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.

翻译：移动自组网（MANETs）有望应用于多种通信技术。通过将MANETs与非正交多址接入（NOMA）通信相结合，可以实现可扩展、频谱高效且灵活的网络拓扑结构。为充分发挥NOMA MANETs的优势，需确定传输协议，尤其是叠加码的设计方案。然而，叠加码优化过程耗时较长，且需在网络拓扑变化时重复进行。本研究提出一种用于多跳NOMA MANETs中快速优化叠加码的算法。为实现少量迭代下的可靠调优，我们采用新兴的深度展开方法，利用数据驱动提升参数设置的可靠性。所提出的叠加码优化算法在无监督学习框架下，通过少量投影梯度步数学习各用户超参数，以最大化历史信道条件下的最小速率。该学习型优化器设计适用于完全信道状态信息场景，以及需要通过导频估计信道系数的场景。研究表明，将原理性优化与机器学习相结合可产生可扩展的优化器，经训练后能适应不同网络拓扑。针对优化问题的非凸特性，我们采用多起点并行学习优化策略作为集成学习方法。数值结果表明，所提方法能够为各类信道快速配置高速率叠加码。