Energy efficiency analysis for 5G application in massive MIMO systems by using lower bound inequality and SCAM method

Mehdi Nooshyar (), Abdolrasoul Sakhaei Gharagezlou (), Nima Imani (), Elhameh Mikaeili () and Mahdi Nangir ()
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Mehdi Nooshyar: University of Mohaghegh Ardabili
Abdolrasoul Sakhaei Gharagezlou: University of Tabriz
Nima Imani: University of Tabriz
Elhameh Mikaeili: University of Mohaghegh Ardabili
Mahdi Nangir: University of Tabriz

Telecommunication Systems: Modelling, Analysis, Design and Management, 2024, vol. 85, issue 3, No 1, 365-372

Abstract: Abstract Energy efficiency (EE) is one of the most important challenges in fifth generation telecommunication systems. One of the ways to solve this challenge is to allocate the suitable power to users. The goal of the authors in this paper is to solve the challenge of EE in massive MIMO systems. The objective function in the EE optimization problem is a non-convex function and also has two constrained: maximum transmission power and minimum data rate. To convert the objective function into a convex function, maximum ratio transmission (MRT) precoding and the lower bound of the data rate are used. In order to obtain the lower bound of the data rate, the inequality of the lower bound is used. Lagrange dual function is also used to eliminate existing constrained. Since the power of the users is obtained by optimizing a lower bound, the successive convex approximation method (SCAM) can obtain a good result. According to this method, an iterative algorithm is introduced that solves the optimization problem numerically. One of the features of the proposed algorithm is that it has low computational complexity and can reach the optimal value faster than existing algorithms. The simulation results show that the proposed method performs better than the existing methods and has good results in the field of EE of massive MIMO systems.

Keywords: Massive multiple input multiple output (MIMO); Energy efficiency (EE); Power allocation; Lower bound inequality; Lagrange dual function (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11235-023-01077-3

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