Symbiotic Positioning, Navigation, and Timing
Symbiotic Positioning, Navigation, and Timing
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DOI：
10.1109/dcoss-iot58021.2023.00052
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发表时间：
2023-06
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影响因子：
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通讯作者：
Md. Sadman Siraj;Aisha B. Rahman;E. Tsiropoulou;S. Papavassiliou;Jim Plusquellic
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文献类型：
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作者：
Md. Sadman Siraj;Aisha B. Rahman;E. Tsiropoulou;S. Papavassiliou;Jim Plusquellic
物联网(IoT)设备和基于位置的服务的爆炸性增长，以及各种6G使能技术的发展，推动了对替代定位、导航和定时(PNT)解决方案设计的需求。本文顺应这一趋势，受生物生态系统演化的启发，基于博弈论原理和可重构智能表面的开发，提出了一种新的共生PNT解决方案。由锚节点和已知坐标的RISS、粗略估计其位置的协作节点和未知位置的目标组成的一组参与者正在协作，以准确确定目标的位置和定时。关键目标是最小化每个目标和协作者节点以及整个被检查系统的估计误差。RISS的相移优化被执行以最大化在RISS上反射并由协作方节点和目标接收的信号的接收信号强度。然后，将定位和定时估计误差的优化问题描述为目标节点和协作节点之间的势博弈，并证明了至少存在一个纳什均衡。提出了两种确定纳什均衡的算法方法，即异步和同步最佳响应动力学方法，并通过建模和仿真对该方法的性能进行了评估。
The explosive growth of Internet of Things (IoT) devices and location based services, along with the development of various 6G enabling technologies, are fueling the need for the design of alternative Positioning, Navigation, and Timing (PNT) solutions. In this paper, following this trend and inspired by the evolution of biological ecosystems, we introduce a novel symbiotic PNT solution, based on the principles of Game Theory and the exploitation of Reconfigurable Intelligent Surfaces (RISs). A set of actors, consisting of anchor nodes and RISs with known coordinates, collaborator nodes having a rough estimate of their positions, and targets of unknown positions, are cooperating to accurately determine the targets' positioning and timing. The key objective is to minimize the estimation error of each target and collaborator node, as well as of the overall examined system. The RISs' phase shifts optimization is performed to maximize the received signal strength of the signals reflected on the RISs and received by the collaborator nodes and the targets. Then, the optimization problem of the positioning and timing estimation error is formulated as a potential game among the targets and collaborator nodes, and the existence of at least one Nash Equilibrium is proven. Two algorithmic approaches, namely Asynchronous and Synchronous Best Response Dynamics, are introduced to determine the Nash Equilibrium, while the performance evaluation of the proposed approach is achieved via modeling and simulation.