# Integrated Sensing and Communication from Learning Perspective: An SDP3 Approach

arXiv: https://arxiv.org/abs/2107.09621 · HTML: https://mlsanity.com/paper/2107.09621/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 50 references that are in the map.
- First posted on arXiv 20 Jul 2021.

## Builds on (most cited first, 5 of 5)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Machine Intelligence at the Edge with Learning Centric Power Allocation](https://mlsanity.com/paper/1911.04922/) (2019)
- [Cram'er-Rao Bound Optimization for Joint Radar-Communication Design](https://mlsanity.com/paper/2101.12530/) (2021)
- [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)

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

- [Deep Learning-based Techniques for Integrated Sensing and Communication Systems: State-of-the-Art, Challenges, and Opportunities](https://mlsanity.com/paper/2509.06968/) (2025)
- [Content Accuracy and Quality Aware Resource Allocation Based on LP-Guided DRL for ISAC-Driven AIGC Networks](https://mlsanity.com/paper/2508.12079/) (2025)
- [Dual-Attention and Adversarial Transfer Networks for Sim-to-Real Cross-Orientation Wireless Sensing](https://mlsanity.com/paper/2608.05664/) (2026)

## Abstract (arXiv)

Characterizing the sensing and communication performance tradeoff in integrated sensing and communication (ISAC) systems is challenging in the applications of learning-based human motion recognition. This is because of the large experimental datasets and the black-box nature of deep neural networks. This paper presents SDP3, a Simulation-Driven Performance Predictor and oPtimizer, which consists of SDP3 data simulator, SDP3 performance predictor and SDP3 performance optimizer. Specifically, the SDP3 data simulator generates vivid wireless sensing datasets in a virtual environment, the SDP3 performance predictor predicts the sensing performance based on the function regression method, and the SDP3 performance optimizer investigates the sensing and communication performance tradeoff analytically. It is shown that the simulated sensing dataset matches the experimental dataset very well in the motion recognition accuracy. By leveraging SDP3, it is found that the achievable region of recognition accuracy and communication throughput consists of a communication saturation zone, a sensing saturation zone, and a communication-sensing adversarial zone, of which the desired balanced performance for ISAC systems lies in the third one.