不可靠可重构智能曲面的最优路径分配
Towards Optimal Path Allocation for Unreliable Reconfigurable Intelligent Surfaces
摘要：
太赫兹（THz）通信和可重构智能表面（RISs）最近被提出来实现各种强大的室内应用，例如无线虚拟现实（VR）。为了有效地为VR用户提供服务，有效的THz路径分配解决方案变得必要。假设RIS组件是最关键的一个在启用服务，我们调查RIS硬件故障对路径分配性能的影响。为此，我们研究了太赫兹网络，采用太赫兹操作的RIS作为基站，为VR用户服务。我们提出了一种基于半马尔可夫决策过程（SMDP）的路径分配模型，以确保THz连接的可靠性，同时最大化总的长期预期系统回报，考虑系统收益，链路利用率的成本，和RIS故障的惩罚。通过定义状态空间、动作空间、奖励模型和转移概率分布，建立了基于SMDP的RIS系统模型。我们提出了一个最佳的路径分配迭代算法，决定在每个系统状态下的下一个动作。结果显示了不同场景下的平均奖励和VR服务阻塞概率，以及各种VR服务到达和RIS故障率，作为在不可靠的THz RIS上实现可行的VR服务的第一步。
英文摘要：
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.
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。
