基于学习的波束自适应用于主动/被动感知单脉冲ISAC与泄漏抑制
Learning-Based Beam Adaptation for Active/Passive-Aware Monopulse ISAC with Leakage Suppression
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中文总结 AI 辅助
本文提出基于软演员-评论家强化学习的波束自适应框架，用于单脉冲ISAC系统，通过融合雷达与上行信息区分主动/被动方向并抑制泄漏，在被动目标增多时保持低泄漏和高总速率。
中文摘要 AI 辅助
本文提出了一种基于学习的波束自适应框架，用于具有空间泄漏抑制的主动/被动感知单脉冲集成感知与通信（ISAC）。单个gNB使用共享阵列和正交频分复用双功能雷达通信信号，联合支持下行传输和单脉冲感知。雷达检测和上行空间证据被融合以区分合作主动方向与被动方向，同时一个受保护的角度区域限制了非预期照射。软演员-评论家深度强化学习利用通信、感知、泄漏和先前动作反馈来调整发射波束。数值结果表明，随着被动目标数量从1个增加到12个，主动/被动空间分类准确且泄漏控制稳健。所提出的SAC控制器在整个扫描范围内将总被动泄漏保持在预设预算以下，并且从两个被动目标起，在所考虑的基线中实现了最高的符合泄漏限制的总速率。
英文摘要
This paper proposes a learning-based beam adaptation framework for active/passive-aware monopulse integrated sensing and communication (ISAC) with spatial leakage suppression. A single gNB uses a shared array and orthogonal frequency division multiplexing dual-functional radar-communication signaling to jointly support downlink transmission and monopulse sensing. Radar detections and uplink spatial evidence are fused to distinguish cooperative active directions from passive directions, while a protected angular zone limits unintended illumination. Soft Actor-Critic deep reinforcement learning adapts the transmit beam using communication, sensing, leakage, and previous-action feedback. Numerical results show accurate active/passive spatial classification and robust leakage control as the number of passive targets increases from one to twelve. The proposed SAC controller maintains aggregate passive leakage below the prescribed budget across the full sweep and, from two passive targets onward, achieves the highest leakage-compliant sum rate among the considered baselines.
发表机构
- Rochester Institute of Technology（罗切斯特理工学院）
机构由 AI 辅助整理，请以论文原文为准。
