# Reduced-Dimension Design of MIMO Over-the-Air Computing for Data Aggregation in Clustered IoT Networks

arXiv: https://arxiv.org/abs/1812.02373 · HTML: https://mlsanity.com/paper/1812.02373/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2018.

## 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)
- [Task-Oriented Over-the-Air Computation for Multi-Device Edge AI](https://mlsanity.com/paper/2211.01255/) (2022)
- [A Survey on Integrated Sensing, Communication, and Computation](https://mlsanity.com/paper/2408.08074/) (2024)
- [Federated Learning over Wireless Networks: A Band-limited Coordinated Descent Approach](https://mlsanity.com/paper/2102.07972/) (2021)
- [Balancing Accuracy and Integrity for Reconfigurable Intelligent Surface-aided Over-the-Air Federated Learning](https://mlsanity.com/paper/2207.08057/) (2022)
- [Collaborative Edge AI Inference over Cloud-RAN](https://mlsanity.com/paper/2404.06007/) (2024)
- [Integrated Sensing, Communication, and Computation for Over-the-Air Federated Edge Learning](https://mlsanity.com/paper/2508.15185/) (2025)
- [Over-the-Air Federated Edge Learning with Error-Feedback One-Bit Quantization and Power Control](https://mlsanity.com/paper/2303.11319/) (2023)
- [Accelerating Wireless Federated Learning via Nesterov's Momentum and Distributed Principle Component Analysis](https://mlsanity.com/paper/2303.17885/) (2023)
- [Task-oriented Over-the-air Computation for Edge-device Co-inference with Balanced Classification Accuracy](https://mlsanity.com/paper/2407.00955/) (2024)
- [Waveform Design for Over-the-Air Computing](https://mlsanity.com/paper/2405.20877/) (2024)

## Abstract (arXiv)

One basic operation of Internet-of-Things (IoT) networks is aggregating distributed sensing-data over wireless-channels for function-computation, called wireless-data-aggregation (WDA). Targeting dense sensors, a recently developed technology called over-the-air computing (AirComp) can dramatically reduce the WDA latency by aggregating distributed data "over-the-air" using the waveform-superposition property of a multi-access channel. In this work, we design multiple-input-multiple-output (MIMO) AirComp for computing a vector-valued function in a clustered IoT network with multi-antenna sensors forming clusters and a multi-antenna access-point (AP) performing WDA. The resultant high-dimensional but low-rank MIMO channels makes it important to reduce channel or signal dimensionality in AirComp to avoid exposure to noise from channel null-spaces. Motivated by this, we develop a framework of reduced-dimension MIMO AirComp, featuring decomposed-aggregation-beamforming (DAB). Consider the case of separable channel-clusters with non-overlapping angle-of-arrival ranges. The optimal DAB has the structure where inner-components extract the dominant eigen-spaces of corresponding channel-clusters and outer-components jointly equalize the resultant low-dimensional channels. Consider the more complex case of inseparable clusters. We propose a suboptimal DAB design where the inner-component performs both dimension-reduction and joint-equalization over clustered-channel covariance matrices and the outer-component jointly equalizes the small-scale fading-channels. Furthermore, efficient algorithms for rank-optimization of individual DAB components and channel-feedback leveraging the AirComp principle are developed.