The use of large arrays might be the solution to the capacity problems in wireless communications. The signal-to-noise ratio (SNR) grows linearly with the number of array elements $N$ when using Massive MIMO receivers and half-duplex relays. Moreover, intelligent reflecting surfaces (IRSs) have recently attracted attention since these can relay signals to achieve an SNR that grows as $N^2$, which seems like a major benefit. In this paper, we use a deterministic propagation model for a planar array of arbitrary size, to demonstrate that the mentioned SNR behaviors, and associated power scaling laws, only apply in the far-field. They cannot be used to study the regime where $N\to\infty$. We derive an exact channel gain expression that captures three essential near-field behaviors and use it to revisit the power scaling laws. We derive new finite asymptotic SNR limits but also conclude that these are unlikely to be approached in practice. We further prove that an IRS-aided setup cannot achieve a higher SNR than an equal-sized Massive MIMO setup, despite its faster SNR growth. We quantify analytically how much larger the IRS must be to achieve the same SNR. Finally, we show that an optimized IRS does not behave as an "anomalous" mirror but can vastly outperform that benchmark.

翻译：大规模阵列可能是解决无线通信容量问题的方案。当采用大规模MIMO接收机和半双工中继时，信噪比（SNR）随阵列单元数$N$线性增长。此外，智能反射面（IRS）近年来备受关注，因其可实现SNR随$N^2$增长的中继信号传输，这似乎是一个重大优势。本文采用适用于任意规模平面阵列的确定性传播模型，证明上述SNR行为及其关联的功率标度律仅适用于远场场景，无法用于研究$N\to\infty$的极限情况。我们推导出能捕捉三种关键近场行为的精确信道增益表达式，并据此重新审视功率标度律。我们得到了新的有限渐近SNR极限，但指出这些极限在实际中难以达到。进一步证明，尽管IRS辅助系统的SNR增速更快，但其无法达到同等尺寸大规模MIMO系统的SNR。我们通过解析计算量化了IRS需达到何种尺寸才能获取相同SNR。最后表明，优化后的IRS并非表现为"反常"反射镜，而是能显著超越该基准性能。