的分集阶数。考虑多种情​​况,我们使用提出的方法找到所有数据流的瞬时SNR中断概率以及所有用户的总速率容量的累积分布函数(cdf),并将结果与​​最新提出的方法进行比较一个封闭形式的解决方案,并在适用的情况下提供块对角化,通道反转和THP解决方案。

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