Zak-OTFS: Pulse Shaping and the Tradeoff between Time/Bandwidth Expansion and Predictability
<|reference_start|>Zak-OTFS: Pulse Shaping and the Tradeoff between Time/Bandwidth Expansion and Predictability: The Zak-OTFS input/output (I/O) relation is predictable and non-fading when the delay and Doppler periods are greater than the effective channel delay and Doppler spreads, a condition which we refer to as the crystallization condition. When the crystallization condition is satisfied, we describe how to integrate sensing and communication within a single Zak-OTFS subframe by transmitting a pilot in the center of the subframe and surrounding the pilot with a pilot region and guard band to mitigate interference between data symbols and pilot. At the receiver we first read off the effective channel taps within the pilot region, and then use the estimated channel taps to recover the data from the symbols received outside the pilot region. We introduce a framework for filter design in the delay-Doppler (DD) domain where the symplectic Fourier transform connects aliasing in the DD domain (predictability of the I/O relation) with time/bandwidth expansion. The choice of pulse shaping filter determines the fraction of pilot energy that lies outside the pilot region and the degradation in BER performance that results from the interference to data symbols. We demonstrate that Gaussian filters in the DD domain provide significant improvements in BER performance over the sinc and root raised cosine filters considered in previous work. We also demonstrate that, by limiting DD domain aliasing, Gaussian filters extend the region where the crystallization condition is satisfied. The Gaussian filters considered in this paper are a particular case of factorizable pulse shaping filters in the DD domain, and this family of filters may be of independent interest.<|reference_end|>
@article{jayachandran2024zak-otfs:,
    title={Zak-OTFS: Pulse Shaping and the Tradeoff between Time/Bandwidth
  Expansion and Predictability},
    author={Jinu Jayachandran, Rahul Kumar Jaiswal, Saif Khan Mohammed, Ronny
  Hadani, Ananthanarayanan Chockalingam and Robert Calderbank},
    journal={arXiv preprint arXiv:2405.02718},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.02718},
    primaryClass={eess.SP cs.IT math.IT}
}