An Introduction to Deep Learning for the Physical Layer
radio-transformer-networks
An Introduction to Deep Learning for the Physical Layer
An usable PyTorch implementation of the noisy autoencoder infrastructure in the paper “An Introduction to Deep Learning for the Physical Layer” by Kenta Iwasaki on behalf of Gram.AI.
Overall a fun experiment for constructing a communications system for the physical layer with transmitters/receivers in which the transmitter efficiently encodes a signal in a way such that the receiver can still, with minimal error, decode this encoded signal despite being inflicted with noise in amidst transmission.
The signal dimension for the encoded message is set to be 4, with the compressed signal representation’s channel size being 2 (log_2(signal_dim)) to maximize information/bit as a basis to the principles of shannon entropy.
The signal-to-noise ratio simulated in amidst training