Open Access

ARTICLE

Energy-Efficient Resource Optimization for Massive MIMO Networks Considering Network Load

Samira Mujkic^1,*, Suad Kasapovic¹, Mohammed Abuibaid²

1 Department of Telecommunications, Faculty of Electrical Engineering, University of Tuzla, Tuzla, 75000, Bosnia and Herzegovina
2 Department of Systems and Computer Engineering, Faculty of Engineering and Design, Carleton University, Ottawa, K1S 5B6, Canada

* Corresponding Author: Samira Mujkic. Email:

(This article belongs to the Special Issue: Radio Networks for new Disruptive Digital Services in Fourth Industrial Revolution)

Computers, Materials & Continua 2022, 71(1), 871-888. https://doi.org/10.32604/cmc.2022.021441

Received 03 July 2021; Accepted 30 August 2021; Issue published 03 November 2021

Abstract

This paper investigates the resource optimization problem for a multi-cell massive multiple-input multiple-output (MIMO) network in which each base station (BS) is equipped with a large number of antennas and each base station (BS) adapts the number of antennas to the daily load profile (DLP). This paper takes into consideration user location distribution (ULD) variation and evaluates its impact on the energy efficiency of load adaptive massive MIMO system. ULD variation is modeled by dividing the cell into two coverage areas with different user densities: boundary focused (BF) and center focused (CF) ULD. All cells are assumed identical in terms of BS configurations, cell loading, and ULD variation and each BS is modeled as an M/G/m/m state dependent queue that can serve a maximum number of users at the peak load. Together with energy efficiency (EE) we analyzed deployment and spectrum efficiency in our adaptive massive MIMO system by evaluating the impact of cell size, available bandwidth, output power level of the BS, and maximum output power of the power amplifier (PA) at different cell loading. We also analyzed average energy consumption on an hourly basis per BS for the model proposed for data traffic in Europe and also the model proposed for business, residential, street, and highway areas.

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Keywords

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