A novel framework is proposed for integrating reconfigurable intelligent
surfaces (RIS) in unmanned aerial vehicle (UAV) enabled wireless networks,
where an RIS is deployed for enhancing the service quality of the UAV.
Non-orthogonal multiple access (NOMA) technique is invoked to further improve
the spectrum efficiency of the network, while mobile users (MUs) are considered
as roaming continuously. The energy consumption minimizing problem is
formulated by jointly designing the movement of the UAV, phase shifts of the
RIS, power allocation policy from the UAV to MUs, as well as determining the
dynamic decoding order. A decaying deep Q-network (D-DQN) based algorithm is
proposed for tackling this pertinent problem. In the proposed D-DQN based
algorithm, the central controller is selected as an agent for periodically
observing the state of UAV-enabled wireless network and for carrying out
actions to adapt to the dynamic environment. In contrast to the conventional
DQN algorithm, the decaying learning rate is leveraged in the proposed D-DQN
based algorithm for attaining a tradeoff between accelerating training speed
and converging to the local optimal. Numerical results demonstrate that: 1) In
contrast to the conventional Q-learning algorithm, which cannot converge when
being adopted for solving the formulated problem, the proposed D-DQN based
algorithm is capable of converging with minor constraints; 2) The energy
dissipation of the UAV can be significantly reduced by integrating RISs in
UAV-enabled wireless networks; 3) By designing the dynamic decoding order and
power allocation policy, the RIS-NOMA case consumes 11.7% less energy than the
RIS-OMA case.

提出一种新的框架，通过在无人机网中部署 RIS 进行服务质量的提升，采用非正交多址技术进一步提高网络的频谱效率，设计深度 Q - 网络算法来解决能源消耗最小化问题。