Machine learning for optical communication over a dispersive fiber
Simulates pruned DFT spread FBMC and compares the performance to OFDM, SC-FDMA and conventional FBMC. The included classes (QAM, DoublySelectiveChannel, OFDM, FBMC) can be reused in other projects.
Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex Convolutional Networks
Autoencoder PHY for fading channel
Implementation of research paper: End-to-End Learning of Communications Systems Without a Channel Model
Compilation of the different MATLAB codes that were used for the experimental part of the research work presented in the article "Next Generation 5G OFDM-Based Modulations for Intensity Modulation-Direct Detection (IM-DD) Optical Fronthauling".
A framework to estimate the Channel State Information for a 5G communication
Conditional GAN based End-to-End Communication System
Deep learning for PAPR reduction in OFDM system
本代码给出了一种基于移相器网络实现的FBMC原理仿真源代码。由于OFDM固有的一些缺点,比如:要求严格的时间频率同步等,许多新型多载波调制技术在5G的研究中也层出不穷。FBMC是基于滤波器组的多载波调制方法中的一种。通过每个子载波上优化的滤波器设计,FBMC可以很好的抑制旁瓣泄露。为了保证系统的传输速率,需要在每个子信道上进行复数传输,但是相邻子信道上承载的复数数据之间会存在干扰,因此为了消除干扰需要使用OQAM调制。另外实现时通常使用PPN-FFT结构,以便降低复杂度。
Compares FBMC to OFDM based schemes. Reproduces all figures from “Filter bank multicarrier modulation schemes for future mobile communications”, IEEE Journal on Selected Areas in Communications, 2017.
Measure the performance of three modulation techniques using PAPR, spectral efficiency, BER
学之思开源考试系统 - mysql版,支持多种题型:选择题、多选题、判断题、填空题、解答题以及数学公式,包含PC端、小程序端,扩展性强,部署方便(集成部署、前后端分离部署、docker部署)、界面设计友好、代码结构清晰
基于Spring Boot的迷你天猫商城,快速部署运行,所用技术:Spring Boot/MySQL/Druid/Log4j2/Maven/Echarts/Bootstrap