Low-power wide area (LPWA) technology, specifically Narrowband Internet of Things (NB-IoT), has emerged as a pivotal sixth-generation (6G) network technology, catering to the demands of massive-type communications (-MTC), extended battery life, wide range coverage, and high reliability. Despite its advancements, existing studies often fall short in simultaneously addressing energy efficiency, latency, link adaptation, and resource allocation in the bandwidth-constrained environments typical of NB-IoT. The complexity of finding a comprehensive optimal solution for resource allocation is compounded by its non-convex and NP-hard combinatorial nature. This research addresses these challenges by introducing a Hybrid-Optimizer approach, which integrates link adaptation, resource allocation, and scheduling algorithms. This approach aims to identify the optimal feasible combination for each device (UE), focusing on energy conservation and improved signal reception. Applied to the Narrow Physical Uplink Shared Channel (NPUSCH) uplink scheduling in NB-IoT, this method is benchmarked against the conventional Round-Robin scheduling algorithm. Simulation results demonstrate that the Hybrid-Optimizer approach significantly outperforms the Round-Robin algorithm in terms of scheduling processing time and energy efficiency. Furthermore, the proposed algorithm is capable of generating candidate combinations that enhance signal reception in UEs and meet the required subframe criteria, adhering to system modeling assumptions for scheduling optimization.

中文翻译：

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通过链路自适应、资源分配和能源效率的可行组合，针对 NB-IoT 应用优化 NPUSCH 上行链路调度方法

低功耗广域（LPWA）技术，特别是窄带物联网（NB-IoT），已成为关键的第六代（6G）网络技术，满足大规模通信（-MTC）的需求，扩展电池寿命长、覆盖范围广、可靠性高。尽管取得了进步，但现有的研究往往无法同时解决 NB-IoT 典型的带宽受限环境中的能源效率、延迟、链路自适应和资源分配问题。寻找资源分配的综合最优解决方案的复杂性因其非凸和 NP 困难组合性质而变得更加复杂。本研究通过引入混合优化器方法来解决这些挑战，该方法集成了链路自适应、资源分配和调度算法。该方法旨在确定每个设备 (UE) 的最佳可行组合，重点关注节能和改善信号接收。该方法应用于NB-IoT中的窄物理上行链路共享信道（NPUSCH）上行链路调度，以传统的循环调度算法为基准。仿真结果表明，混合优化器方法在调度处理时间和能源效率方面显着优于循环算法。此外，所提出的算法能够生成候选组合，增强 UE 中的信号接收并满足所需的子帧标准，遵守用于调度优化的系统建模假设。