Energy Efficiency Optimization for Multi-Cell Massive MIMO: Centralized and Distributed Power Allocation Algorithms

You, Li; Huang, Yufei; Zhang, Di; Chang, Zheng; Wang, Wenjin; Gao, Xiqi

doi:10.1109/tcomm.2021.3081451

Cited by 21 publications

(10 citation statements)

References 49 publications

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“…However, the power allocation in FD-style CF systems is non-convex and computationally challenging due to the large size of optimization variables. To date, many optimization algorithms for resource allocation have been proposed, such as Dinkelbach's transform [16], quadratic transform (QT) [17,18], dual decomposition [19] and successive convex approximation (SCA) [20,21]. These approaches can generally converge to local optimum within several iterations, but their computational complexity expands exponentially as the size of network.…”

Section: A Related Workmentioning

confidence: 99%

Heterogeneous graph neural network for power allocation in multicarrier-division duplex cell-free massive MIMO systems

Li¹,

Yang²,

Maunder³

et al. 2022

Preprint

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In-band full duplex-based cell-free (IBFD-CF) systems suffer from severe interference problem including self-interference (SI) and cross-link interference (CLI), especially when cell-free (CF) systems are operated in a distributed way. To this end, we propose multicarrier-division duplex (MDD) as an enabler for full-duplex (FD)-style operation in distributed CF massive MIMO systems, where DL and UL transmissions take place simultaneously at the same frequency band but mutually orthogonal subcarrier sets. In order to maximize the spectral efficiency (SE) in the proposed systems, we present heterogeneous graph neural network specific for CF systems (CF-HGNN), which consists of an adaptive node embedding layer, meta-path based message passing, meta-path based attention and downstream power allocation learning. In particular, the adaptive node embedding layer can handle the varying number of access points (APs), mobile stations (MSs) and subcarriers, and the involved attention mechanism enables each AP/MS node in CF-HGNN to aggregate the information from interfering path and communication path with different priorities. Numerical results show that CF-HGNN is capable of using 10 4 times less operation time to achieve the 99% performance of the SE of quadratic transform and successive convex approximation (QT-SCA). Additionally, CF-HGNN also significantly outperforms unfair greedy method in terms of SE performance. Furthermore, CF-HGNN exhibits good adaptivity to varying number of nodes and subcarriers, and also generalization ability to different sizes of CF network.

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Section: A Related Workmentioning

confidence: 99%

Heterogeneous graph neural network for power allocation in multicarrier-division duplex cell-free massive MIMO systems

Li¹,

Yang²,

Maunder³

et al. 2022

Preprint

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“…Under the assumption that all metamaterials have the same frequency selectivity, H[τ ] is expressed as I multiplied by a constant, thus H[τ ] is eliminated in (7).…”

Section: A Problem Formulationmentioning

confidence: 99%

“…With the system SE in (7) and power consumption in (8), the EE of our DMAs-assisted uplink system is given by…”

Section: A Problem Formulationmentioning

confidence: 99%

“…This has been proven to significantly increase the throughput of wireless systems [3]. However, It brings a great demand on the radio frequency (RF) chains to realize massive MIMO transmissions by conventional antennas with the fully digital architecture [4], which exposes some problems that cannot be ignored in practice, such as increased fabrication cost [5], high power consumption [6], [7], limited physical size and shape, and deployment restriction [8]. To this end, some works have focused on the design of antennas to effectively implement massive MIMO systems.…”

Section: Introductionmentioning

confidence: 99%

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Energy Efficiency Maximization of Massive MIMO Communications With Dynamic Metasurface Antennas

You,

Xu,

Alexandropoulos

et al. 2021

Preprint

Self Cite

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Future wireless communications are largely inclined to deploy a massive number of antennas at the base stations (BS) by exploiting energy-efficient and environmentally friendly technologies. An emerging technology called dynamic metasurface antennas (DMAs) is promising to realize such massive antenna arrays with reduced physical size, hardware cost, and power consumption. This paper aims to optimize the energy efficiency (EE) performance of DMAs-assisted massive MIMO uplink communications.We propose an algorithmic framework for designing the transmit precoding of each multi-antenna user and the DMAs tuning strategy at the BS to maximize the EE performance, considering the availability of the instantaneous and statistical channel state information (CSI), respectively. Specifically, the proposed framework includes Dinkelbach's transform, alternating optimization, and deterministic equivalent methods. In addition, we obtain a closed-form solution to the optimal transmit signal directions for the statistical CSI case, which simplifies the corresponding transmission design. The numerical resultsshow good convergence performance of our proposed algorithms as well as considerable EE performance gains of the DMAs-assisted massive MIMO uplink communications over the baseline schemes.

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“…Therefore, the optimization problem of EE is fractional programming (FP) problem which is non-convex and impossible to solve directly. In [19], the authors investigated the EE optimization problem for the downlink multi-cell mMIMO networks. In order to address the non-convex FP problem, Dinkelbach method was adopted to transform the original FP objective into several sub-problems which are convex.…”

Section: Introductionmentioning

confidence: 99%

Energy Efficient Resource Allocation for 5G Heterogeneous Networks Using Genetic Algorithm

Qi¹,

Khattak

Zaib

et al. 2021

IEEE Access

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The energy efficient resource allocation scheme based on genetic algorithm (GA) for the downlink orthogonal frequency division multiple access (OFDMA) heterogeneous networks (HetNets) is developed in this paper. To maximize the spectrum efficiency for the fifth generation (5G) mobile networks, frequency reuse-1 is employed. Thus, advanced inter-cell interference coordination techniques are required to mitigate the inter-cell interference for 5G HetNets. In this paper, the energy efficient optimization problem based on coordinated scheduling is formulated, which is a mixed-integer nonlinear fractional programming problem and is intractable to solve directly. To tackle this, a two-step GA based scheme is proposed to solve the optimization problem. In the first step, the resource blocks matrix is solved by normal GA in the spectral efficiency aspect with fixed power distribution matrix, and then the power distribution matrix is obtained in the second step by non-dominated sorting genetic algorithm II (NSGA-II) with obtained resource blocks allocation matrix. Finally, the system level numerical evaluation process is provided to illustrate the effectiveness of the developed scheme.INDEX TERMS Energy efficiency, resource allocation, heterogeneous networks, genetic algorithm.

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Energy Efficiency Optimization for Multi-Cell Massive MIMO: Centralized and Distributed Power Allocation Algorithms

Cited by 21 publications

References 49 publications

Heterogeneous graph neural network for power allocation in multicarrier-division duplex cell-free massive MIMO systems

Heterogeneous graph neural network for power allocation in multicarrier-division duplex cell-free massive MIMO systems

Energy Efficiency Maximization of Massive MIMO Communications With Dynamic Metasurface Antennas

Energy Efficient Resource Allocation for 5G Heterogeneous Networks Using Genetic Algorithm

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