Trainable Communication Systems: Concepts and Prototype

29 Nov 2019Sebastian CammererFayçal Ait AoudiaSebastian DörnerMaximilian StarkJakob HoydisStephan ten Brink

We consider a trainable point-to-point communication system, where both transmitter and receiver are implemented as neural networks (NNs), and demonstrate that training on the bit-wise mutual information (BMI) allows seamless integration with practical bit-metric decoding (BMD) receivers, as well as joint optimization of constellation shaping and labeling. Moreover, we present a fully differentiable neural iterative demapping and decoding (IDD) structure which achieves significant gains on additive white Gaussian noise (AWGN) channels using a standard 802.11n low-density parity-check (LDPC) code... (read more)

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