Power Measurement Based Channel Estimation for IRS-Enhanced Wireless Coverage
<|reference_start|>Power Measurement Based Channel Estimation for IRS-Enhanced Wireless Coverage: In this paper, we study an IRS-assisted coverage enhancement problem for a given region, aiming to optimize the passive reflection of the IRS for improving the average communication performance in the region by accounting for both deterministic and random channels in the environment. To this end, we first derive the closed-form expression of the average received signal power in terms of the deterministic base station (BS)-IRS-user cascaded channels over all user locations, and propose an IRS-aided coverage enhancement framework to facilitate the estimation of such deterministic channels for IRS passive reflection design. Specifically, to avoid the exorbitant overhead of estimating the cascaded channels at all possible user locations, a location selection method is first proposed to select only a set of typical user locations for channel estimation by exploiting the channel spatial correlation in the region. To estimate the deterministic cascaded channels at the selected user locations, conventional IRS channel estimation methods require additional pilot signals, which not only results in high system training overhead but also may not be compatible with the existing communication protocols. To overcome this issue, we further propose a single-layer neural network (NN)-enabled IRS channel estimation method in this paper, based on only the average received signal power measurements at each selected location corresponding to different IRS random training reflections, which can be offline implemented in current wireless systems. Numerical results demonstrate that our proposed scheme can significantly improve the coverage performance of the target region and outperform the existing power-measurement-based IRS reflection designs.<|reference_end|>
@article{sun2024power,
    title={Power Measurement Based Channel Estimation for IRS-Enhanced Wireless
  Coverage},
    author={He Sun, Lipeng Zhu, Weidong Mei, Rui Zhang},
    journal={arXiv preprint arXiv:2405.12432},
    year={2024},
    doi={10.1109/TWC.2024.3480314},
    archivePrefix={arXiv},
    eprint={2405.12432},
    primaryClass={cs.IT eess.SP math.IT}
}