RIS-aided MIMO Beamforming: Piece-Wise Near-field Channel Model
<|reference_start|>RIS-aided MIMO Beamforming: Piece-Wise Near-field Channel Model: This paper proposes a joint active and passive beamforming design for reconfigurable intelligent surface (RIS)-aided wireless communication systems, adopting a piece-wise near-field channel model. While a traditional near-field channel model, applied without any approximations, offers higher modeling accuracy than a far-field model, it renders the system design more sensitive to channel estimation errors (CEEs). As a remedy, we propose to adopt a piece-wise near-field channel model that leverages the advantages of the near-field approach while enhancing its robustness against CEEs. Our study analyzes the impact of different channel models, including the traditional near-field, the proposed piece-wise near-field and far-field channel models, on the interference distribution caused by CEEs and model mismatches. Subsequently, by treating the interference as noise, we formulate a joint active and passive beamforming design problem to maximize the spectral efficiency (SE). The formulated problem is then recast as a mean squared error (MSE) minimization problem and a suboptimal algorithm is developed to iteratively update the active and passive beamforming strategies. Simulation results demonstrate that adopting the piece-wise near-field channel model leads to an improved SE compared to both the near-field and far-field models in the presence of CEEs. Furthermore, the proposed piece-wise near-field model achieves a good trade-off between modeling accuracy and system's degrees of freedom (DoF).<|reference_end|>
@article{chen2024ris-aided,
    title={RIS-aided MIMO Beamforming: Piece-Wise Near-field Channel Model},
    author={Weijian Chen, Zai Yang, Zhiqiang Wei, Derrick Wing Kwan Ng, and
  Michail Matthaiou},
    journal={arXiv preprint arXiv:2406.14939},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.14939},
    primaryClass={cs.IT eess.SP math.IT}
}