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zhoub86/Deep-Learning-Based-Radio-Signal-Classification

Final Project for AI Wireless

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zhoub86/Sequential-RNN-Decoder

Sequential RNN Decoder Code for 'Communication Algorithms via Deep Learning' ICLR paper

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zhoub86/RadioCNN

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zhoub86/Cooperative-Multi-Agent-Reinforcement-Learning-for-Low-Level-Wireless-Communi...

Spring 2017 Deep Reinforcement Learning Final Project

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zhoub86/FBMC-1

FBMC & OFDM /SC-FDMA Transceiver Simulator

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zhoub86/SatCom_Sim

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zhoub86/deeprm

Resource Management with Deep Reinforcement Learning (HotNets '16)

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zhoub86/FBMC_UFMC_OFDM_5G

Measure the performance of three modulation techniques using PAPR, spectral efficiency, BER

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zhoub86/SC-FDMA

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zhoub86/Impact-of-Phase-Noise-on-OFDM

UCLA_EE 209AS_SPRING 2017_Project

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zhoub86/DNN_WMMSE

Code to reproduce our works on DNN research.

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zhoub86/entropy

Entropy estimator

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zhoub86/pg-ofdm-distortion

UFES-PG: Harmonic distortion in OFDM signals for IM-DD systems

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zhoub86/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/

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zhoub86/Optical-Comm-ML

Machine learning for optical communication over a dispersive fiber

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zhoub86/ML-in-physical-layer

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zhoub86/FBMC

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.

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zhoub86/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"

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zhoub86/DensityEstimationML.jl

A neural network based method for estimation continuous conditional distributions

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zhoub86/ECE257B_OFDM_NL

ECE257B (Sp2018) : Reception of Nonlinearly Distorted Multicarrier Signals