# Deep Reinforcement Learning for Adaptive Power Allocation in ISAC Systems with Mobile Target

arXiv: https://arxiv.org/abs/2606.12078 · HTML: https://mlsanity.com/paper/2606.12078/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 10 Jun 2026.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 10 of 10)

- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [Radar-assisted Predictive Beamforming for Vehicular Links: Communication Served by Sensing](https://mlsanity.com/paper/2001.09306/) (2020)
- [Enabling Joint Communication and Radar Sensing in Mobile Networks -- A Survey](https://mlsanity.com/paper/2006.07559/) (2020)
- [Throughput Maximization for UAV-enabled Integrated Periodic Sensing and Communication](https://mlsanity.com/paper/2203.06358/) (2022)
- [Sensing as a Service in 6G Perceptive Networks: A Unified Framework for ISAC Resource Allocation](https://mlsanity.com/paper/2202.09969/) (2022)
- [Deep Learning-based Link Configuration for Radar-aided Multiuser mmWave Vehicle-to-Infrastructure Communication](https://mlsanity.com/paper/2201.04657/) (2022)
- [A Prescriptive Dirichlet Power Allocation Policy with Deep Reinforcement Learning](https://mlsanity.com/paper/2201.08445/) (2022)
- [Framework for a Perceptive Mobile Network using Joint Communication and Radar Sensing](https://mlsanity.com/paper/1901.05558/) (2019)
- [Networked Integrated Sensing and Communications for 6G Wireless Systems](https://mlsanity.com/paper/2405.16398/) (2024)
- [Integrated Sensing and Channel Estimation by Exploiting Dual Timescales for Delay-Doppler Alignment Modulation](https://mlsanity.com/paper/2310.11326/) (2023)

## Abstract (arXiv)

In this paper, we study the power allocation for an integrated sensing and communication (ISAC) system which tracks a mobile target. We first model the problem as a Markov decision process, and then tackle it with a soft actor-critic (SAC) based deep reinforcement learning (DRL) approach. We also combine a Dirichlet policy, which naturally produces normalized continuous actions under random target motion. To exploit different features of sensing and communication operations, we carefully design a reward function such that the system can dynamically control power allocation to conserve resources. The simulation results demonstrate that the proposed scheme enhances tracking performance compared to other baselines while sustaining communication performance.