  A novel framework is proposed for the deployment and passive beamforming
design of a reconfigurable intelligent surface (RIS) with the aid of
non-orthogonal multiple access (NOMA) technology. The problem of joint
deployment, phase shift design, as well as power allocation is formulated for
maximizing the energy efficiency with considering users' particular data
requirements. To tackle this pertinent problem, machine learning approaches are
adopted in two steps. Firstly, a novel long short-term memory (LSTM) based echo
state network (ESN) algorithm is proposed to predict users' tele-traffic demand
by leveraging a real dataset. Secondly, a decaying double deep Q-network (D3QN)
based position-acquisition and phase-control algorithm is proposed to solve the
joint problem of deployment and design of the RIS. In the proposed algorithm,
the base station, which controls the RIS by a controller, acts as an agent. The
agent periodically observes the state of the RIS-enhanced system for attaining
the optimal deployment and design policies of the RIS by learning from its
mistakes and the feedback of users. Additionally, it is proved that the
proposed D3QN based deployment and design algorithm is capable of converging
within mild conditions. Simulation results are provided for illustrating that
the proposed LSTM-based ESN algorithm is capable of striking a tradeoff between
the prediction accuracy and computational complexity. Finally, it is
demonstrated that the proposed D3QN based algorithm outperforms the benchmarks,
while the NOMA-enhanced RIS system is capable of achieving higher energy
efficiency than orthogonal multiple access (OMA) enabled RIS system.
