Wireless Power Transfer in Massive MIMO-Aided HetNets With User Association

Zhu, Yongxu; Wang, Lifeng; Wong, Kai-Kit; Jin, Shi; Zheng, Zhongbin

doi:10.1109/tcomm.2016.2594794

Cited by 60 publications

(72 citation statements)

References 45 publications

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“…and ϑ m w 2 α m λ m /λ Σ = m λ m /µ. By using (15), an accurate tight lower on the CDF of P eh bound can be found as 6 In practice, the energy conversion efficiency η may not be a constant since it may change over time due to the non-linearity property of the EH circuits at a user while the user is harvesting energy. However, in this paper we consider a constant η because our focus is to analyze the harvested power in a time-averaged sense.…”

Section: A the Statistical Properties Of The Harvested Powermentioning

confidence: 99%

Fundamentals of Simultaneous Wireless Information and Power Transmission in Heterogeneous Networks: A Cell-Load Perspective

Liu

Hsu

2019

IEEE J. Select. Areas Commun.

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In a heterogeneous cellular network (HetNet) consisting of multiple different types (tiers) of base stations (BSs), the void cell event in which a BS does not have any users has been shown to exist due to user-centric BS association and its probability is dominated by the cell load of each tier. Such a void cell phenomenon has not been well characterized in the modeling and analytical framework of simultaneous wireless information and power transmission (SWIPT) in a HetNet. This paper aims to accurately exploit the fundamental performance limits of the SWIPT between a BS and its user by modeling the cell-load impact on the downlink and uplink transmissions of each BS. We first characterize the power-splitting receiver architecture at a user and analyze the statistical properties and limits of its harvested power and energy, which reveals how much of the average energy can be harvested by users and how likely the selfpowered sustainability of users can be achieved. We then derive the downlink and uplink rates that characterize the cell-load and user association effects and use them to define the energy efficiency of a user. The optimality of the energy efficiency is investigated, which maximizes the SWIPT performance of the receiver architecture for different user association and network deployment scenarios.Index Terms-Wireless information and power transmission, energy harvesting, heterogeneous network, energy efficiency, stochastic geometry.

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Section: A the Statistical Properties Of The Harvested Powermentioning

confidence: 99%

Fundamentals of Simultaneous Wireless Information and Power Transmission in Heterogeneous Networks: A Cell-Load Perspective

Liu

Hsu

2019

IEEE J. Select. Areas Commun.

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“…We now consider the probability density function (PDF) of the association distance between the typical MU and its serving BS. With (3), (4), (5), and (6), Lemma 2 is achieved.…”

Section: Lemma 1 With the Average Brp-based DL Mu Association Rule mentioning

confidence: 96%

“…In the work of Deng et al, the authors proposed safeguarding massive MIMO–enabled HetNets. To enhance the information rate and overcome the path loss of energy‐limited MU terminals, the massive MIMO–enabled HetNets have been investigated by exploiting the potential of wireless power transfer in the work of Zhu et al Wang et al presented the massive MIMO–enabled HetNet cloud radio access network . The rationale behind the proposed scheme is that baseband processing is centralized and coordinated among sites in a centralized baseband processing unit.…”

Section: Introductionmentioning

confidence: 99%

Effective self‐backhaul scheme for massive MIMO–enabled full‐duplex heterogeneous networks by exploiting NOMA

Jia

Yang

et al. 2019

Trans Emerging Tel Tech

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By integrating full‐duplex (FD) and nonorthogonal multiple‐access (NOMA) techniques for wireless self‐backhaul at small‐cell base stations (SBSs), a novel multitier heterogeneous network (HetNet) is proposed. In the HetNet, NOMA is used at SBSs for combing backhaul and downlink (DL) signals. As a result, at each SBS, the backhaul, uplink transmission, and DL transmission are executed simultaneously, whereas the FD macro base stations (MBSs) communicate with mobile users (MUs) and transceive backhaul signals from associated SBSs by using massive‐MIMO antennas. The simple time‐division duplex is performed for the communications between MBSs and MUs so that a round of communications consists of MBS‐MU‐DL and MBS‐MU‐UL phases. This work particularly focuses on the MBS‐MU‐DL phase. By assuming the HetNet to be fully loaded, this paper first models the distribution of active SBSs and those of the interference received at the different types of receivers. Then, by formulating the received signal‐to‐interference ratios, this paper obtains the coverage probabilities of the MU‐SBS‐MBS uplink backhaul and MU DLs as well as the spectrum and energy efficiencies. The presented simulations and numerical results show that the integration of FD and NOMA at SBSs is an effective solution for creating the backhaul between FD‐SBS and FD‐MBS. It is obtained that when the power‐sharing coefficient allocated for a small‐cell DL is small, the coverage probability of SBS DL transmission increases; otherwise, it decreases. However, the coverage probabilities of the MBS DL and MU‐SBS‐MBS backhaul link monotonically vary with the power‐sharing coefficient. It is also achieved that the power‐sharing coefficient imposes the effect on the energy efficiencies of the MU‐SBS‐MBS backhaul link and MU DL in inverse directions. At the same time, it is found that the achieved energy efficiency gain depends greatly on the resolution of massive‐MIMO antennas.

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“…not contained in the training set, input vectors a. As a result, every time the positions of the users in the network change, the new association rule can be computed by simply feeding the new a to the ANN, without having to actually solve (10). As further elaborated next, this grants a huge complexity reduction and enables to update the user-cell association in real-time as the mobile users move around the coverage area.…”

Section: A Ann Trainingmentioning

confidence: 99%

“…: After training and validation, the performance of the resulting ANN has been evaluated over a test set of 15000 data samples that has been independently generated from the training and validation sets. For each test sample, denoting by ρ AN N = {ρ k,m } k,m the ANN output, user k has been associated to BSm ifm = arg max m ρ k,m , and then the resulting sum-rate performance has been compared to the optimal solution of Problem (10). Fig.…”

Section: Numerical Analysismentioning

confidence: 99%

User Association and Load Balancing for Massive MIMO through Deep Learning

Zappone

Sanguinetti

Debbah

2018

2018 52nd Asilomar Conference on Signals, Systems, and Computers

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This work investigates the use of deep learning to perform user-cell association for sum-rate maximization in Massive MIMO networks. It is shown how a deep neural network can be trained to approach the optimal association rule with a much more limited computational complexity, thus enabling to update the association rule in real-time, on the basis of the mobility patterns of users. In particular, the proposed neural network design requires as input only the users' geographical positions. Numerical results show that it guarantees the same performance of traditional optimization-oriented methods.

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Wireless Power Transfer in Massive MIMO-Aided HetNets With User Association

Cited by 60 publications

References 45 publications

Fundamentals of Simultaneous Wireless Information and Power Transmission in Heterogeneous Networks: A Cell-Load Perspective

Fundamentals of Simultaneous Wireless Information and Power Transmission in Heterogeneous Networks: A Cell-Load Perspective

Effective self‐backhaul scheme for massive MIMO–enabled full‐duplex heterogeneous networks by exploiting NOMA

User Association and Load Balancing for Massive MIMO through Deep Learning

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