# Accelerating Edge Intelligence via Integrated Sensing and Communication

arXiv: https://arxiv.org/abs/2107.09574 · HTML: https://mlsanity.com/paper/2107.09574/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 20 Jul 2021.

## Builds on (most cited first, 9 of 9)

- [Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing](https://mlsanity.com/paper/1905.10083/) (2019)
- [Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)](https://mlsanity.com/paper/1812.11494/) (2018)
- [Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing](https://mlsanity.com/paper/1910.05316/) (2019)
- [Towards an Intelligent Edge: Wireless Communication Meets Machine Learning](https://mlsanity.com/paper/1809.00343/) (2018)
- [In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning](https://mlsanity.com/paper/1809.07857/) (2018)
- [Machine Intelligence at the Edge with Learning Centric Power Allocation](https://mlsanity.com/paper/1911.04922/) (2019)
- [Wireless Sensing With Deep Spectrogram Network and Primitive Based Autoregressive Hybrid Channel Model](https://mlsanity.com/paper/2104.10378/) (2021)
- [Learning Centric Wireless Resource Allocation for Edge Computing: Algorithm and Experiment](https://mlsanity.com/paper/2010.15371/) (2020)
- [Edge Learning with Unmanned Ground Vehicle: Joint Path, Energy and Sample Size Planning](https://mlsanity.com/paper/2009.03140/) (2020)

## Built on by (most cited first, 5 of 5)

- [Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI](https://mlsanity.com/paper/2207.00969/) (2022)
- [Wireless Federated Learning over UAV-enabled Integrated Sensing and Communication](https://mlsanity.com/paper/2411.08918/) (2024)
- [Reinforcement Learning for Enhancing Sensing Estimation in Bistatic ISAC Systems with UAV Swarms](https://mlsanity.com/paper/2501.06454/) (2025)
- [Latency-aware Multimodal Federated Learning over UAV Networks](https://mlsanity.com/paper/2510.01717/) (2025)
- [When Agentic AI Meets Integrated Sensing and Communication](https://mlsanity.com/paper/2608.05792/) (2026)

## Abstract (arXiv)

Realizing edge intelligence consists of sensing, communication, training, and inference stages. Conventionally, the sensing and communication stages are executed sequentially, which results in excessive amount of dataset generation and uploading time. This paper proposes to accelerate edge intelligence via integrated sensing and communication (ISAC). As such, the sensing and communication stages are merged so as to make the best use of the wireless signals for the dual purpose of dataset generation and uploading. However, ISAC also introduces additional interference between sensing and communication functionalities. To address this challenge, this paper proposes a classification error minimization formulation to design the ISAC beamforming and time allocation. The globally optimal solution is derived via the rank-1 guaranteed semidefinite relaxation, and performance analysis is performed to quantify the ISAC gain over that of conventional edge intelligence. Simulation results are provided to verify the effectiveness of the proposed ISAC-assisted edge intelligence system. Interestingly, we find that ISAC is always beneficial, when the duration of generating a sample is more than the duration of uploading a sample. Otherwise, the ISAC gain can vanish or even be negative. Nevertheless, we still derive a sufficient condition, under which a positive ISAC gain is feasible.