  We focus on C-RAN random access protocols for IoT devices that yield
low-latency high-rate active-device detection in dense networks of large-array
remote radio heads. In this context, we study the problem of learning the
strengths of links between detected devices and network sites. In particular,
we develop recommendation-system inspired algorithms, which exploit
random-access observations collected across the network to classify links
between active devices and network sites across the network. Our simulations
and analysis reveal the potential merit of data-driven schemes for such
on-the-fly link classification and subsequent resource allocation across a
wide-area network.
