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<p>Integrated sensing and communication (ISAC) has emerged as a key enabling technology for sixth-generation (6&#xa0;G) networks, supporting joint environment perception and data transmission with high spectral efficiency and reduced hardware cost. However, the design and deployment of ISAC systems remain challenging due to dynamic wireless environments, sensing–communication trade-offs, and the increasing complexity of large-scale networks. Reinforcement learning (RL) and deep reinforcement learning (DRL) have recently attracted significant attention as data-driven approaches for addressing these challenges through model-free, adaptive, and real-time optimization. This survey first reviews the fundamentals of ISAC technology and then summarizes major RL and DRL algorithms relevant to ISAC design. It further provides a comprehensive overview of RL/DRL methods for ISAC in 6&#xa0;G networks. Existing approaches are categorized into value-based, policy-based, and hybrid methods, and are further classified according to representative ISAC scenarios, including reconfigurable intelligent surface (RIS)-assisted systems, unmanned aerial vehicle (UAV) and satellite systems, vehicular networks, and other emerging 6&#xa0;G applications. The survey highlights reported gains in spectral efficiency, sensing accuracy, adaptability, and robustness, while also identifying key limitations of current approaches. Finally, the survey outlines current challenges, future research directions, and key lessons learned.</p>

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Advancing integrated sensing and communications with RL/DRL in 6 G networks: a survey

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月14日

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  • Wali Ullah Khan,
  • Waqas Khalid,
  • Muhammad Iqbal,
  • Chiew Foong Kwong,
  • Manzoor Ahmed,
  • Syed Tariq Shah

摘要

Integrated sensing and communication (ISAC) has emerged as a key enabling technology for sixth-generation (6 G) networks, supporting joint environment perception and data transmission with high spectral efficiency and reduced hardware cost. However, the design and deployment of ISAC systems remain challenging due to dynamic wireless environments, sensing–communication trade-offs, and the increasing complexity of large-scale networks. Reinforcement learning (RL) and deep reinforcement learning (DRL) have recently attracted significant attention as data-driven approaches for addressing these challenges through model-free, adaptive, and real-time optimization. This survey first reviews the fundamentals of ISAC technology and then summarizes major RL and DRL algorithms relevant to ISAC design. It further provides a comprehensive overview of RL/DRL methods for ISAC in 6 G networks. Existing approaches are categorized into value-based, policy-based, and hybrid methods, and are further classified according to representative ISAC scenarios, including reconfigurable intelligent surface (RIS)-assisted systems, unmanned aerial vehicle (UAV) and satellite systems, vehicular networks, and other emerging 6 G applications. The survey highlights reported gains in spectral efficiency, sensing accuracy, adaptability, and robustness, while also identifying key limitations of current approaches. Finally, the survey outlines current challenges, future research directions, and key lessons learned.