Abstract
In this paper, the power transmission and energy efficiency (EE) in downlink multi-cell massive multiple-input-multiple-output (MIMO) systems are investigated and optimized. Most of the existing works do not take into account different user's quality of service (QoS) requirements. These models also depend on a fixed transmit power consumption, which cannot reflect the actual EE levels concerning QoS. Therefore, in this paper, a new base station (BS) transmit power adaptation is firstly introduced, termed the BSTPA method. The transmitted power is adapted to channel condition and user-level QoS including data rate requirement and maximum allowable outage probability to minimize the total BS radiated power. An analytical closed-form expression of the average BS transmit power adaptation is derived. Then, a corresponding iterative optimization algorithm is proposed to maximize the average EE per BS and obtain the optimal design parameters. The proposed optimization algorithm aims to globally achieve the optimal EE value with the optimal amount of data rate, the number of BS antennas, and users. Simulation results are demonstrated to verify our analytical findings. For a wide range of different design parameters, the results indicate that the proposed method obtains remarkably higher EE levels compared to the conventional scenario, particularly if per-antenna circuit power is very small. The optimization results show that the case with lower per-antenna circuit power can achieve about 4.5 times better EE gain than the case with higher per-antenna circuit power with 13.3% optimum data rate improvement.
Highlights
G LOBAL mobile communication data traffic is forecasted to increase seven-fold by 2022 [1] exponentially, which would place a burden on the generation’s mobile networks
We investigate and analyze how different system design parameters and quality of service (QoS) constraints impact the average base station (BS) transmit power and average EE per BS
We propose a corresponding iterative optimization algorithm to maximize the average EE per BS and obtain the optimal design parameters
Summary
G LOBAL mobile communication data traffic is forecasted to increase seven-fold by 2022 [1] exponentially, which would place a burden on the generation’s mobile networks This Overgrowth traffic has led to social and economic concerns due to the power expenditure of the information technology industry and the pollution caused by the need for enormous energy consumption relatively [2]. This concern has urged the academicians and industry to take serious action in the green cellular network, which is a new area of research [3]. As a matter of fact, the massive MIMO technology is capa-
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