论文标题
IRS辅助多机器人NOMA室内网络的轨迹和被动横梁形成设计
Trajectory and Passive Beamforming Design for IRS-aided Multi-Robot NOMA Indoor Networks
论文作者
论文摘要
提出了一种新型的智能反射表面（IRS）辅助多机器人网络，其中多个移动轮式机器人通过非正交多重访问（NOMA）提供了多个移动车轮机器人。目的是通过共同优化机器人的轨迹和NOMA解码顺序来最大化所有机器人的总和，反映了IRS的系数以及AP的功率分配，但要遵守每个机器人的服务质量（QOS）。为了解决这个问题，调用了基于Duel Duel Duel Duel Duel Deep Q-NetWork（D^{3} Qn）算法，以共同确定相移矩阵和机器人的轨迹，以共同确定相移。具体而言，机器人的轨迹包含一组本地最佳位置，该位置揭示了机器人在每个步骤中做出最佳决定。数值结果表明，所提出的D^{3} Qn算法的表现优于常规算法，而IRS-NOMA网络的性能优于正交多重访问（OMA）网络。
A novel intelligent reflecting surface (IRS)-aided multi-robot network is proposed, where multiple mobile wheeled robots are served by an access point (AP) through non-orthogonal multiple access (NOMA). The goal is to maximize the sum-rate of all robots by jointly optimizing trajectories and NOMA decoding orders of robots, reflecting coefficients of the IRS, and the power allocation of the AP, subject to the quality of service (QoS) of each robot. To tackle this problem, a dueling double deep Q-network (D^{3}QN) based algorithm is invoked for jointly determining the phase shift matrix and robots' trajectories. Specifically, the trajectories for robots contain a set of local optimal positions, which reveals that robots make the optimal decision at each step. Numerical results demonstrated that the proposed D^{3}QN algorithm outperforms the conventional algorithm, while the performance of IRS-NOMA network is better than the orthogonal multiple access (OMA) network.
