  A novel framework of reconfigurable intelligent surfaces (RISs)-enhanced
indoor wireless networks is proposed, where an RIS mounted on the robot is
invoked to enable mobility of the RIS and enhance the service quality for
mobile users. Meanwhile, non-orthogonal multiple access (NOMA) techniques are
adopted to further increase the spectrum efficiency since RISs are capable to
provide NOMA with artificial controlled channel conditions, which can be seen
as a beneficial operation condition to obtain NOMA gains. To optimize the sum
rate of all users, a deep deterministic policy gradient (DDPG) algorithm is
invoked to optimize the deployment and phase shifts of the mobile RIS as well
as the power allocation policy. In order to improve the efficiency and
effectiveness of agent training for the DDPG agents, a federated learning (FL)
concept is adopted to enable multiple agents to simultaneously explore similar
environments and exchange experiences. We also proved that with the same random
exploring policy, the FL armed deep reinforcement learning (DRL) agents can
theoretically obtain a reward gain compare to the independent agents. Our
simulation results indicate that the mobile RIS scheme can significantly
outperform the fixed RIS paradigm, which provides about three times data rate
gain compare to the fixed RIS paradigm. Moreover, the NOMA scheme is capable to
achieve a gain of 42% in contrast with the OMA scheme in terms of sum rate.
Finally, the multi-cell simulation proved that the FL enhanced DDPG algorithm
has a superior convergence rate and optimization performance than the
independent training framework.
