# Learning-Based Resource Management in Integrated Sensing and Communication Systems

arXiv: https://arxiv.org/abs/2506.20849 · HTML: https://mlsanity.com/paper/2506.20849/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 19 references that are in the map.
- First posted on arXiv 25 Jun 2025.

## Builds on (most cited first, 4 of 4)

- [Reward Constrained Policy Optimization](https://mlsanity.com/paper/1805.11074/) (2018)
- [Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyond](https://mlsanity.com/paper/2108.07165/) (2021)
- [A Survey on Fundamental Limits of Integrated Sensing and Communication](https://mlsanity.com/paper/2104.09954/) (2021)
- [Deep Reinforcement Learning Control for Radar Detection and Tracking in Congested Spectral Environments](https://mlsanity.com/paper/2006.13173/) (2020)

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

- [Constrained Deep Reinforcement Learning for Cognitive Radar Resource Management](https://mlsanity.com/paper/2606.05526/) (2026)

## Abstract (arXiv)

In this paper, we tackle the task of adaptive time allocation in integrated sensing and communication systems equipped with radar and communication units. The dual-functional radar-communication system's task involves allocating dwell times for tracking multiple targets and utilizing the remaining time for data transmission towards estimated target locations. We introduce a novel constrained deep reinforcement learning (CDRL) approach, designed to optimize resource allocation between tracking and communication under time budget constraints, thereby enhancing target communication quality. Our numerical results demonstrate the efficiency of our proposed CDRL framework, confirming its ability to maximize communication quality in highly dynamic environments while adhering to time constraints.