This is my attempt to reproduce and extend the results in the paper "An Introduction to Deep Learning for the Physical Layer" by Tim O'Shea and Jakob Hoydis

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

Partly implemented paper "An Introduction to Deep Learning for the Physical Layer"

Implement the autoencoder referred to the paper 'An introduction to deep learning for the physical layer'

This code implements AAIF, a robust physical layer authentication scheme for IIoT that combines Autoencoders and Adaptive Isolation Forest for zero-knowledge attacker detection.

An experiment to discover a novel Physical Layer (PHY) for VHF/UHF Ham Radio using End-to-End Deep Learning (Autoencoders). Target sub-noise floor performance (negative SNR) and high resilience to terrestrial impairments.

A two-stage low-power FPGA architecture for AI-based physical-layer anomaly detection in EV charging systems using dv/dt triage and autoencoder-driven deep inspection.