DeepAlloc：共享频谱系统中基于 CNN 的高效频谱分配方法
DeepAlloc: CNN-Based Approach to Efficient Spectrum Allocation in Shared Spectrum Systems
AI总结：
针对共享频谱中路径损耗模型不准、主用户信息可能缺失的问题，提出基于 CNN 的 DeepAlloc，利用众包频谱传感读数直接学习分配函数，准确率最高提升 60%。
摘要：
共享频谱系统有助于在不损害授权用户的情况下向非授权用户分配频谱；它们在优化频谱效用方面具有巨大前景，但其管理（尤其是向非授权用户进行高效频谱分配）颇具挑战。当前分配方法的一个显著缺点是，它们要么为确保正确性而以非常保守的方式完成，要么基于不完善的传播模型和/或空间粒度较差的频谱感知。这会导致频谱利用率低下，而频谱利用率正是共享频谱系统的根本目标。为了在一般场景中近乎最优地向次级用户分配频谱，从根本上说我们需要掌握信号路径损耗函数。然而在实践中，即使是已知最佳的路径损耗模型也具有不能令人满意的精度，而开展大规模勘测以收集路径损耗值并不可行。为规避这一挑战，我们提出直接使用监督学习技术学习频谱分配函数。我们特别处理主用户信息可能不可用的场景；对于此类设置，我们利用众包感知架构，并将频谱传感器读数作为特征。我们开发了一种高效的基于 CNN 的方法（称为 DeepAlloc），并解决了将其应用于学习频谱分配函数时出现的各种挑战。通过广泛的大规模仿真和一个小型测试平台，我们证明了所开发技术的有效性；具体而言，我们观察到，与标准学习技术和先前工作相比，我们的方法将准确率提高了最高 60%。
英文摘要：
Shared spectrum systems facilitate spectrum allocation to unlicensed users without harming the licensed users; they offer great promise in optimizing spectrum utility, but their management (in particular, efficient spectrum allocation to unlicensed users) is challenging. A significant shortcoming of current allocation methods is that they are either done very conservatively to ensure correctness, or are based on imperfect propagation models and/or spectrum sensing with poor spatial granularity. This leads to poor spectrum utilization, the fundamental objective of shared spectrum systems. To allocate spectrum near-optimally to secondary users in general scenarios, we fundamentally need to have knowledge of the signal path-loss function. In practice, however, even the best known path-loss models have unsatisfactory accuracy, and conducting extensive surveys to gather path-loss values is infeasible. To circumvent this challenge, we propose to learn the spectrum allocation function directly using supervised learning techniques. We particularly address the scenarios when the primary users' information may not be available; for such settings, we make use of a crowdsourced sensing architecture and use the spectrum sensor readings as features. We develop an efficient CNN-based approach (called DeepAlloc) and address various challenges that arise in its application to the learning the spectrum allocation function. Via extensive large-scale simulation and a small testbed, we demonstrate the effectiveness of our developed techniques; in particular, we observe that our approach improves the accuracy of standard learning techniques and prior work by up to 60%.
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