  Various communication technologies are expected to utilize mobile ad hoc
networks (MANETs). By combining MANETs with non-orthogonal multiple access
(NOMA) communications, one can support scalable, spectrally efficient, and
flexible network topologies. To achieve these benefits of NOMA MANETs, one
should determine the transmission protocol, particularly the superposition
code. However, the latter involves lengthy optimization that has to be repeated
when the topology changes. In this work, we propose an algorithm for rapidly
optimizing superposition codes in multi-hop NOMA MANETs. To achieve reliable
tunning with few iterations, we adopt the emerging deep unfolding methodology,
leveraging data to boost reliable settings. Our superposition coding
optimization algorithm utilizes a small number of projected gradient steps
while learning its per-user hyperparameters to maximize the minimal rate over
past channels in an unsupervised manner. The learned optimizer is designed for
both settings with full channel state information, as well as when the channel
coefficients are to be estimated from pilots. We show that the combination of
principled optimization and machine learning yields a scalable optimizer, that
once trained, can be applied to different topologies. We cope with the
non-convex nature of the optimization problem by applying parallel-learned
optimization with different starting points as a form of ensemble learning. Our
numerical results demonstrate that the proposed method enables the rapid
setting of high-rate superposition codes for various channels.
