  Nonorthogonal multiple access (NOMA) with multi-antenna base station (BS) is
a promising technology for next-generation wireless communication, which has
high potential in performance and user fairness. Since the performance of NOMA
depends on the channel conditions, we can combine NOMA and reconfigurable
intelligent surface (RIS), which is a large and passive antenna array and can
optimize the wireless channel. However, the high dimensionality makes the RIS
optimization a complicated problem. In this work, we propose a machine learning
approach to solve the problem of joint optimization of precoding and RIS
configuration. We apply the RIS to realize the quasi-degradation of the
channel, which allows for optimal precoding in closed form. The neural network
architecture RISnet is used, which is designed dedicatedly for RIS
optimization. The proposed solution is superior to the works in the literature
in terms of performance and computation time.
