EconPapers    
Economics at your fingertips  
 

Enhancing IoT Connectivity in Massive MIMO Networks through Systematic Scheduling and Power Control Strategies

Byung Moo Lee ()
Additional contact information
Byung Moo Lee: Department of Intelligent Mechatronics Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of Korea

Mathematics, 2023, vol. 11, issue 13, 1-18

Abstract: Massive MIMO systems can support a large number of Internet of Things (IoT) devices, even if the number of IoT devices exceeds the number of service antennas in a single base station (BS) located at the data center. In order to improve the performance of Massive MIMO with massive IoT connectivity in a BS, simple scheduling and power control schemes can be of great help, but typically, they require high power consumption in the situation of serious shadow fading. In this paper, we try to improve the performance of Massive MIMO with massive IoT connectivity by using the dropping technique that drops the IoT devices that require high power consumption. Several scheduling and power control schemes have been proposed to increase the spectral efficiency (SE) and the energy efficiency (EE) of Massive MIMO systems. By the combination of these schemes with the dropping technique, we show that the performance can be even further increased under some circumstances. There is a dropping coefficient factor (DCF) to determine the IoT devices that should be dropped. This technique gives more benefits to the power control schemes that require higher power consumption. Simulation results and relevant analyses are provided to verify the effectiveness of the proposed technique.

Keywords: massive connectivity; Internet of Things; energy efficiency; Massive MIMO; scheduling (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/13/3012/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/13/3012/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:13:p:3012-:d:1188480

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().

 
Page updated 2025-03-19
Handle: RePEc:gam:jmathe:v:11:y:2023:i:13:p:3012-:d:1188480