Energy Efficiency Optimization of 5G Radio Frequency Chain Systems

TL;DR

This paper proposes novel algorithms to optimize energy efficiency in 5G systems with massive MIMO and millimeter wave technology, significantly reducing energy consumption and RF costs.

Contribution

It introduces the EEHP and EEHP-MRFC algorithms for energy-efficient hybrid precoding, addressing cost and energy challenges in 5G RF chain systems.

Findings

EEHP improves energy efficiency by 220% over conventional methods.

EEHP-MRFC reduces RF chain costs by 171%.

Algorithms optimize antenna and user equipment numbers for better efficiency.

Abstract

With the massive multi-input multi-output (MIMO) antennas technology adopted for the fifth generation (5G) wireless communication systems, a large number of radio frequency (RF) chains have to be employed for RF circuits. However, a large number of RF chains not only increase the cost of RF circuits but also consume additional energy in 5G wireless communication systems. In this paper we investigate energy and cost efficiency optimization solutions for 5G wireless communication systems with a large number of antennas and RF chains. An energy efficiency optimization problem is formulated for 5G wireless communication systems using massive MIMO antennas and millimeter wave technology. Considering the nonconcave feature of the objective function, a suboptimal iterative algorithm, i.e., the energy efficient hybrid precoding (EEHP) algorithm is developed for maximizing the energy efficiency of 5G wireless communication systems. To reduce the cost of RF circuits, the energy efficient hybrid precoding with the minimum number of RF chains (EEHP-MRFC) algorithm is also proposed. Moreover, the critical number of antennas searching (CNAS) and user equipment number optimization (UENO) algorithms are further developed to optimize the energy efficiency of 5G wireless communication systems by the number of transmit antennas and UEs. Compared with the maximum energy efficiency of conventional zero-forcing (ZF) precoding algorithm, numerical results indicate that the maximum energy efficiency of the proposed EEHP and EEHP-MRFC algorithms are improved by 220% and 171%, respectively.