Abstract
Massive Multiple-Input Multiple-Output (Massive MIMO) is widely regarded as a highly promising technology for the forthcoming generation of wireless systems. The massive MIMO implementation involves the integration of a substantial number of antenna elements into base stations (BSs) to enhance spectral efficiency (SE) and energy efficiency (EE). The energy efficiency (EE) of base stations (BSs) has become an increasingly important issue for telecommunications network operators due to the need to take care of profitability while simultaneously minimizing their detrimental effects on the environment and addressing economic challenges faced by wireless communication operators. In this paper, the EE of massive MIMO networks and the relationship between EE, SE, and other parameters like bandwidth (B), number of antennas (M), circuit power, and number of users’ equipment (K) are discussed and investigated. For a fixed circuit power (PFIX), simulation results showed that the EE could be increased by about 1.12 as the number of antennas was doubled. The findings in this work also indicated an almost linear relationship between maximum EE and optimal SE, with a massive increase in the number of antennas when the power consumed by each antenna (PBS) was included in circuit power. In addition, when considering the power consumed per user’s equipment (PUE) impact, the SE increased with the ratio (M/K), in which SE showed a cubic relationship against M/K. On the other hand, the EE increased with M/K ratio until M/K reached a specific value. The maximum EE (and hence optimum SE) was achieved by massive MIMO, where the number of antennas was three times the number of users. However, EE started degrading after this value, as the number of antennas was considered larger than the users’ and consumed more energy, resulting in EE degradation.
Subject
Electrical and Electronic Engineering,Mechanical Engineering,Environmental Science (miscellaneous),Chemical Engineering (miscellaneous),Civil and Structural Engineering,Environmental Engineering
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