论文标题
强大的模仿和智能反射表面的力量缩放定律和近场行为
Power Scaling Laws and Near-Field Behaviors of Massive MIMO and Intelligent Reflecting Surfaces
论文作者
论文摘要
大型阵列的使用可能是解决无线通信的容量问题的解决方案。使用大量的MIMO接收器和半双链继电器时，信噪比（SNR）随数组元素$ n $的数量线性增长。此外，智能反射表面（IRS）最近引起了人们的注意，因为这些可以传递信号以实现成长为$ n^2 $的SNR，这似乎是一个重大好处。在本文中，我们为任意大小的平面阵列使用确定性的繁殖模型，以证明所述的SNR行为和相关的功率缩放定律仅适用于远场。他们不能用来研究$ n \ to \ infty $的制度。我们得出了确切的渠道增益表达式，该表达式捕获了三种基本的近场行为，并使用它来重新审视功率缩放定律。我们得出了新的有限渐近SNR限制，但也得出结论，实际上不太可能在实践中处理这些限制。我们进一步证明，尽管SNR的增长速度更快，但IRS辅助设置仍无法获得比同等大小的MIMO设置更高的SNR。我们通过分析量化了IRS要实现相同SNR必须有多大的数量。最后，我们表明，优化的IRS并不是“异常”的镜子，但可以极大地表现基准。
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.
