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In this paper, we study data-aided sensing (DAS) for a system consisting of abase station (BS) and a number of nodes, where the BS becomes a receiver thatcollects measurements or data sets from the nodes that are distributed over acell. DAS is an iterative data collection scheme that allows the BS toefficiently estimate a target signal (i.e., all nodes' measurements) with asmall number of measurements (compared to random polling). In DAS, a set ofnodes are selected in each round based on the data sets that are alreadyavailable at the BS from previous rounds for efficient data collection. Weconsider DAS for measurements that are correlated Gaussian in this paper. Theresulting DAS is referred to as Gaussian DAS. Using the mean squared error(MSE) criterion, in each round, the BS is able to choose a node that has a dataset to minimize the MSE of the next round. Furthermore, we generalize GaussianDAS in two different ways: i) with multiple parallel channels to uploadmeasurements from nodes using random access; ii) with a model selection, wherea multi-armed bandit problem formulation is used to combine the model selectionwith DAS.

Gaussian Data-aided Sensing with Multichannel Random Access and Model Selection