Brown Colin, Rong Bo
Communications Research Centre, Ottawa, ON K2H 8S2, Canada.
Sensors (Basel). 2024 Sep 11;24(18):5885. doi: 10.3390/s24185885.
The anticipated surge of Internet of Things (IoT) devices is expected to intensify the demand for mid-band spectrum resources, posing challenges to traditional spectrum sharing methods. This paper addresses the limitations of static database-assisted spectrum management frameworks and proposes a novel approach integrating high-resolution geospatial and real-time environmental data. Leveraging these inputs, the proposed framework enhances spectrum allocation accuracy, and mitigates interference more effectively, thereby increasing the access opportunities for IoT deployments. A detailed example scenario illustrates the efficacy of the proposed approach, demonstrating significant gains in spectrum sharing efficiency. It shows gains in the number of new entrants accessing the spectrum, ranging from 77% to 140%. These gains occur when moving to less conservative interference conditions and including more complex geospatial information in the propagation environment. These findings underscore the critical role of advanced spectrum sharing techniques in optimizing spectrum utilization for future IoT networks.
物联网（IoT）设备数量预计会激增，这将加剧对中频段频谱资源的需求，给传统频谱共享方法带来挑战。本文探讨了静态数据库辅助频谱管理框架的局限性，并提出了一种整合高分辨率地理空间数据和实时环境数据的新颖方法。利用这些输入信息，所提出的框架提高了频谱分配的准确性，并更有效地减轻了干扰，从而增加了物联网部署的接入机会。一个详细的示例场景说明了所提方法的有效性，展示了频谱共享效率的显著提升。结果表明，在转向不太保守的干扰条件并在传播环境中纳入更复杂的地理空间信息时，新进入频谱的用户数量增加了77%至140%。这些发现凸显了先进频谱共享技术在优化未来物联网网络频谱利用方面的关键作用。
