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arXiv · 2606.23252

Learning to Compute on Dirty Paper

Shreesal Shrestha·Kuranage Roche Rayan Ranasinghe·Giuseppe Thadeu Freitas de Abreu·Elza Erkip
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Abstract

We propose a fully learning-based approach to integrated communication and computing (ICC) that combines dirty paper coding (DPC) with over-the-air computation. Each user employs a neural encoder with sinusoidal activations that learns to pre-cancel its own computing symbol as non-causally known interference, recovering modulo-like periodic structures consistent with lattice-based DPC schemes. A joint neural decoder recovers all users' messages from the received signal, while a separate neural AirComp estimator exploits a multi-slot block structure to estimate a target function of the computing symbols after the encoder-decoder network converges. To our knowledge, this is the first fully learning-based approach to jointly address DPC-based interference pre-cancellation and over-the-air computation in a unified framework.

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Shreesal Shrestha, Kuranage Roche Rayan Ranasinghe, Giuseppe Thadeu Freitas de Abreu, Elza Erkip. 2026-06-22. Learning to Compute on Dirty Paper. https://arxiv.org/abs/2606.23252

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