• Title/Summary/Keyword: Massive matrix decomposition

Search Result 4, Processing Time 0.017 seconds

Hybrid combiner design for downlink massive MIMO systems

  • Seo, Bangwon
    • ETRI Journal
    • /
    • v.42 no.3
    • /
    • pp.333-340
    • /
    • 2020
  • We consider a hybrid combiner design for downlink massive multiple-input multiple-output systems when there is residual inter-user interference and each user is equipped with a limited number of radio frequency (RF) chains (less than the number of receive antennas). We propose a hybrid combiner that minimizes the mean-squared error (MSE) between the information symbols and the ones estimated with a constant amplitude constraint on the RF combiner. In the proposed scheme, an iterative alternating optimization method is utilized. At each iteration, one of the analog RF and digital baseband combining matrices is updated to minimize the MSE by fixing the other matrix without considering the constant amplitude constraint. Then, the other matrix is updated by changing the roles of the two matrices. Each element in the RF combining matrix is obtained from the phase component of the solution matrix of the optimization problem for the RF combining matrix. Simulation results show that the proposed scheme performs better than conventional matrix-decomposition schemes.

Low Complexity Hybrid Precoding in Millimeter Wave Massive MIMO Systems

  • Cheng, Tongtong;He, Yigang;Wu, Yuting;Ning, Shuguang;Sui, Yongbo;Huang, Yuan
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • v.16 no.4
    • /
    • pp.1330-1350
    • /
    • 2022
  • As a preprocessing operation of transmitter antennas, the hybrid precoding is restricted by the limited computing resources of the transmitter. Therefore, this paper proposes a novel hybrid precoding that guarantees the communication efficiency with low complexity and a fast computational speed. First, the analog and digital precoding matrix is derived from the maximum eigenvectors of the channel matrix in the sub-connected architecture to maximize the communication rate. Second, the extended power iteration (EPI) is utilized to obtain the maximum eigenvalues and their eigenvectors of the channel matrix, which reduces the computational complexity caused by the singular value decomposition (SVD). Third, the Aitken acceleration method is utilized to further improve the convergence rate of the EPI algorithm. Finally, the hybrid precoding based on the EPI method and the Aitken acceleration algorithm is evaluated in millimeter-wave (mmWave) massive multiple-input and multiple-output (MIMO) systems. The experimental results show that the proposed method can reduce the computational complexity with the high performance in mmWave massive MIMO systems. The method has the wide application prospect in future wireless communication systems.

An Analysis Method of Large Structure Using Matrix Blocking (블록화기법을 이용한 대형구조물의 해석방법)

  • Jung, Sung-Jin;Lee, Min-Sup
    • Journal of the Korea institute for structural maintenance and inspection
    • /
    • v.18 no.2
    • /
    • pp.30-37
    • /
    • 2014
  • In this study, we studied how to perform the structural analysis which need a large-capacity flash memory with the computer program when the flash memory storage of a personal computer has no enough room for the analysis of structure. As one of the solutions of this problem, the blocking method of stiffness matrix, which is a method that stiffness matrix is divided by a few blocks and each block is sequentially used for the calculation of matrix decomposition, is proposed and an algorithm available in computer program is derived on the method. Finally, A structural analysis program (sNs) based on this study is developed and the correctness and efficiency of the algorithm is founded through some examples which are fundamental in structural analysis.

A Simple Toeplitz Channel Matrix Decomposition with Vectorization Technique for Large scaled MIMO System (벡터화 기술을 이용한 대규모 MIMO 시스템의 간단한 Toeplitz 채널 행렬 분해)

  • Park, Ju Yong;Hanif, Mohammad Abu;Kim, Jeong Su;Song, Sang Seob;Lee, Moon Ho
    • Journal of the Institute of Electronics and Information Engineers
    • /
    • v.51 no.9
    • /
    • pp.21-29
    • /
    • 2014
  • Due to enormous number of user and limited memory space, the memory saving is become an important issue for big data service these days. In the large scaled multiple-input multiple-output (MIMO) system, the Teoplitz channel can play the significance rule to improve the performance as well as power efficiency. In this paper, we propose a Toeplitz channel decomposition based on matrix vectorization. Here we use Toeplitz matrix to the channel for large scaled MIMO system. And we show that the Toeplitz Jacket matrices are decomposed to Cooley-Tukey sparse matrices like fast Fourier transform (FFT).