Radio frequency interference (RFI) is a significant threat to astronomical observations. Thus, this letter exploits the intelligent reflecting surfaces (IRSs) to mitigate RFI by adjusting the reflection coefficients of IRSs. Aiming to synthesize a spatial quiet zone in the control area of a radio telescope, an optimization problem for joint multiple reflected beamforming at IRSs is formulated. As the interference behavior and direction are dynamic, an IRS relative position encoding attention deep deterministic policy gradient (RPEA-DDPG) learning algorithm is proposed to jointly optimize the reflected beamforming of IRSs without the knowledge of the interference model. Simulation results demonstrate that the proposed technique can effectively establish an open electromagnetic field quiet zone to prevent RFI from entering the receiver.

中文翻译：

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射电天文学中具有深度强化学习的智能反射表面辅助干扰抑制


射频干扰（RFI）是对天文观测的重大威胁。因此，这封信利用智能反射表面 (IRS) 通过调整 IRS 的反射系数来减轻 RFI。为了在射电望远镜控制区域合成一个空间静区，提出了IRS联合多重反射波束形成的优化问题。由于干扰行为和方向是动态的，提出了一种IRS相对位置编码注意深度确定性策略梯度（RPEA-DDPG）学习算法，在不了解干扰模型的情况下联合优化IRS的反射波束形成。仿真结果表明，该技术可以有效建立开放的电磁场静区，防止RFI进入接收机。