Meta-learning lets a receiver adapt its demodulator to a new transmitter's channel and hardware distortions using only a handful of pilot symbols, beating model-based and conventional learning in simulations.
Information-Centric Grant-Free Access for IoT Fog Networks: Edge vs Cloud Detection and Learning
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abstract
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. In this paper, this form of information-centric radio access is studied for the first time in a multi-cell F-RAN model with edge or cloud detection. Edge and cloud detection are designed and compared for a multi-cell system. Optimal model-based detectors are introduced and the resulting asymptotic behavior 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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Learning to Demodulate from Few Pilots via Offline and Online Meta-Learning
Meta-learning lets a receiver adapt its demodulator to a new transmitter's channel and hardware distortions using only a handful of pilot symbols, beating model-based and conventional learning in simulations.