The use of large-scale antenna arrays grants considerable benefits in energy and spectral efficiency to wireless systems due to spatial resolution and array gain techniques. By assuming a dominant line-of-sight environment in a massive multiple-input multiple-output scenario, we derive analytical expressions for the sum-capacity. Then, we show that convenient simplifications on the sum-capacity expressions are possible when working at low and high signal-to-noise ratio regimes. Furthermore, in the case of low and high signal-to-noise ratio regimes, it is demonstrated that the Gamma probability density function can approximate the probability density function of the instantaneous channel sum-capacity as the number of served devices and base station antennas grows, respectively. A second important demonstration presented in this work is that a Gamma probability density function can also be used to approximate the probability density function of the summation of the channel's singular values as the number of devices increases. Finally, it is important to highlight that the presented framework is useful for a massive number of Internet of Things devices as we show that the transmit power of each device can be made inversely proportional to the number of base station antennas.

中文翻译：

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视线传播环境中密集大规模MIMO的容量限制。

由于空间分辨率和阵列增益技术的原因，大规模天线阵列的使用在无线系统的能量和频谱效率方面具有可观的优势。通过在大规模多输入多输出场景中假设一个主导的视线环境，我们得出了求和能力的解析表达式。然后，我们表明在低信噪比和高信噪比条件下工作时，可以简化求和能力表达式。此外，在低信噪比和高信噪比情况下，证明随着服务设备和基站天线数量的增加，伽马概率密度函数可以近似瞬时信道总容量的概率密度函数。 ， 分别。这项工作中提出的第二个重要证明是，随着设备数量的增加，伽马概率密度函数也可以用于近似信道奇异值总和的概率密度函数。最后，重要的是要突出显示的框架对大量的物联网设备很有用，因为我们表明每个设备的发射功率可以与基站天线的数量成反比。