The increasing demand for communication is degrading the electromagnetic (EM) transmission environment due to severe EM interference, significantly reducing the efficiency of the radio frequency (RF) spectrum. Metasurfaces, a promising technology for controlling desired EM waves, have recently received significant attention from both academia and industry. However, the potential impact of out-of-band signals has been largely overlooked, leading to RF spectrum pollution and degradation of wireless transmissions. To address this issue, we propose a novel surface structure called the Filtering Reconfigurable Intelligent Computational Surface (FRICS). We introduce two types of FRICS structures: one that dynamically reflects resonance band signals through a tunable spatial filter while absorbing out-of-band signals using metamaterials and the other one that dynamically amplifies in-band signals using computational metamaterials while reflecting out-of-band signals. To evaluate the performance of FRICS, we implement it in device-to-device (D2D) communication and vehicular-to-everything (V2X) scenarios. The experiments demonstrate the superiority of FRICS in signal-to-interference-noise ratio (SINR) and energy efficiency (EE). Finally, we discuss the critical challenges faced and promising techniques for implementing FRICS in future wireless systems.

翻译：日益增长的通信需求正因严重的电磁干扰而恶化电磁传输环境，显著降低了射频频谱的利用效率。超表面作为一种调控期望电磁波的前沿技术，近年来受到了学术界和工业界的广泛关注。然而，带外信号的潜在影响在很大程度上被忽视，这导致了射频频谱污染和无线传输质量的下降。为解决这一问题，我们提出了一种新颖的表面结构，称为滤波可重构智能计算表面。我们引入了两种类型的FRICS结构：一种通过可调空间滤波器动态反射谐振频带信号，同时利用超材料吸收带外信号；另一种则利用计算超材料动态放大带内信号，同时反射带外信号。为评估FRICS的性能，我们在设备到设备通信和车联万物通信场景中进行了实现。实验结果表明，FRICS在信号与干扰加噪声比和能量效率方面均表现出优越性。最后，我们讨论了在未来无线系统中实现FRICS所面临的关键挑战以及具有前景的潜在技术。