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Leveraging Transfer Learning for Radio Map Estimation via Mixture of Experts
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  • Rahul Kumar Jaiswal ,
  • Mohamed Elnourani ,
  • Siddharth Deshmukh ,
  • Baltasar Beferull-Lozano
Rahul Kumar Jaiswal
University of Agder

Corresponding Author:[email protected]

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Mohamed Elnourani
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Siddharth Deshmukh
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Baltasar Beferull-Lozano
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This paper leverages transfer learning~(TL) on a mixture of experts~(MoE) model for radio map estimation. The proposed MoE combines location-based and location-free experts through a gating network exploiting their complementary benefits. To estimate the radio map in a new wireless environment, the learned model of another sufficiently similar environment is transferred and fine-tuned with additional data from the new environment. The proposed data-driven similarity measure predicts the amount of data needed for TL. Results demonstrate that the proposed method only requires 20-40\% of measurements to adapt to several varying environments, and as expected, the proposed MoE method outperforms both experts.