Energy-Efficient Resource Allocation in Uplink Multiuser Massive MIMO Systems

Hu, Ying; Ji, Baofeng; Huang, Yongming; Yu, Fei; Yang, Liu

doi:10.1155/2015/873134

Cited by 12 publications

(6 citation statements)

References 25 publications

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“…Obviously, the circuit power changes dynamically during the information transmission and can be influenced by many factors. However, P CP is often ignored or treated as a constant [4][5][6] in most previous works which may lead to a certain degree of error in the results obtained. To obtain the optimal system EE value by adjusting the numbers of BS antennas and terminal users, we use a more realistic power consumption model in which the circuit power consumption changes dynamically with the signal processing process.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Aiming at solving the problem of EE reduction when using all antennas to transfer information in a massive MIMO system, many experts devote themselves to studying the algorithm to improve the system EE [4][5][6][7][8][9][10][11]. Reference [4] studies the factors affecting the EE of the massive MIMO uplink systems, but it ignores the power consumption of the circuit. On this basis, [5] establishes a power consumption model in which the circuit power consumption is constant to study the EE of massive MIMO systems and draws the conclusion that the system EE increases infinitely with an increasing number of BS antennas.…”

Section: Introductionmentioning

confidence: 99%

“…On this basis, [5] establishes a power consumption model in which the circuit power consumption is constant to study the EE of massive MIMO systems and draws the conclusion that the system EE increases infinitely with an increasing number of BS antennas. Through the analysis of [4,5], we can see that the existing power consumption models fail to consider the circuit power consumption or treat it as a constant, resulting in the inaccurate expression of EE. In [6], a more realistic power consumption model is considered that only analyzes the perfect channel state information (CSI) scenarios and ignores the existence of imperfect CSI scenarios.…”

Section: Introductionmentioning

confidence: 99%

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Energy Efficiency Optimization of Massive MIMO Systems Based on the Particle Swarm Optimization Algorithm

Yang

Zhang

Zhu

et al. 2021

Wireless Communications and Mobile Computing

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As one of the key technologies in the fifth generation of mobile communications, massive multi-input multioutput (MIMO) can improve system throughput and transmission reliability. However, if all antennas are used to transmit data, the same number of radiofrequency chains is required, which not only increases the cost of system but also reduces the energy efficiency (EE). To solve these problems, in this paper, we propose an EE optimization based on the particle swarm optimization (PSO) algorithm. First, we consider the base station (BS) antennas and terminal users and analyze their impact on EE in the uplink and downlink of a single-cell multiuser massive MIMO system. Second, a dynamic power consumption model is used under zero-forcing processing, and it obtains the expression of EE that is used as the fitness function of the PSO algorithm under perfect and imperfect channel state information (CSI) in single-cell scenarios and imperfect CSI in multicell scenarios. Finally, the optimal EE value is obtained by updating the global optimal positions of the particles. The simulation results show that compared with the traditional iterative algorithm and artificial bee colony algorithm, the proposed algorithm not only possesses the lowest complexity but also obtains the highest optimal value of EE under the single-cell perfect CSI scenario. In the single-cell and multicell scenarios with imperfect CSI, the proposed algorithm is capable of obtaining the same or slightly lower optimal EE value than that of the traditional iterative algorithm, but the running time is at most only 1/12 of that imposed by the iterative algorithm.

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Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Energy Efficiency Optimization of Massive MIMO Systems Based on the Particle Swarm Optimization Algorithm

Yang

Zhang

Zhu

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

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“…The arithmetic-mean metric is proposed alike an energy-efficient scheduler among users during the uplink transmission. Moreover, the EE optimization objective is proposed as a function of users' data rate and a number of antennas at the serving AP in [21]. Similarly, authors of [11] optimize the EE by solving the objective of a sum of users' quality-of-service (QoS) and energy-aware utility functions.…”

Section: Introductionmentioning

confidence: 99%

Users First: A Robust Two-Level Learning of Power Control in Uplink Ultra-Dense HetNets

Makhanbet

2020

IEEE Access

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In this article, power control of uplink connection in the ultra-dense heterogeneous networks (HetNets), which are studied as different types of access points (APs), is investigated. It is demonstrated that an efficient performance of users during the uplink transmission is limited to the issue of per-user power control. Although the per-user power control allows users to transmit with full power to maintain a stable connection, it also causes a higher outage probability during the uplink transmission. In light of this, we propose a robust distributed energy-efficient scheme for uplink power control in HetNets, which confronts the per-user power control problem and coordination of the multi-user interferences. Therefore, firstly, Jarvic-Patric (JP) algorithm is adopted for the users' clustering. Unlike the traditional JP algorithm, conditions for the formation of users' clustering are extended with a term named the degree of membership in this article. Secondly, the distributed energy efficiency (EE) problem is formulated as the mean-metric of the EE, i.e., a sum of users' cooperative EE functions to address the cooperation problem among clustered interfering users at the local level and coordination of multi-user interferences at a global level. The formulation of the EE problem in this fashion reveals the interdependence of power optimization at local and global levels, which brings about the necessity of joint optimization. Hence, we propose a novel 2level distributed cooperative learning (DCL) scheme, where users act as self-organized agents and optimize power control at local and global levels jointly. In the 2-level DCL scheme, clustered users are engaged in the cooperative game of power control at the local level to maximize the cooperative EE. Meantime, users communicate with each other to learn an online power control at the global level. Besides, a popular performance metric for a machine learning scheme named regret is demonstrated for the 2-level DCL scheme. Finally, the numerical results demonstrate that the proposed scheme significantly improves the EE compared to existing works.

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