Intelligent Trajectory Design for RIS-NOMA Aided Multi-Robot Communications
Intelligent Trajectory Design for RIS-NOMA Aided Multi-Robot Communications
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DOI：
10.1109/twc.2023.3254130
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发表时间：
2022-05
影响因子：
10.4
通讯作者：
Xinyu Gao;Xidong Mu;Wenqiang Yi;Yuanwei Liu
中科院分区：
文献类型：
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作者：
Xinyu Gao;Xidong Mu;Wenqiang Yi;Yuanwei Liu
提出了一种新型的可重构智能表面辅助多机器人网络，通过一个接入点(AP)通过非正交多址(NOMA)为多个移动机器人提供服务。目标是通过联合优化机器人的轨迹和NOMA解码顺序、RIS的相移系数和AP的功率分配，来最大化多机器人系统的整个轨迹的总和速率，同时满足预测的机器人的初始和最终位置以及每个机器人的服务质量(Qos)。针对这一问题，提出了一种结合长短期记忆(LSTM)-自回归综合移动平均(ARIMA)模型和双深度Q网络($Text{D}^{3}$QN)算法的综合机器学习(ML)方案。对于机器人的初始和最终位置预测，LSTM-ARIMA能够克服非平稳和非线性数据序列的梯度消失问题。为了联合确定相移矩阵和机器人的轨迹，调用$\Text{D}^{3}$QN来解决动作值高估问题。基于所提出的方案，每个机器人基于整个轨迹的最大和速率保持一个最优轨迹，这表明机器人对整个轨迹设计追求长期利益。数值结果表明：1)LSTM-ARIMA模型提供了高精度的预测模型；2)提出的QN算法能够实现快速的平均收敛；3)RIS-NOMA网络与RIS辅助的正交网络相比具有更好的网络性能。
A novel reconfigurable intelligent surface-aided multi-robot network is proposed, where multiple mobile robots are served by an access point (AP) through non-orthogonal multiple access (NOMA). The goal is to maximize the sum-rate of whole trajectories for the multi-robot system by jointly optimizing trajectories and NOMA decoding orders of robots, phase-shift coefficients of the RIS, and the power allocation of the AP, subject to predicted initial and final positions of robots and the quality of service (QoS) of each robot. To tackle this problem, an integrated machine learning (ML) scheme is proposed, which combines long short-term memory (LSTM)-autoregressive integrated moving average (ARIMA) model and dueling double deep Q-network ( $\text{D}^{3}$ QN) algorithm. For initial and final position prediction for robots, the LSTM-ARIMA is able to overcome the problem of gradient vanishment of non-stationary and non-linear sequences of data. For jointly determining the phase shift matrix and robots’ trajectories, $\text{D}^{3}$ QN is invoked for solving the problem of action value overestimation. Based on the proposed scheme, each robot holds an optimal trajectory based on the maximum sum-rate of a whole trajectory, which reveals that robots pursue long-term benefits for whole trajectory design. Numerical results demonstrated that: 1) LSTM-ARIMA model provides high accuracy predicting model; 2) The proposed $\text{D}^{3}$ QN algorithm can achieve fast average convergence; and 3) RIS-NOMA networks have superior network performance compared to RIS-aided orthogonal counterparts.