Information-Centric Grant-Free Access for IoT Fog Networks: Edge vs Cloud Detection and Learning
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A multi-cell Fog-Radio Access Network (F-RAN) architecture is considered in which Internet of Things (IoT) devices periodically make noisy observations of a Quantity of Interest (QoI) and transmit using grant-free access in the uplink. The devices in each cell are connected to an Edge Node (EN), which may also have a finite-capacity fronthaul link to a central processor. In contrast to conventional information-agnostic protocols, the devices transmit using a Type-Based Multiple Access (TBMA) protocol that is tailored to enable the estimate of the field of correlated QoIs in each cell based on the measurements received from IoT devices. TBMA has been previously introduced in the single-cell scenarios as a bandwidth-efficient data collection method, and is here studied for the first time in a multi-cell F-RAN model as an instance of information-centric access protocols. To this end, in this paper, edge and cloud detection are designed and compared for a multi-cell system. In the former case, detection of the local QoI is done locally at each EN, while, with the latter, ENs forward the received signals, upon quantization, over the fronthaul links to the central processor that carries out centralized detection of all QoIs. Optimal model-based detectors are introduced and the resulting asymptotic behaviour of the probability of error at cloud and edge is derived. Then, for the scenario in which a statistical model is not available, data-driven edge and cloud detectors are discussed and evaluated in numerical results.
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