NOMA增强型室内智能机器人路径设计与资源管理
Path Design and Resource Management for NOMA enhanced Indoor Intelligent Robots
摘要：
提出了一种通信使能的室内智能机器人服务框架，其中采用非正交多址接入（NOMA）技术，以实现高可靠的通信。结合国际电信联盟（ITU）最近提出的超现代室内信道模型，提出了一种可以确定性地描述室内布局和信道状态以构建无线电地图的Lego建模方法。研究的无线电地图调用作为一个虚拟环境训练的强化学习代理，这可以节省训练时间和硬件成本。基于该通信模型，联合优化了IR到达指定任务目的地的运动及其相应的下行功率分配策略，以最大化IR的任务效率和通信可靠性。为了解决这一优化问题，提出了一种新的强化学习方法--深度转移确定性策略梯度（DT-DPG）算法。仿真结果表明：（1）NOMA技术有效地提高了红外系统的通信可靠性：（2）无线地图可以作为虚拟训练环境，其统计信道状态信息可以提高训练效率约30%; 3）与传统的深度确定性策略梯度（DDPG）算法相比，DT-DPG算法在优化性能、训练时间和抗局部最优能力。
英文摘要：
A communication enabled indoor intelligent robots (IRs) service framework is proposed, where non-orthogonal multiple access (NOMA) technique is adopted to enable highly reliable communications. In cooperation with the ultramodern indoor channel model recently proposed by the International Telecommunication Union (ITU), the Lego modeling method is proposed, which can deterministically describe the indoor layout and channel state in order to construct the radio map. The investigated radio map is invoked as a virtual environment to train the reinforcement learning agent, which can save training time and hardware costs. Build on the proposed communication model, motions of IRs who need to reach designated mission destinations and their corresponding down-link power allocation policy are jointly optimized to maximize the mission efficiency and communication reliability of IRs. In an effort to solve this optimization problem, a novel reinforcement learning approach named deep transfer deterministic policy gradient (DT-DPG) algorithm is proposed. Our simulation results demonstrate that 1) With the aid of NOMA techniques, the communication reliability of IRs is effectively improved; 2) The radio map is qualified to be a virtual training environment, and its statistical channel state information improves training efficiency by about 30%; 3) The proposed DT-DPG algorithm is superior to the conventional deep deterministic policy gradient (DDPG) algorithm in terms of optimization performance, training time, and anti-local optimum ability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
