# Over-the-Air Goal-Oriented Communications

arXiv: https://arxiv.org/abs/2512.20533 · HTML: https://mlsanity.com/paper/2512.20533/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 36 of its 71 references that are in the map.
- First posted on arXiv 23 Dec 2025.

## Builds on (most cited first, 30 of 36)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Variational Dropout and the Local Reparameterization Trick](https://mlsanity.com/paper/1506.02557/) (2015)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [Deep Learning Enabled Semantic Communication Systems](https://mlsanity.com/paper/2006.10685/) (2020)
- [All-Optical Machine Learning Using Diffractive Deep Neural Networks](https://mlsanity.com/paper/1804.08711/) (2018)
- [Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications](https://mlsanity.com/paper/2207.09353/) (2022)
- [Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications](https://mlsanity.com/paper/2111.12444/) (2021)
- [Deep Learning-Based Communication Over the Air](https://mlsanity.com/paper/1707.03384/) (2017)
- [Wireless Image Retrieval at the Edge](https://mlsanity.com/paper/2007.10915/) (2020)
- [Noisy Activation Functions](https://mlsanity.com/paper/1603.00391/) (2016)
- [Joint Source-Channel Coding: Fundamentals and Recent Progress in Practical Designs](https://mlsanity.com/paper/2409.17557/) (2024)
- [Design of Task-Specific Optical Systems Using Broadband Diffractive Neural Networks](https://mlsanity.com/paper/1909.06553/) (2019)
- [Indoor Signal Focusing with Deep Learning Designed Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/1905.07726/) (2019)
- [Over-the-Air Computation for 6G: Foundations, Technologies, and Applications](https://mlsanity.com/paper/2210.10524/) (2022)
- [Stacked Intelligent Metasurfaces for Efficient Holographic MIMO Communications in 6G](https://mlsanity.com/paper/2305.08079/) (2023)
- [Multi-hop RIS-Empowered Terahertz Communications: A DRL-based Hybrid Beamforming Design](https://mlsanity.com/paper/2101.09137/) (2021)
- [Phase Configuration Learning in Wireless Networks with Multiple Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2010.04376/) (2020)
- [Goal-Oriented Communication for Edge Learning based on the Information Bottleneck](https://mlsanity.com/paper/2202.12639/) (2022)
- [Reconfigurable Intelligent Surfaces for Wireless Communications: Overview of Hardware Designs, Channel Models, and Estimation Techniques](https://mlsanity.com/paper/2203.03176/) (2022)
- [Hybrid Reconfigurable Intelligent Metasurfaces: Enabling Simultaneous Tunable Reflections and Sensing for 6G Wireless Communications](https://mlsanity.com/paper/2104.04690/) (2021)
- [Enabling Edge Artificial Intelligence via Goal-oriented Deep Neural Network Splitting](https://mlsanity.com/paper/2312.03555/) (2023)
- [Reconfigurable Intelligent Surfaces for 6G: Emerging Hardware Architectures, Applications, and Open Challenges](https://mlsanity.com/paper/2312.16874/) (2023)
- [Universal Approximation with XL MIMO Systems: OTA Classification via Trainable Analog Combining](https://mlsanity.com/paper/2504.12758/) (2025)
- [Metasurfaces-Enabled Wave Computing for Future Wireless Systems: Opportunities and Challenges](https://mlsanity.com/paper/2501.05173/) (2025)
- [Low-to-Zero-Overhead IRS Reconfiguration: Decoupling Illumination and Channel Estimation](https://mlsanity.com/paper/2111.09421/) (2021)
- [Reconfigurable Intelligent Computational Surfaces: When Wave Propagation Control Meets Computing](https://mlsanity.com/paper/2208.04509/) (2022)
- [Asymptotically Optimal Closed-Form Phase Configuration of 1-bit RISs via Sign Alignment](https://mlsanity.com/paper/2407.13510/) (2024)
- [Optimizing RIS Impairments through Semantic Communication](https://mlsanity.com/paper/2410.08155/) (2024)

## Abstract (arXiv)

Goal-oriented communications offer an attractive alternative to the Shannon-based communication paradigm, where the data is never reconstructed at the Receiver (RX) side. Rather, focusing on the case of edge inference, the Transmitter (TX) and the RX cooperate to exchange features of the input data that will be used to predict an unseen attribute of them, leveraging information from collected data sets. This chapter demonstrates that the wireless channel can be used to perform computations over the data, when equipped with programmable metasurfaces. The end-to-end system of the TX, RX, and MS-based channel is treated as a single deep neural network which is trained through backpropagation to perform inference on unseen data. Using Stacked Intelligent Metasurfaces (SIM), it is shown that this Metasurfaces-Integrated Neural Network (MINN) can achieve performance comparable to fully digital neural networks under various system parameters and data sets. By offloading computations onto the channel itself, important benefits may be achieved in terms of energy consumption, arising from reduced computations at the transceivers and smaller transmission power required for successful inference.