Emergence of shared spectrum, such as the 3.5 GHz Citizen Broadband Radio Service (CBRS) band in the US, promises to broaden the mobile operator ecosystem and lead to proliferation of small cell deployments. We consider the interoperator interference problem that arises when multiple small cell networks access the shared spectrum. Towards this end, we take a novel communication-free approach that seeks implicit coordination between operators without explicit communication.
The key idea is for each operator to sense the spectrum through its mobiles to be able to model the channel vacancy distribution and extrapolate it for the next epoch. We use reproducing kernel Hilbert space kernel embedding of channel vacancy and predict it by vector-valued regression. This predicted value is then relied on by each operator to perform independent but optimal channel assignment to its base stations taking traffic load into account. Via numerical results, we show that our approach, aided by the above channel vacancy forecasting, adapts the spectrum allocation over time as per the traffic demands and more crucially, yields as good as or better performance than a coordination based approach, even without accounting the overhead of the latter.
- Shared spectrum
- multi-operator small cell networks
- interference prediction and management
- machine learning
- kernel embedding of distributions