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Shared spectrum systems facilitate spectrum allocation to unlicensed userswithout harming the licensed users; they offer great promise in optimizingspectrum utility, but their management (in particular, efficient spectrumallocation to unlicensed users) is challenging. A significant shortcoming ofcurrent allocation methods is that they are either done very conservatively toensure correctness, or are based on imperfect propagation models and/orspectrum sensing with poor spatial granularity. This leads to poor spectrumutilization, 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. Inpractice, however, even the best known path-loss models have unsatisfactoryaccuracy, and conducting extensive surveys to gather path-loss values isinfeasible. To circumvent this challenge, we propose to learn the spectrumallocation function directly using supervised learning techniques. Weparticularly address the scenarios when the primary users' information may notbe available; for such settings, we make use of a crowdsourced sensingarchitecture and use the spectrum sensor readings as features. We develop anefficient CNN-based approach (called DeepAlloc) and address various challengesthat arise in its application to the learning the spectrum allocation function.Via extensive large-scale simulation and a small testbed, we demonstrate theeffectiveness of our developed techniques; in particular, we observe that ourapproach improves the accuracy of standard learning techniques and prior workby up to 60%.

DeepAlloc: CNN-Based Approach to Efficient Spectrum Allocation in Shared Spectrum Systems