End-to-end Waveform Learning Through Joint Optimization of Pulse and Constellation Shaping
This work addresses the problem of waveform design for communication systems enabling new services like joint communication and sensing, but it is incremental as it builds on existing learning methods for optimization.
The paper tackled the challenge of designing waveforms for emerging communication applications by proposing an end-to-end learning approach that jointly optimizes pulse shaping and constellation geometry with a neural network-based receiver, resulting in up to orders of magnitude smaller adjacent channel leakage ratios (ACLRs) and competitive peak-to-average power ratios (PAPRs) without significant loss of information rate on an AWGN channel.
As communication systems are foreseen to enable new services such as joint communication and sensing and utilize parts of the sub-THz spectrum, the design of novel waveforms that can support these emerging applications becomes increasingly challenging. We present in this work an end-to-end learning approach to design waveforms through joint learning of pulse shaping and constellation geometry, together with a neural network (NN)-based receiver. Optimization is performed to maximize an achievable information rate, while satisfying constraints on out-of-band emission and power envelope. Our results show that the proposed approach enables up to orders of magnitude smaller adjacent channel leakage ratios (ACLRs) with peak-to-average power ratios (PAPRs) competitive with traditional filters, without significant loss of information rate on an additive white Gaussian noise (AWGN) channel, and no additional complexity at the transmitter.