# Digital Twin-Based User-Centric Edge Continual Learning in Integrated Sensing and Communication

arXiv: https://arxiv.org/abs/2311.12223 · HTML: https://mlsanity.com/paper/2311.12223/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 15 references that are in the map.
- First posted on arXiv 20 Nov 2023.

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

- [Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyond](https://mlsanity.com/paper/2108.07165/) (2021)
- [Edge Learning for B5G Networks with Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing](https://mlsanity.com/paper/2206.00422/) (2022)
- [Machine Intelligence at the Edge with Learning Centric Power Allocation](https://mlsanity.com/paper/1911.04922/) (2019)
- [Holistic Network Virtualization and Pervasive Network Intelligence for 6G](https://mlsanity.com/paper/2301.00519/) (2023)
- [Toward Ambient Intelligence: Federated Edge Learning with Task-Oriented Sensing, Computation, and Communication Integration](https://mlsanity.com/paper/2206.05949/) (2022)
- [In-situ Model Downloading to Realize Versatile Edge AI in 6G Mobile Networks](https://mlsanity.com/paper/2210.03555/) (2022)
- [Digital Twin-Empowered Network Planning for Multi-Tier Computing](https://mlsanity.com/paper/2210.02616/) (2022)
- [AdaEvo: Edge-Assisted Continuous and Timely DNN Model Evolution for Mobile Devices](https://mlsanity.com/paper/2309.15500/) (2023)
- [Impact of Channel Aging on Dual-Function Radar-Communication Systems: Performance Analysis and Resource Allocation](https://mlsanity.com/paper/2303.00634/) (2023)

## Built on by (most cited first, 1 of 1)

- [Cross-Domain Continual Learning for Edge Intelligence in Wireless ISAC Networks](https://mlsanity.com/paper/2502.12736/) (2025)

## Abstract (arXiv)

In this paper, we propose a digital twin (DT)-based user-centric approach for processing sensing data in an integrated sensing and communication (ISAC) system with high accuracy and efficient resource utilization. The considered scenario involves an ISAC device with a lightweight deep neural network (DNN) and a mobile edge computing (MEC) server with a large DNN. After collecting sensing data, the ISAC device either processes the data locally or uploads them to the server for higher-accuracy data processing. To cope with data drifts, the server updates the lightweight DNN when necessary, referred to as continual learning. Our objective is to minimize the long-term average computation cost of the MEC server by optimizing two decisions, i.e., sensing data offloading and sensing data selection for the DNN update. A DT of the ISAC device is constructed to predict the impact of potential decisions on the long-term computation cost of the server, based on which the decisions are made with closed-form formulas. Experiments on executing DNN-based human motion recognition tasks are conducted to demonstrate the outstanding performance of the proposed DT-based approach in computation cost minimization.