  The relay channel, consisting of a source-destination pair and a relay, is a
fundamental component of cooperative communications. While the capacity of a
general relay channel remains unknown, various relaying strategies, including
compress-and-forward (CF), have been proposed. For CF, given the correlated
signals at the relay and destination, distributed compression techniques, such
as Wyner-Ziv coding, can be harnessed to utilize the relay-to-destination link
more efficiently. In light of the recent advancements in neural network-based
distributed compression, we revisit the relay channel problem, where we
integrate a learned one-shot Wyner--Ziv compressor into a primitive relay
channel with a finite-capacity and orthogonal (or out-of-band)
relay-to-destination link. The resulting neural CF scheme demonstrates that our
task-oriented compressor recovers "binning" of the quantized indices at the
relay, mimicking the optimal asymptotic CF strategy, although no structure
exploiting the knowledge of source statistics was imposed into the design. We
show that the proposed neural CF scheme, employing finite order modulation,
operates closely to the capacity of a primitive relay channel that assumes a
Gaussian codebook. Our learned compressor provides the first proof-of-concept
work toward a practical neural CF relaying scheme.
