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a-method-for-guaranteeing-wireless-communication-based-on-a-combination-of-deep-and-shallow-learnin

A Method for Guaranteeing Wireless Communication Based on a Combination of Deep and Shallow Learning

auto-sync

use deep learning to auto-sync in communication

autoencoder-based-communication-system

Tensorflow Implementation and result of Auto-encoder Based Communication System From Research Paper : "An Introduction to Deep Learning for the Physical Layer" http://ieeexplore.ieee.org/document/8054694/

autoencoder-for-the-physical-layer

Using Keras to validate the simulation results according to Paper : "An Introduction to Deep Learning for the Physical Layer"

awesome-deep-learning-for-wireless-communication

2018-2019年最新深度学习用于无线通信（物理层）的论文整理，附论文核心**总结与代码分析。（中文）/ A collection of latest papers for wireless communication based on Deep Learning (intelligent communication), with some own understanding and codes analysis.

ch_est_data_det_1-bit

This is a collection of MATLAB scripts to generate numerical results for the paper "Channel Estimation and Data Detection Analysis of Massive MIMO with 1-Bit ADCs," IEEE Trans. Wireless Commun. (to appear), 2021

channel-estimation

Channel estimations based on RLS, LMS and ML methods.

channelnet

Implementation of the paper "Deep Learning-Based Channel Estimation"

communication-using-cnn

Digital Communication using deep learning

cooperative-multi-agent-reinforcement-learning-for-low-level-wireless-communication

Spring 2017 Deep Reinforcement Learning Final Project

decoder-using-deep-learning

Academic project for constructing the decoder for communication using Deep learning networks - CNN architecture and ResNet Inception model architecture.

deep-learning-and-communications-systems

Related to ECE 766 term project

deep-learning-based-csi-estimation-for-5g-communication

A framework to estimate the Channel State Information for a 5G communication

deep-learning-for-the-physical-layer

PyTorch implementation for part of paper "An Introduction to Deep Learning for the Physical Layer" by Kenta Iwasaki on behalf of Gram.AI.

deep-learning-with-tensorflow-book

深度学习入门开源书，基于TensorFlow 2.0案例实战。Open source Deep Learning book, based on TensorFlow 2.0 framework.

deepmimodetection

dive-into-dl-tensorflow2.0

本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为TensorFlow 2.0实现，项目已得到李沐老师的同意

dl-channeldecoding

On Deep Learning-based Channel Decoding

dl_ofdm

Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex Convolutional Networks

dnn-fading-channel

Autoencoder PHY for fading channel

gnu-generated-i-q-data-of-digital-modulation-techniques-using-matlab

The use of USRP radio, GNU radios, and Software Defined Radios to transmit and collect raw IQ (In-phase and Quadrature) data of the BPSK, QPSK, 8PSK, 16QAM, and 32QAM modulation schemes prior to developing the Machine learning and Neural Network classification models. The data collection task was conducted at San Jose State University Communications Lab, and can be used by people looking to work on RF and Machine Learning projects.

iterative-bp-cnn

The codes reproduce the research of our work in our JSTSP paper "An Iterative BP-CNN Architecture for Channel Decoding under Correlated Noise"

jianan_machine_learning

machine learning present by jianan

large-scale-fading-decoding

Simulation code for “Large-Scale-Fading Decoding in Cellular Massive MIMO Systems with Spatially Correlated Channels,” by Trinh Van Chien, Christopher Mollén, and Emil Björnson, IEEE Transactions on Communications, vol. 67, no. 4, pp. 2746-2762, April 2019.

master-thesis

Source Code to my master's thesis with the topic "End-to-end optimisation of MIMO systems using deep learning autoencoders"

meta-autoencoder

Code for the paper "Meta-Learning to Communicate: Fast End-to-End Training for Fading Channels"

mimo_ofdm

《MIMO-OFDM无线通信技术及MATLAB实现》随书源码

mist_cnn_decoder

MIST: A Novel Training Strategy for Low-latency Scalable Neural Net Decoders

mmsp

Research Proposal - Deep Reinforcement Learning based Resource Management for Cellular Vehicle-to-Vehicle Communication(C-V2V)

multipath_simulation

:signal_strength: Using Python to simulate multipath fading channel