# Integrated Sensing, Communication, and Computation Over-the-Air: MIMO Beamforming Design

arXiv: https://arxiv.org/abs/2201.12581 · HTML: https://mlsanity.com/paper/2201.12581/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2022.

## Built on by (most cited first, 11 of 11)

- [Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directions](https://mlsanity.com/paper/2307.00974/) (2023)
- [Finding the Missing Data: A BERT-inspired Approach Against Package Loss in Wireless Sensing](https://mlsanity.com/paper/2403.12400/) (2024)
- [A Survey on Integrated Sensing, Communication, and Computation](https://mlsanity.com/paper/2408.08074/) (2024)
- [CSI-BERT2: A BERT-inspired Framework for Efficient CSI Prediction and Classification in Wireless Communication and Sensing](https://mlsanity.com/paper/2412.06861/) (2024)
- [Integrated Sensing-Communication-Computation for Edge Artificial Intelligence](https://mlsanity.com/paper/2306.01162/) (2023)
- [Task-Oriented Integrated Sensing, Computation and Communication for Wireless Edge AI](https://mlsanity.com/paper/2306.06603/) (2023)
- [Multiple Access in the Era of Distributed Computing and Edge Intelligence](https://mlsanity.com/paper/2403.07903/) (2024)
- [Waveform Design for Over-the-Air Computing](https://mlsanity.com/paper/2405.20877/) (2024)
- [GFlowNets for Active Learning Based Resource Allocation in Next Generation Wireless Networks](https://mlsanity.com/paper/2505.05224/) (2025)
- [Analog RF Computing: A New Paradigm for Energy-Efficient Edge AI Over MU-MIMO Systems](https://mlsanity.com/paper/2605.14331/) (2026)
- [When Agentic AI Meets Integrated Sensing and Communication](https://mlsanity.com/paper/2608.05792/) (2026)

## Abstract (arXiv)

To support the unprecedented growth of the Internet of Things (IoT) applications, tremendous data need to be collected by the IoT devices and delivered to the server for further computation. By utilizing the same signals for both radar sensing and data communication, the integrated sensing and communication (ISAC) technique has broken the barriers between data collection and delivery in the physical layer. By exploiting the analog-wave addition in a multi-access channel, over-the-air computation (AirComp) enables function computation via transmissions in the physical layer. The promising performance of ISAC and AirComp motivates the current work on developing a framework called integrated sensing, communication, and computation over-the-air (ISCCO). The performance metrics of radar sensing and AirComp are evaluated by the mean squared errors of the estimated target response matrix and the received computation results, respectively. The design challenge of MIMO ISCCO lies in the joint optimization of beamformers for sensing, communication, and computation at both the IoT devices and the server, which results in a non-convex problem. To solve this problem, an algorithmic solution based on the technique of semidefinite relaxation is proposed. The use case of target location estimation based on ISCCO is demonstrated in simulation to show the performance superiority.