Zak-OTFS and Turbo Signal Processing for Joint Sensing and Communication

TL;DR

This Letter demonstrates how to use turbo signal processing to match BER performance of this baseline system when it integrate sensing and communication within the same Zak-OTFS subframe.

Abstract

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. The filter taps can simply be read off from the response to a single Zak-OTFS pilot pulsone, and the I/O relation can be reconstructed for a sampled system that operates under finite duration and bandwidth constraints. In previous work we had measured BER performance of a baseline system where we used separate Zak-OTFS subframes for sensing and data transmission. In this Letter we demonstrate how to use turbo signal processing to match BER performance of this baseline system when we integrate sensing and communication within the same Zak-OTFS subframe. The turbo decoder alternates between channel sensing using a noise-like waveform (spread pulsone) and recovery of data transmitted using point pulsones.

Figures (3)

• Figure 1: Signal processing for proposed Zak-OTFS based iterative joint sensing and communication.
• Figure 2: Uncoded $4$-QAM BER as a function of increasing $\nu_{max}$. Veh-A channel, data SNR $=25$ dB, PDR $= 10$ dB, Doppler period $\nu_p = 30$ KHz, $M=31, N=37$, RRC pulse shaping filter ($\beta_{\nu} = \beta_{\tau} = 0.6$).
• Figure 3: Uncoded $4$-QAM BER as a function of increasing PDR. Veh-A channel considered in Fig. \ref{['fig_bernumax']}.