Spatio-Temporal Representation With Deep Neural Recurrent Network in MIMO CSI Feedback简记 文章目录 Spatio-Temporal Representation With Deep Neural Recurrent Network in MIMO CSI Feedback简记参考简记LSTM结构深度可分
Convolutional Neural Network based Multiple-Rate Compressive Sensing for Massive MIMO CSI Feedback: Design, Simulation, and Analysis简记 文章目录 Convolutional Neural Network based Multiple-Rate Compres
文章目录 II. SYSTEM MODEL AND PROBLEM FORMULATIONC. Problem Formulation III. PROPOSED ALGORITHMA. Alternating OptimizationB. Solution for Problem (P2-m) APPENDIX II. SYSTEM MODEL AND PROBLEM FORM
[转]MIMO信道容量推导 (奇异值分解法) Massive MIMO,目前是5G的一项关键技术。那么求解它的信道容量,对于我们研究它的属性尤为重要。今天,我们就求算一下信道容量。 在通信系统中我们学到信道容量的一个计算式: C = B l o g 2 ( 1 + S N R ) C=Blog_2(1+SNR) C=Blog2(1+SNR) 其中 B B B是带宽, S N R SNR SN
前言: Beamforming 是MIMO 技术里面的核心技术之一,所以讲MIMO 必须对Beamforming 有所了解,本篇主要了解一下beamforming Explains how a beam is formed by adding delays to antenna elements. 波束赋形(Beamforming
读后感: 今天读了《On the Performance of MIMO-NOMA-Based Visible Light Communication Systems》有感如下: 摘要:在本文中,我们应用non-orthogonal multiple access(NOMA)技术去提高基于多输入多输出(MIMO)的多用户可见光通信(VLC)系统的可实现和速率。去确信在室内MIMO-NOMA-b