Machine Learning for Communications
Can we "learn" to communicate?
Inspired by Shannon’s groundbreaking work on the fundamental limits of communications, we seek to solve the task of “reproducing at one point either exactly or approximately a message selected at another point”. In other words, we seek to reliably transmit a message from a source to a destination over a channel using a transmitter and a receiver.
We propose a thinking-outside-the-box solution where we reinterpret transceiver signal-processing blocks (e.g., quantization, error correcting coding, modulation, detection) as neural networks, enabling data-driven communications systems that perpetually learn and adapt to any environment.
We built the world’s first over-the-air communications system that consists solely of neural networks. In this spirit, we propose one holistic block that does it all using joint signal-processing for detection, synchronization and decoding so that the information is completely transmitted within a single sequence (waveform).