Filtering Reconfigurable Intelligent Computational Surface for RF Spectrum Purification
<|reference_start|>Filtering Reconfigurable Intelligent Computational Surface for RF Spectrum Purification: 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.<|reference_end|>
@article{wang2024filtering,
    title={Filtering Reconfigurable Intelligent Computational Surface for RF
  Spectrum Purification},
    author={Kaining Wang, Bo Yang, Zhiwen Yu, Xuelin Cao, M'erouane Debbah, Chau
  Yuen},
    journal={arXiv preprint arXiv:2406.18055},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.18055},
    primaryClass={cs.IT eess.SP math.IT}
}