Energy Efficiency in Multi-Cell Massive MIMO: Impact of Signal Processing Schemes and Hardware Implementation Parameters

This paper investigates the trade-off between energy efficiency and spectral efficiency in multi-cell massive MIMO systems. The relevance of the study is driven by the need to simultaneously increase throughput and reduce the energy consumption of base stations in next-generation wireless networks, given the growing number of antennas and served users.

The purpose of the study is to determine how the number of base-station antennas, the number of served users, linear signal processing schemes, and hardware implementation parameters affect the energy efficiency of multi-cell massive MIMO systems, and to identify configurations that provide the best trade-off between energy and spectral efficiency.

Methods. A mathematical model of a massive MIMO system is developed, taking into account the number of base station antennas M, the number of user equipment K, and various linear signal processing schemes, including MR, ZF, RZF, S-MMSE, and M-MMSE. The model incorporates power consumption parameters reflecting hardware implementation characteristics, represented by two different sets of component specifications. The evaluation of energy and spectral efficiency is carried out using simulation-based analysis for various system configurations.

Results. The results show that an optimal antenna-to-user ratio of M/K ≈ 3–4 achieves maximum energy efficiency without a significant reduction in spectral efficiency. It is demonstrated that the M-MMSE and S-MMSE algorithms provide the highest energy efficiency performance with moderate computational complexity, particularly when improved hardware components are employed. The obtained results confirm the existence of a pronounced energy efficiency optimum as the number of base station antennas increases.

The novelty of this work lies in the comprehensive analysis of the energy–spectral efficiency trade-off in multi-cell massive MIMO systems while jointly accounting for linear signal processing schemes and hardware implementation parameters, which enables the formulation of practical recommendations for base station configuration under technological constraints.

Practical significance. The findings of this study can be applied to the design and optimization of energy-efficient multi-cell massive MIMO systems for next-generation wireless communication networks, taking into account hardware implementation constraints and quality-of-service requirements.

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