  In this work, the capabilities of an encoder-decoder learning framework are
leveraged to predict soft-failure evolution over a long future horizon. This
enables the triggering of timely repair actions with low
quality-of-transmission (QoT) margins before a costly hard-failure occurs,
ultimately reducing the frequency of repair actions and associated operational
expenses. Specifically, it is shown that the proposed scheme is capable of
triggering a repair action several days prior to the expected day of a
hard-failure, contrary to soft-failure detection schemes utilizing rule-based
fixed QoT margins, that may lead either to premature repair actions (i.e.,
several months before the event of a hard-failure) or to repair actions that
are taken too late (i.e., after the hard failure has occurred). Both frameworks
are evaluated and compared for a lightpath established in an elastic optical
network, where soft-failure evolution can be modeled by analyzing
bit-error-rate information monitored at the coherent receivers.
