mlsanityData to 14 Oct 2026

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

arXiv 2606.12078 ↗First posted 10 Jun 2026eess.SP cs.SY eess.SYcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

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.

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.

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10 of 23 references.

References · 10

  1. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,240
  2. 2020Radar-assisted Predictive Beamforming for Vehicular Links: Communication Served by Sensingcited by 12
  3. 2020Enabling Joint Communication and Radar Sensing in Mobile Networks -- A Surveycited by 10
  4. 2022Throughput Maximization for UAV-enabled Integrated Periodic Sensing and Communicationcited by 8
  5. 2022Sensing as a Service in 6G Perceptive Networks: A Unified Framework for ISAC Resource Allocationcited by 7
  6. 2022Deep Learning-based Link Configuration for Radar-aided Multiuser mmWave Vehicle-to-Infrastructure Communicationcited by 6
  7. 2022A Prescriptive Dirichlet Power Allocation Policy with Deep Reinforcement Learningcited by 4
  8. 2019Framework for a Perceptive Mobile Network using Joint Communication and Radar Sensingcited by 2
  9. 2024Networked Integrated Sensing and Communications for 6G Wireless Systemscited by 2
  10. 2023Integrated Sensing and Channel Estimation by Exploiting Dual Timescales for Delay-Doppler Alignment Modulationcited by 1

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  2. 2025Learning-Based Resource Management in Integrated Sensing and Communication Systems
  3. 2025Reinforcement Learning for Enhancing Sensing Estimation in Bistatic ISAC Systems with UAV Swarms
  4. 2024Integrated Sensing and Communications for Low-Altitude Economy: A Deep Reinforcement Learning Approach
  5. 2024Synesthesia of Machines (SoM)-Enhanced ISAC Precoding for Vehicular Networks with Double Dynamics
  6. 2025Beam Selection in ISAC using Contextual Bandit with Multi-modal Transformer and Transfer Learning
  7. 2025Efficient Beam Selection for ISAC in Cell-Free Massive MIMO via Digital Twin-Assisted Deep Reinforcement Learning
  8. 2024Enhancing Battlefield Awareness: An Aerial RIS-assisted ISAC System with Deep Reinforcement Learning ↗