发表机构
Iran University of Science and Technology; Ulster University（伊朗理工大学; 阿尔斯特大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出预测感知确定性框架，通过半盲级联信道获取和因果控制器优化分区IRS辅助mMIMO上行链路，实现净速率提升14.09%并保证服务可行性。
AI 中文摘要
智能反射面（IRS）辅助的移动物联网上行链路需要联合控制信道老化、昂贵的级联信道状态信息（CSI）获取以及耦合的IRS/无线资源。本研究为分区IRS辅助的大规模多输入多输出（mMIMO）上行链路开发了一个统一的预测感知确定性框架。直接信道被递归跟踪，而级联信道则通过差分半盲获取选择性刷新，该获取在最小反射基线和DFT编码的IRS状态之间重用相同的未知数据块。此外，有限块获取协方差初始化降阶波束域预测，该预测仅在线传播单位波束均值，并从预计算表中检索年龄相关协方差。一个混合整数非凸公式捕获净吞吐量、公平性、中断、不确定性、切换成本和滑动窗口IRS服务。因果控制器集成了协方差底限可操作性、可行的等大小粒度、残差可行的所有权、隔离波束单次扫描细化、同块新载荷核算以及使用重新计算的不确定性感知ZF速率和双向接收机一致风险的服务优先分配。分析建立了服务可行性保持、有效信道误差界、无分布可靠性以及多项式在线扩展。在未见环境中，预测的获取底限与实际半盲误差匹配，可操作的重新校准将平均重新校准率降低了31.5%，IRS辅助控制相对于仅直接传输实现了14.09%的平均净速率增益。接收机一致的分配提高了实现速率，而消融研究支持等大小分区和单次扫描细化；在标称、移动和负载压力条件下，操作后服务违规为零。
英文摘要
Intelligent reflecting surface (IRS)-assisted mobile Internet of Things uplinks require joint control of channel aging, costly cascaded channel state information (CSI) acquisition, and coupled IRS/radio resources. This investigation develops a unified prediction-aware deterministic framework for partitioned IRS-assisted massive multiple-input multiple-output (mMIMO) uplinks. Direct channels are recursively tracked, whereas cascaded channels are selectively refreshed by differential semi-blind acquisition reusing the same unknown data block across a minimum-reflection baseline and DFT-coded IRS states. Moreover, finite-block acquisition covariances initialize reduced-order beam-domain prediction, which propagates only unit-beam means online and retrieves age-dependent covariances from precomputed tables. A mixed-integer nonconvex formulation captures net throughput, fairness, outage, uncertainty, switching cost, and sliding-window IRS service. The causal controller integrates covariance-floor actionability, feasible equal-size granularity, residual-feasible ownership, isolated-beam one-sweep refinement, same-block fresh-payload accounting, and service-first allocation using recomputed uncertainty-aware ZF rates and bidirectional receiver-consistent risk. Analysis establishes service-feasibility preservation, effective-channel error bounds, distribution-free reliability, and polynomial online scaling. In unseen environments, the predicted acquisition floor matches the practical semi-blind error, actionable recalibration reduces the mean recalibration rate by 31.5%, and IRS-assisted control achieves a 14.09% mean net-rate gain over direct-only transmission. Receiver-consistent allocation improves realized rate, while ablations support equal-size partitioning and single-sweep refinement; zero post-action service violations occur under nominal, mobility, and load-stress conditions.
