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arXiv · 1801.04001

Efficient C-RAN Random Access for IoT Devices: Learning Links via Recommendation Systems

Ozgun Y. Bursalioglu·Zheda Li·Chenwei Wang·Haralabos Papadopoulos
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Abstract

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.

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Ozgun Y. Bursalioglu, Zheda Li, Chenwei Wang, Haralabos Papadopoulos. 2018-01-11. Efficient C-RAN Random Access for IoT Devices: Learning Links via Recommendation Systems. https://arxiv.org/abs/1801.04001

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