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zhoub86/Pruned-DFT-s-FBMC_Python

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.

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zhoub86/dl_ofdm

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

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zhoub86/opticalfibreml

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

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zhoub86/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]

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zhoub86/nlp_comm

Developing joint source and channel codes for transmission of text

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zhoub86/SpLPAS

Sparse Linear Predictive Analysis-by-Synthesis for Speech and Audio Coding

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zhoub86/BPCNN

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zhoub86/DNN_short_block_comms

Redesigning Communication systems with short block lengths using DNNs

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zhoub86/ML-Receiver

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zhoub86/polar-coded-SCMA

Matlab simulation code for uplink polar coded SCMA system. "Z. Pan, E. Li, L. Wen, J. Lei, and C. Tang, “Joint iterative detection and decoding receiver for polar coded SCMA system,” in 2018 IEEE International Conference on Communications Workshops (ICC Workshops), May 2018, pp. 1–6."

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zhoub86/ESN_MIMO_v1

Ref: S. Mosleh, L. Liu, C. Sahin, Y. R. Zheng and Y. Yi, "Brain-Inspired Wireless Communications: Where Reservoir Computing Meets MIMO-OFDM," in IEEE Transactions on Neural Networks and Learning Systems.

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zhoub86/AutoencoderFiber

This shows how to use Autoencoders for learning constellations and receivers in fiber optical communications

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zhoub86/Machine-Learning-for-Signal-Processing

This repository consists of work done in Machine Learning and Signal Processing. Machine Learning Stage consists of: * K-means * Expectation Maximization * Principal Component Analysis (PCA) * Mixture Models * Hidden Markov Models (HMM) * Graphical Models * Gibbs Sampling * Manifold Learning * Hashing Signal Processing Stage consists of : * Source Separation * Stereo Matching * Audio Processing * Fourier Transform * Brain Waves * Keyword Detection * Sentiment Analysis * Music Signal Processing * Image Segmentation

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zhoub86/Paper-with-Code-of-Wireless-communication-Based-on-DL

无线与深度学习结合的论文代码整理/Wireless based on deep learning papers' code

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zhoub86/deep_complex_networks

Implementation related to the Deep Complex Networks

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zhoub86/JointSCCoding

This repository contains code for a joint source-channel coding architecture designed to transmit text data over wireless, noisy channels.

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zhoub86/MIST_CNN_Decoder

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

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zhoub86/Capacity-Estimation-on-the-Mathworks-5G-NR-CDL-Model

Single-link channel capacity estimation on the microwave and millimetre wave frequencies by using the Mathworks 5G NR CDL model for NLOS.

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zhoub86/gan_distribution_tutorial

A simple tutorial of training GAN to model distribution

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zhoub86/ResourceAllocationReinforcementLearning

intial version