Affan Affan, Asif Hafiz M, Tarhuni Naser
Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY 40292, USA.
Department of Electrical and Computer Engineering, Sultan Qaboos University, Al-Khoud, Muscat 123, Oman.
Sensors (Basel). 2023 Jun 3;23(11):5319. doi: 10.3390/s23115319.
The localization of agents for collaborative tasks is crucial to maintain the quality of the communication link for successful data transmission between the base station and agents. Power-domain Non-Orthogonal Multiple Access (P-NOMA) is an emerging multiplexing technique that enables the base station to accumulate signals for different agents using the same time-frequency channel. The environment information such as distance from the base station is required at the base station to calculate communication channel gains and allocate suitable signal power to each agent. The accurate estimate of the position for power allocation of P-NOMA in a dynamic environment is challenging due to the changing location of the end-agent and shadowing. In this paper, we take advantage of the two-way Visible Light Communication (VLC) link to (1) estimate the position of the end-agent in a real-time indoor environment based on the signal power received at the base station using machine learning algorithms and (2) allocate resources using the Simplified Gain Ratio Power Allocation (S-GRPA) scheme with the look-up table method. In addition, we use the Euclidean Distance Matrix (EDM) to estimate the location of the end-agent whose signal was lost due to shadowing. The simulation results show that the machine learning algorithm is able to provide an accuracy of 0.19 m and allocate power to the agent.
用于协作任务的智能体定位对于维持基站与智能体之间成功进行数据传输的通信链路质量至关重要。功率域非正交多址接入（P-NOMA）是一种新兴的复用技术，它使基站能够使用相同的时频信道为不同智能体积聚信号。基站需要诸如与基站的距离等环境信息来计算通信信道增益并为每个智能体分配合适的信号功率。由于终端智能体位置的变化和阴影效应，在动态环境中准确估计用于P-NOMA功率分配的位置具有挑战性。在本文中，我们利用双向可见光通信（VLC）链路来：（1）基于基站接收到的信号功率，使用机器学习算法在实时室内环境中估计终端智能体的位置；（2）使用带有查找表方法的简化增益比功率分配（S-GRPA）方案分配资源。此外，我们使用欧几里得距离矩阵（EDM）来估计因阴影而信号丢失的终端智能体的位置。仿真结果表明，机器学习算法能够提供0.19米的定位精度并为智能体分配功率。
