论文标题
达到最佳的路径分配，以进行不可靠的可重新配置智能表面
Towards Optimal Path Allocation for Unreliable Reconfigurable Intelligent Surfaces
论文作者
论文摘要
最近提出了Terahertz（THZ）通信和可重新配置的智能表面（RISS），以启用各种强大的室内应用，例如无线虚拟现实（VR）。对于VR用户的有效维修，有效的THZ路径分配解决方案成为必要。假设RIS组件是启用服务中最关键的组件，我们研究了RIS硬件故障对路径分配性能的影响。为此，我们研究了一个THZ网络，该网络采用THZ操作的RISS充当基站，为VR用户提供服务。我们提出了一个基于半马科夫的决策过程（SMDP）的路径分配模型，以确保THZ连接的可靠性，同时考虑系统收益，链路利用的成本以及RIS失败的惩罚，从而最大程度地提高了总长期预期系统奖励。通过定义状态空间，行动空间，奖励模型和过渡概率分布来制定基于SMDP的RIS系统模型。我们为路径分配提出了一种最佳的迭代算法，该算法决定每个系统状态下的下一个动作。结果表明，在不同的情况下以及各种VR服务到达和RIS故障率的情况下，平均奖励和VR服务阻止了概率，这是迈向不可靠的THZ RIS的第一步。
Terahertz (THz) communications and reconfigurable intelligent surfaces (RISs) have been recently proposed to enable various powerful indoor applications, such as wireless virtual reality (VR). For an efficient servicing of VR users, an efficient THz path allocation solution becomes a necessity. Assuming the RIS component is the most critical one in enabling the service, we investigate the impact of RIS hardware failure on path allocation performance. To this end, we study a THz network that employs THz operated RISs acting as base stations, serving VR users. We propose a Semi-Markov decision Process (SMDP)-based path allocation model to ensure the reliability of THz connection, while maximizing the total long-term expected system reward, considering the system gains, costs of link utilization, and the penalty of RIS failure. The SMDP-based model of the RIS system is formulated by defining the state space, action space, reward model, and transition probability distribution. We propose an optimal iterative algorithm for path allocation that decides the next action at each system state. The results show the average reward and VR service blocking probability under different scenarios and with various VR service arrivals and RIS failure rates, as first step towards feasible VR services over unreliable THz RIS.
