Waveform Learning under Phase Noise Impairment for Sub-THz Communications
Dileepa Marasinghe, Le Hang Nguyen, Jafar Mohammadi, Yejian Chen,, Thorsten Wild, Nandana Rajatheva

TL;DR
This paper proposes a waveform design for sub-THz communications that is robust to phase noise and low PAPR, utilizing geometric constellation shaping, pulse filtering, and neural network demappers to improve performance under hardware impairments.
Contribution
It introduces a novel waveform optimization method that incorporates phase noise robustness, PAPR reduction, and neural network demappers for enhanced sub-THz communication performance.
Findings
Achieves up to 2.5 dB reduction in Eb/N0 under strong phase noise.
Reduces PAPR by up to 1.2 dB while maintaining BLER and spectral efficiency.
Demonstrates robustness of the proposed waveform in practical PN scenarios.
Abstract
The large untapped spectrum in sub-THz allows for ultra-high throughput communication to realize many seemingly impossible applications in 6G. Phase noise (PN) is one key hardware impairment, which is accentuated as we increase the frequency and bandwidth. Furthermore, the modest output power of the power amplifier demands limits on peak to average power ratio (PAPR) signal design. In this work, we design a PN-robust, low PAPR single-carrier (SC) waveform by geometrically shaping the constellation and adapting the pulse shaping filter pair under practical PN modeling and adjacent channel leakage ratio (ACLR) constraints for a given excess bandwidth. We optimize the waveforms under conventional and state-of-the-art PN-aware demappers. Moreover, we introduce a neural-network (NN) demapper enhancing transceiver adaptability. We formulate the waveform optimization problem in its augmented…
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Taxonomy
TopicsPhotonic and Optical Devices · Advanced Photonic Communication Systems · Radio Frequency Integrated Circuit Design
