发表机构
Polytechnique Montreal（蒙特利尔高等理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对6G上中频段URLLC的可靠性最大化问题，提出RIS辅助的联合功率分配与UE-RIS关联方案，采用SCA和匹配理论，实现近最优性能。
AI 中文摘要
在第六代（6G）网络中，支持超可靠低延迟通信（URLLC）服务对可靠性、延迟和高效资源利用提出了严格要求。上中频段（FR3）频谱因其在覆盖范围和带宽可用性之间的有利权衡而成为有前景的候选频段。然而，在密集部署中实现高可靠性仍因干扰和传播损伤而具有挑战性。为解决这些挑战，本文研究了一种在FR3频段运行的智能反射面（RIS）辅助网络。我们制定了一个联合发射功率分配和用户设备（UE）-RIS关联问题，以在有限块长度约束下最大化网络可靠性。由于干扰耦合和二元关联变量，该问题是非凸且组合的。为高效解决该问题，我们提出了一种多阶段资源分配框架，该框架集成了基于连续凸近似（SCA）的功率优化方案和基于匹配理论的UE-RIS关联算法。仿真结果表明，所提方案优于贪婪和随机基线，并实现了接近穷举搜索方案的近最优性能。
英文摘要
In sixth-generation (6G) networks, supporting ultra-reliable low-latency communication (URLLC) services imposes stringent requirements on reliability, latency, and efficient resource utilization. The upper mid-band (FR3) spectrum has emerged as a promising candidate due to its favorable trade-off between coverage and bandwidth availability. However, achieving high reliability in dense deployments remains challenging due to interference and propagation impairments. To address these challenges, this paper investigates a reconfigurable intelligent surface (RIS)-assisted network operating in the FR3 band. We formulate a joint transmit power allocation and user equipment (UE)-RIS association problem to maximize network reliability under finite blocklength constraints. The resulting problem is nonconvex and combinatorial due to interference coupling and binary association variables. To solve this problem efficiently, we propose a multiphase resource allocation framework that integrates a successive convex approximation (SCA)-based power optimization scheme with a matching theory-based UE-RIS association algorithm. Simulation results show that the proposed scheme outperforms greedy and random baselines and achieves near-optimal performance close to the exhaustive search scheme.
