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Fast Selection of Indoor Wireless Transmitter Locations with Generalizable Neural Network Propagation Models
  • Aristeidis Seretis ,
  • Charley Xu ,
  • Costas Sarris
Aristeidis Seretis
University of Toronto

Corresponding Author:[email protected]

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Charley Xu
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Costas Sarris
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The continuous emergence of new wireless communication systems increases the need for intelligent planning prior to their deployment. This planning phase includes determining the position and transmit power of wireless access points, to meet quality of service objectives along with electromagnetic compatibility standards. How to effectively place a set of transmitters in an environment is a long-standing problem that has been widely studied over the years. This process has mainly relied on expensive measurement campaigns, low-fidelity empirical models, or high-fidelity but time-consuming simulations. Recent advances in scientific machine learning create new possibilities for overcoming the standard dichotomy between speed and accuracy.
In this paper, we train a deep neural network (U-Net) to predict received signal strength levels for a variety of different geometries and for positions of multiple transmitters. Then, we leverage the computational efficiency of the trained model to determine the position of access point transmitters in new geometries. This approach dramatically accelerates the process of selecting the position of access points, meeting multiple optimization objectivesin an efficient manner.Â