Instituto Nacional de Telecomunicações-INATEL, Santa Rita do Sapucaí 37540-000, MG, Brazil.
IDLab, Department of Information Technology at Ghent University-IMEC, 9052 Ghent, Belgium.
Sensors (Basel). 2020 Jan 17;20(2):520. doi: 10.3390/s20020520.
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.
大规模天线阵列的使用由于空间分辨率和阵列增益技术，为无线系统提供了相当大的能量和频谱效率收益。在大规模多输入多输出场景中假设主导视线路径环境，我们推导出了和容量的解析表达式。然后，我们表明，在低信噪比和高信噪比两种情况下，当工作在低和高信噪比区域时，和容量表达式可以进行方便的简化。此外，在低和高信噪比情况下，证明了当服务设备和基站天线数量增加时，伽马概率密度函数可以分别逼近瞬时信道和容量的概率密度函数。本工作中的另一个重要证明是，伽马概率密度函数也可以用来近似随着设备数量的增加，信道奇异值之和的概率密度函数。最后，值得强调的是，所提出的框架对于大量物联网设备是有用的，因为我们表明每个设备的发射功率可以与基站天线数量成反比。
