TESSO: An analytical tool for characterizing aggregate interference and enabling spatial spectrum sharing

Sudeep Bhattarai, J. Park, W. Lehr, Bo Gao
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引用次数: 7

Abstract

Radio propagation models play a crucial role in realizing effective spectrum sharing. Unlike propagation models that do not use the exact details of terrain, terrain-based propagation models are effective in identifying spatial spectrum sharing opportunities for the secondary users (SUs) around an incumbent user (IU). Unfortunately, terrain-based propagation models, such as the Irregular Terrain Model (ITM) in point-to-point (PTP) mode, are computationally expensive, and they require precise geo-locations of the SUs. Such requirements render them challenging, if not impractical, to implement in real-time applications, such as geolocation database (GDB)-driven spectrum sharing. To address this problem, we propose a pragmatic approach called Tool for Enabling Spatial Spectrum Sharing Opportunities (TESSO). TESSO characterizes the aggregate interference caused by the SUs and identifies spatial spectrum sharing opportunities effectively. It is computationally efficient, and does not require precise geo-locations of the SUs. Our results show that TESSO provides the same level of interference protection guarantee to the IU as that offered by the terrain-based models. TESSO can be implemented in GDB-driven spectrum sharing ecosystems for effectively exploiting spatial spectrum sharing opportunities.
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TESSO:描述聚合干扰和实现空间频谱共享的分析工具
无线电传播模型对实现有效的频谱共享起着至关重要的作用。与不使用地形精确细节的传播模型不同,基于地形的传播模型在识别现有用户(IU)周围的次要用户(su)的空间频谱共享机会方面是有效的。不幸的是,基于地形的传播模型,例如点对点(PTP)模式中的不规则地形模型(ITM),计算成本很高,并且它们需要su的精确地理位置。这样的需求使得在实时应用中实现它们具有挑战性,如果不是不切实际的话,例如地理位置数据库(GDB)驱动的频谱共享。为了解决这个问题,我们提出了一种实用的方法,称为实现空间频谱共享机会的工具(TESSO)。TESSO表征了由单一单元引起的综合干扰,并有效地识别了空间频谱共享机会。它具有计算效率,并且不需要su的精确地理位置。我们的研究结果表明,TESSO为基于地形的模型提供了与基于地形的模型相同的干扰保护保证。TESSO可以在gdb驱动的频谱共享生态系统中实施,以有效地利用空间频谱共享机会。
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