Waveform Learning for Next-Generation Wireless Communication Systems

TL;DR

This paper introduces a learning-based approach for designing waveforms and neural detectors in wireless systems, optimizing information rate while controlling spectral leakage and power, outperforming traditional methods in various channel conditions.

Contribution

It presents a novel joint waveform and detector design method that maximizes information rate with constraints on ACLR and PAPR, suitable for beyond-5G systems.

Findings

Significant reduction in ACLR and PAPR compared to traditional QAM and RRC.

Achieves higher or comparable data rates on multipath channels.

No additional complexity on the transmitter side.

Abstract

We propose a learning-based method for the joint design of a transmit and receive filter, the constellation geometry and associated bit labeling, as well as a neural network (NN)-based detector. The method maximizes an achievable information rate, while simultaneously satisfying constraints on the adjacent channel leakage ratio (ACLR) and peak-to-average power ratio (PAPR). This allows control of the tradeoff between spectral containment, peak power, and communication rate. Evaluation on an additive white Gaussian noise (AWGN) channel shows significant reduction of ACLR and PAPR compared to a conventional baseline relying on quadrature amplitude modulation (QAM) and root-raised-cosine (RRC), without significant loss of information rate. When considering a 3rd Generation Partnership Project (3GPP) multipath channel, the learned waveform and neural receiver enable competitive or higher rates than an orthogonal frequency division multiplexing (OFDM) baseline, while reducing the ACLR by 10 dB and the PAPR by 2 dB. The proposed method incurs no additional complexity on the transmitter side and might be an attractive tool for waveform design of beyond-5G systems.