Circuit Power Consumption-Unaware Energy Efficiency Optimization for Massive MIMO Systems

Liu, Zujun; Li, Jianlong; Sun, Dechun

doi:10.1109/lwc.2017.2693373

Cited by 17 publications

(16 citation statements)

References 12 publications

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“…Besides, the perfect CSI is assumed to be available at BS to generate the correlation channel and high channel gain for different users as high -dimensional channel vector. To offer data rate transmission to users with a transmission target, the target data rate of the transmitting antenna [5] can be written as…”

Section: System Model a Received Signal Modelmentioning

confidence: 99%

“…Energy consumption has become a critical concern for any communication platform since it is used for hardware, power amplifier, and UE to achieve better EE. Massive MIMO systems require additional hardware circuitries, which consume more energy compared with single-antenna systems [4], [5].…”

Section: Introductionmentioning

confidence: 99%

“…To maximize EE involves designing a large antenna for transmitting signals, which requires accounting for transmitted power. Meanwhile, more transmission antennas in the BS will serve the distributed UEs in the same cells with the RF chains working to reduce circuit power consumption [5]. The problem can be alleviated in a single cell due to the small distance between the BS and UEs.…”

Section: Introductionmentioning

confidence: 99%

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Energy-Efficient Power Allocation and Joint User Association in Multiuser-Downlink Massive MIMO System

et al. 2020

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Singular value decomposition is highly essential to achieve a higher performance in signal processing using massive multiple-input multiple-output (MIMO) systems. This paper aims to provide a solution to control power allocation problem identified as an essential metric in a massive MIMO system that maximizes energy efficiency (EE). The network performance was evaluated by measuring circuit power consumption to maximize EE. The computational efficiency to maximize EE power allocation is very important to fifth generation networks (5G). The study aims to maximize the non-convex EE in a downlink (DL) massive MIMO system using a proposed energy-efficient low-complexity algorithm (EELCA) that guarantees optimal power allocation solution based on Newton's methods and joint user's association based on the Lagrange's decomposition method. An optimal power allocation solution in closed form to decrease the complexity of the power subject to both the maximum power and minimum data rate constrained systems was derived. Then, the unconstrained EE power allocation to solve the unconstrained optimal power was used to select the optimal power allocation by computing a root of the first derivative of the EE based on differentiating the instantaneous power allocation to maximize EE was formulated. Simulation results showed that the proposed EELCA with a total transmitted power allocation provided maximum EE for a large number of antennas at the base station (BS), Generally, non-linear schemes outperformed linear schemes. Finally, the large cost of circuit power consumption increased at the BS due to the large loss of radio frequency (RF) chains at every antenna when the signals were transmitted to all users. The maximum EE = 5.9 Mbits/joule when the number of distributed users, K = 33, with (p c , M) = (1000mW, 200). The proposed low complexity algorithm provides the better result EE based on a training channel for a number of distributed users.

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Section: System Model a Received Signal Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Energy-Efficient Power Allocation and Joint User Association in Multiuser-Downlink Massive MIMO System

et al. 2020

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“…However, the usage of a large number of base station (BS) antennas in massive MIMO can significantly increase the radio frequency (RF) circuits and digital signal processing (DSP) power consumption, which has a severe impact on EE [3]. Because of the rapidly rising energy costs and the tremendous carbon footprints of existing systems, EE is gradually accepted as an important design criterion for future communication systems [4,5]. Consequently, research interests are steered to adopt energy-aware network architectures and adjust their operational parameters to optimize the EE performance [6,7].…”

Section: Introductionmentioning

confidence: 99%

Energy Efficiency-Oriented Resource Allocation for Massive MIMO Systems with Separated Channel Estimation and Feedback

Feng

Wang

et al. 2020

Electronics

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This paper proposes a dynamic resource allocation scheme to maximize the energy efficiency (EE) for Massive MIMO Systems. The imperfect channel estimation (CE) and feedback are explicitly considered in the EE maximization problem, which aim to optimize the power allocation, the antenna subset selection for transmission, and the pilot assignment. Assuming CE error to be bounded for the complex-constrained Cramer–Rao Bound (CRB), theoretical results show that the lower bound is directly proportional to its number of unconstrained parameters. Utilizing this perspective, a separated and bi-directional estimation is developed to achieve both low CRB and low complexity by exploiting channel and noise spatial separation. Exploiting global optimization procedure, the optimal resource allocation can be transformed into a standard convex optimization problem. This allows us to derive an efficient iterative algorithm for obtaining the optimal solution. Numerical results are provided to demonstrate that the outperformance of the proposed algorithms are superior to existing schemes.

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“…Reducing interference [13] is more effective to gain maximum EE by using spatially dynamic power control in device-to-device networks. Deploying large BS antennas by using scalable optimal power transfer efficiency model was proposed [14] to improve the EE. The optimization of EE is achieved without knowledge of circuit PC [15] by large BS antennas.…”

Section: Introductionmentioning

confidence: 99%

Massive MIMO: Achievable Energy Efficiency for 5G systems with Multi-user Environment

Rayi¹,

Prasad²

2019

IJRTE

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Massive Multi-Input and Multi-Output (MIMO) antenna system potentially provides a promising solution to improve energy efficiency (EE) for 5G wireless systems. The aim of this paper is to enhance EE and its limiting factors are explored. The maximum EE of 48 Mbit/Joule was achieved with 15 user terminal (UT)s. This problem is related to the uplink spectral efficiency with upper bound for future wireless networks. The maximal EE is obtained by optimizing a number of base station (BS) antennas, pilot reuse factor, and BSs density. We presented a power consumption model by deriving Shannon capacity calculations with closed-form expressions. The simulation result highlights the EE maximization with optimizing variables of circuit power consumption, hardware impairments, and path-loss exponent. Small cells achieve high EE and saturate to a constant value with BSs density. The MRC scheme achieves maximum EE of 36 Mbit/Joule with 12 UTs. The simulation results show that peak EE is obtained by deploying massive BS antennas, where the interference and pilot contamination are mitigated by coherent processing. The simulation results were implemented by using MATLAB 2018b.

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Circuit Power Consumption-Unaware Energy Efficiency Optimization for Massive MIMO Systems

Cited by 17 publications

References 12 publications

Energy-Efficient Power Allocation and Joint User Association in Multiuser-Downlink Massive MIMO System

Energy-Efficient Power Allocation and Joint User Association in Multiuser-Downlink Massive MIMO System

Energy Efficiency-Oriented Resource Allocation for Massive MIMO Systems with Separated Channel Estimation and Feedback

Massive MIMO: Achievable Energy Efficiency for 5G systems with Multi-user Environment

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