Recurrent DQN for radio fingerprinting with constrained measurements collection

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In this paper, we address the problem of fingerprinting-based radio localization with a particular focus on the measurements collection part. We consider the crucial circumstance where the operator that builds the fingerprinting map by collecting measurements can only travel a limited distance. We propose an iterative formulation that increases the accuracy of the position prediction task by using a recurrent deep reinforcement learning algorithm. Numerical results on a real dataset show the effectiveness of the proposed method, and the comparison with other measurement collection strategies corroborates its value.