Capacity Bounds for Dense Massive MIMO in a Line-of-Sight Propagation Environment
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 ex...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-01-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/2/520 |
| _version_ | 1798034579591790592 |
|---|---|
| author | Felipe A. P. de Figueiredo Claudio F. Dias Eduardo R. de Lima Gustavo Fraidenraich |
| author_facet | Felipe A. P. de Figueiredo Claudio F. Dias Eduardo R. de Lima Gustavo Fraidenraich |
| author_sort | Felipe A. P. de Figueiredo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T20:46:11Z |
| format | Article |
| id | doaj.art-5927c3c02eaa4d4aa68588fa9db15fe3 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T20:46:11Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5927c3c02eaa4d4aa68588fa9db15fe32022-12-22T04:04:02ZengMDPI AGSensors1424-82202020-01-0120252010.3390/s20020520s20020520Capacity Bounds for Dense Massive MIMO in a Line-of-Sight Propagation EnvironmentFelipe A. P. de Figueiredo0Claudio F. Dias1Eduardo R. de Lima2Gustavo Fraidenraich3Instituto Nacional de Telecomunicações—INATEL, Santa Rita do Sapucaí 37540-000, MG, BrazilDECOM/FEEC–State University of Campinas (UNICAMP), Campinas 13083-852, BrazilEldorado Research Institute, Campinas 13083-898, BrazilDECOM/FEEC–State University of Campinas (UNICAMP), Campinas 13083-852, BrazilThe 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.https://www.mdpi.com/1424-8220/20/2/520massive mimochannel capacitydense networksoutage probability |
| spellingShingle | Felipe A. P. de Figueiredo Claudio F. Dias Eduardo R. de Lima Gustavo Fraidenraich Capacity Bounds for Dense Massive MIMO in a Line-of-Sight Propagation Environment Sensors massive mimo channel capacity dense networks outage probability |
| title | Capacity Bounds for Dense Massive MIMO in a Line-of-Sight Propagation Environment |
| title_full | Capacity Bounds for Dense Massive MIMO in a Line-of-Sight Propagation Environment |
| title_fullStr | Capacity Bounds for Dense Massive MIMO in a Line-of-Sight Propagation Environment |
| title_full_unstemmed | Capacity Bounds for Dense Massive MIMO in a Line-of-Sight Propagation Environment |
| title_short | Capacity Bounds for Dense Massive MIMO in a Line-of-Sight Propagation Environment |
| title_sort | capacity bounds for dense massive mimo in a line of sight propagation environment |
| topic | massive mimo channel capacity dense networks outage probability |
| url | https://www.mdpi.com/1424-8220/20/2/520 |
| work_keys_str_mv | AT felipeapdefigueiredo capacityboundsfordensemassivemimoinalineofsightpropagationenvironment AT claudiofdias capacityboundsfordensemassivemimoinalineofsightpropagationenvironment AT eduardordelima capacityboundsfordensemassivemimoinalineofsightpropagationenvironment AT gustavofraidenraich capacityboundsfordensemassivemimoinalineofsightpropagationenvironment |