用强化学习提升雷达通信一体化系统的抗干扰能力
RL-Aided Cognitive ISAC: Robust Detection and Sensing-Communication Trade-offs
- 用SARSA强化学习自适应估计目标位置，无需环境先验
- 在非高斯杂波下检测概率显著高于基线方法
- 可动态平衡探测精度与通信速率，适合未来无线网络
本文提出一种基于强化学习的认知框架，用于基于大规模MIMO的集成感知与通信（ISAC）系统，采用均匀平面阵列（UPA）。重点提升在未知且动态干扰环境下的雷达感知性能。采用Wald型检测器实现非高斯杂波下的鲁棒目标检测，同时利用SARSA强化学习算法实现无需环境先验的目标位置自适应估计。基于强化学习获得的感知信息，制定联合波形优化策略，以平衡雷达感知精度与下行链路通信吞吐量。所提方案通过解析推导的闭式解，实现检测性能与可达总速率之间的自适应权衡。蒙特卡洛仿真表明，相比正交和非学习自适应基线，该认知ISAC框架在保持竞争力通信性能的同时，显著提升了检测概率。结果凸显了强化学习辅助感知在下一代无线网络中实现鲁棒、频谱高效ISAC的潜力。
原文摘要 · Abstract (English)
This paper proposes a reinforcement learning (RL)-aided cognitive framework for massive MIMO-based integrated sensing and communication (ISAC) systems employing a uniform planar array (UPA). The focus is on enhancing radar sensing performance in environments with unknown and dynamic disturbance characteristics. A Wald-type detector is employed for robust target detection under non-Gaussian clutter, while a SARSA-based RL algorithm enables adaptive estimation of target positions without prior environmental knowledge. Based on the RL-derived sensing information, a joint waveform optimization strategy is formulated to balance radar sensing accuracy and downlink communication throughput. The resulting design provides an adaptive trade-off between detection performance and achievable sum rate through an analytically derived closed-form solution. Monte Carlo simulations demonstrate that the proposed cognitive ISAC framework achieves significantly improved detection probability compared to orthogonal and non-learning adaptive baselines, while maintaining competitive communication performance. These results underline the potential of RL-assisted sensing for robust and spectrum-efficient ISAC in next-generation wireless networks.
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