# Learning Centric Wireless Resource Allocation for Edge Computing: Algorithm and Experiment

arXiv: https://arxiv.org/abs/2010.15371 · HTML: https://mlsanity.com/paper/2010.15371/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2020.

## Built on by (most cited first, 3 of 3)

- [Green Edge AI: A Contemporary Survey](https://mlsanity.com/paper/2312.00333/) (2023)
- [Accelerating Edge Intelligence via Integrated Sensing and Communication](https://mlsanity.com/paper/2107.09574/) (2021)
- [Integrated Sensing and Communication from Learning Perspective: An SDP3 Approach](https://mlsanity.com/paper/2107.09621/) (2021)

## Abstract (arXiv)

Edge intelligence is an emerging network architecture that integrates sensing, communication, computing components, and supports various machine learning applications, where a fundamental communication question is: how to allocate the limited wireless resources (such as time, energy) to the simultaneous model training of heterogeneous learning tasks? Existing methods ignore two important facts: 1) different models have heterogeneous demands on training data; 2) there is a mismatch between the simulated environment and the real-world environment. As a result, they could lead to low learning performance in practice. This paper proposes the learning centric wireless resource allocation (LCWRA) scheme that maximizes the worst learning performance of multiple tasks. Analysis shows that the optimal transmission time has an inverse power relationship with respect to the generalization error. Finally, both simulation and experimental results are provided to verify the performance of the proposed LCWRA scheme and its robustness in real implementation.