End-to-end Waveform Learning Through Joint Optimization of Pulse and Constellation Shaping

• URL: http://arxiv.org/abs/2106.15158v1
• Date: Tue, 29 Jun 2021 08:22:05 GMT
• Title: End-to-end Waveform Learning Through Joint Optimization of Pulse and Constellation Shaping
• Authors: Fay\c{c}al Ait Aoudia and Jakob Hoydis
• Abstract summary: Communication systems are foreseen to enable new services such as joint communication and sensing. We present in this work an end-to-end learning approach to design waveforms through joint learning of pulse shaping and constellation geometry.
• Score: 16.26230847183709
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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