  Orthogonal Time Frequency Space (OTFS) is a framework for communications and
active sensing that processes signals in the delay-Doppler (DD) domain. It is
informed by 6G propagation environments, where Doppler spreads measured in kHz
make it more and more difficult to estimate channels, and the standard
model-dependent approach to wireless communication is starting to break down.
We consider Zak-OTFS where inverse Zak transform converts information symbols
mounted on DD domain pulses to the time domain for transmission. Zak-OTFS
modulation is parameterized by a delay period $\tau_{p}$ and a Doppler period
$\nu_{p}$, where the product $\tau_{p}\nu_{p}=1$. When the channel spread is
less than the delay period, and the Doppler spread is less than the Doppler
period, the Zak-OTFS input-output relation can be predicted from the response
to a single pilot symbol. The highly reliable channel estimates concentrate
around the pilot location, and we configure low-density parity-check (LDPC)
codes that take advantage of this prior information about reliability. It is
advantageous to allocate information symbols to more reliable bins in the DD
domain. We report simulation results for a Veh-A channel model where it is not
possible to resolve all the paths, showing that LDPC coding extends the range
of Doppler spreads for which reliable model-free communication is possible. We
show that LDPC coding reduces sensitivity to the choice of transmit filter,
making bandwidth expansion less necessary. Finally, we compare BER performance
of Zak-OTFS to that of a multicarrier approximation (MC-OTFS), showing LDPC
coding amplifies the gains previously reported for uncoded transmission.
