The integration of a near-space information network (NSIN) with the reconfigurable intelligent surface (RIS) is envisioned to significantly enhance the communication performance of future wireless communication systems by proactively altering wireless channels. This paper investigates the problem of deploying a RIS-integrated NSIN to provide energy-efficient, ultra-reliable and low-latency communications (URLLC) services. We mathematically formulate this problem as a resource optimization problem, aiming to maximize the effective throughput and minimize the system power consumption, subject to URLLC and physical resource constraints. The formulated problem is challenging in terms of accurate channel estimation, RIS phase alignment, theoretical analysis, and effective solution. We propose a joint resource allocation algorithm to handle these challenges. In this algorithm, we develop an accurate channel estimation approach by exploring message passing and optimize phase shifts of RIS reflecting elements to further increase the channel gain. Besides, we derive an analysis-friend expression of decoding error probability and decompose the problem into two-layered optimization problems by analyzing the monotonicity, which makes the formulated problem analytically tractable. Extensive simulations have been conducted to verify the performance of the proposed algorithm. Simulation results show that the proposed algorithm can achieve outstanding channel estimation performance and is more energy-efficient than diverse benchmark algorithms.

翻译：近空间信息网络（NSIN）与可重构智能表面（RIS）的融合被认为能够通过主动改变无线信道，显著提升未来无线通信系统的通信性能。本文研究了部署融合RIS的NSIN以提供能效型超可靠低延迟通信（URLLC）服务的问题。我们将该问题形式化为一个资源优化问题，旨在满足URLLC及物理资源约束的前提下，最大化有效吞吐量并最小化系统功耗。所构建的问题在精确信道估计、RIS相位对准、理论分析及有效求解方面具有挑战性。为应对这些挑战，我们提出了一种联合资源分配算法。在该算法中，我们通过探索消息传递机制开发了一种精确的信道估计方法，并优化了RIS反射单元的相位偏移以进一步提升信道增益。此外，我们推导了解码错误概率的解析友好表达式，并通过分析单调性将原问题分解为双层优化问题，从而使得该问题具有可解析性。通过大量仿真验证了所提算法的性能。仿真结果表明，所提算法能够实现优异的信道估计性能，并且相较于多种基准算法具有更高的能效。