With research on the technological components of the future 6G wireless communication standard in full swing, the possibilities of an AI-native air interface are also being explored. In collaboration with NVIDIA, Rohde & Schwarz takes another step forward and presents an enhancement to its recent hardware-in-the-loop demonstration of a neural receiver, showing the achievable performance gains when using trained AI/ML models compared to traditional signal processing – while also optimizing the transmitter side and for the first time taking hardware impairments into account. Instead of relying on well-known, symmetric constellations such as QPSK or QAM modulations, the constellation points are determined in an end-to-end learning process, which jointly optimizes the neural receiver and the constellation mapper of the transmitter while taking the faded mobile radio channel and carrier frequency offsets into account.