Watch zhoub86/RNNASIP

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Watch zhoub86/RNN-Conv-Decoder

Accompanying code of paper "On Recurrent Neural Networks for Sequence-based Processing in Communications" by Daniel Tandler, Sebastian Dörner, Sebastian Cammerer, Stephan ten Brink.

Watch zhoub86/gr-wavelearner

Perform deep learning inference on signals

Watch zhoub86/multiplayer-alphazero

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

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Mutual Information Neural Entropic Estimation

Watch zhoub86/codec2

Open source speech codec designed for communications quality speech between 450 and 3200 bit/s. The main application is low bandwidth HF/VHF digital radio.

Watch zhoub86/rate-splitting-multiple-access-for-downlink-communication-systems

RSMA relies on linearly precoded rate-splitting with SIC to decode part of the interference and treat the remaining part of the interference as noise.

Watch zhoub86/Comparison_of_communication_Autoencoder_MI_GAN_WGAN

Master Thesis compairing Communicationsystems Baised on GAN and baised on MI

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