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meisme123

Config files for my GitHub profile.

meisme123.github.io

meta-autoencoder

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

meta-autoencoder-without-channel-model

Preliminary code for the paper "End-to-End Fast Training of Communication Links Without a Channel Model via Online Meta-Learning"

meta-demodulator

Code for the paper "Learning to Demodulate from Few Pilots via Offline and Online Meta-Learning"

mi-nee

Mutual Information Neural Entropic Estimation

mine

Mutual Information Neural Estimation (Pytorch)

mine-mutual-information-neural-estimation-

A pytorch implementation of MINE(Mutual Information Neural Estimation)

mine-pytorch

Mutual Information Neural Estimation in Pytorch

mist_cnn_decoder

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

ml-and-communication-research

All important files and code to my PIR

ml-in-physical-layer

ml-receiver

ml_wirelesscomm

Machine Learning Applications in Wireless Communications - Project work

multiplayer-alphazero

PyTorch AlphaZero implementation with multiplayer support [NeurIPS 2019 Deep Reinforcement Learning Workshop]

necst

Neural Joint-Source Channel Coding

neuraghe

NEURAghe project. CNN Accelerator

neuraldemod

This is the project solution for Winter 2019 for Communication Systems at UCLA!

next-generation-5g-ofdm-based-modulations

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".

nlp_comm

Developing joint source and channel codes for transmission of text

nn_gwtc

Simulations for the paper "Deep Learning for the Gaussian Wiretap Channel" with Tensorflow 2

nonlinear-fiber-channel-detection-scheme

An Optical caommunication fiber link is simulated to implment a detection scheme that is assited by Machine learning to optimize the desicion regions for a mQAM constellation

nsc

Neural Speech Codec

optical-comm-ml

Machine learning for optical communication over a dispersive fiber

opticalfibreml

Data and code for the paper "Deep, complex, invertible networks for inversion of transmission effects in multimode optical fibres" published at NIPS 2018

osmo-tetra

Osmocom TETRA MAC/PHY experimentation code; Mirrored from git://git.osmocom.org/osmo-tetra

pa_twc

papr-net

Deep learning for PAPR reduction in OFDM system

paprnet

A Peak to Average Power Ratio (PAPR) Reduction method for OFDM Systems using neural networks using the encoder-decoder approach. [Course project for EEE 6207 Broadband Wireless Communication MSc 2019]

performmance_evaluation_of_fpga-based_ldpc_codes_for_ccsds