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arXiv Machine Learning By Lorenzo Mario Amorosa, Francesco Conti, Nicola Quercioli, Flavio Zabini, Tayebeh Lotfi Mahyari, Yiqun Ge, Patrizio Frosini

Reconstruction of SINR Maps from Sparse Measurements using Group Equivariant Non-Expansive Operators

Read the original on arXiv Machine Learning →

arXiv:2507. 19349v3 Announce Type: replace Abstract: As sixth generation (6G) wireless networks evolve, accurate signal-to-interference-noise ratio (SINR) maps are becoming increasingly critical for effective resource management and optimization.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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