Capacity maximization of energy-harvesting small cells with dynamic sleep mode operation in heterogeneous networks

Chang, Chen‐Yi; Ho, Kun-Lin; Liao, Wanjiun; Shiu, Da-shan

doi:10.1109/icc.2014.6883730

Cited by 18 publications

(7 citation statements)

References 12 publications

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“…In what follows, we summarize how to solve this dual problem by the subgradient method; see [25,Chapters 5,6] and [26] for details.…”

Section: B Distributed Algorithm For Cell Zoomingmentioning

confidence: 99%

“…An et al [5] study the impact of SBS density on the energy efficiency in cellular networks by the stochastic geometry theory and propose an optimal user association strategy based on quantum particle swarm optimization. The capacity of energy harvesting small cells is maximized via joint sleep-wake scheduling and transmission power control in [6]. Maghsudi and Hossain [7] investigate a distributed user association problem of downlink small cell networks, where SBSs select users by maximizing their successful transmission probability.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Cell Zooming with Masked Data for Off-Grid Small Cell Networks: Distributed Optimization Approach

Wakaiki¹,

Suto²,

Masubuchi³

2021

Preprint

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Cell zooming has been becoming an essential enabler for off-grid small cell networks. Traditional models often utilize the numbers of active users in order to determine cell zooming strategies. However, such confidential measurement data must be concealed from others. We therefore propose a novel cell zooming method with masking noise. The proposed algorithm is designed based on distributed optimization, in which each SBS locally solves a divided optimization problem and learns how much a global constraint is satisfied or violated for temporal solutions. The important feature of this distributed control method is robustness against masking noise. We analyze the trade-off between confidentiality and optimization accuracy, using the notion of differential privacy. Numerical simulations show that the proposed distributed control method outperforms a standard centralized control method in the presence of masking noise.

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“…In what follows, we summarize how to solve this dual problem by the subgradient method; see [25,Chapters 5,6] and [26] for details.…”

Section: B Distributed Algorithm For Cell Zoomingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cell Zooming with Masked Data for Off-Grid Small Cell Networks: Distributed Optimization Approach

Wakaiki¹,

Suto²,

Masubuchi³

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…These worked as benchmarks for latter development of online heuristics [3]. The objectives considered in these works are outage probability [4]- [6], packet latency [2], [4], and cell capacity [7].…”

Section: Introductionmentioning

confidence: 99%

Fair Scheduling for Energy Harvesting Nodes

Goonewardena

Yadav

Ajib

et al. 2015

IEEE Wireless Commun. Lett.

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This letter considers the problem of scheduling in the multiple input multiple output (MIMO), multiple-access wireless channel, where the transmitters are energy harvesting nodes (EHNs) that are powered by renewable energy sources (RESs). In this letter the conventional scheduling objective of maximizing rate is augmented by two other objectives, regulating fairness, and stabilization of the stored energy processes of the EHNs. This problem is formulated as a network of energy queues, which represent the batteries. Considering the stochastic nature of the wireless channel and the energy harvesting processes, this letter employs Lyapunov drift plus penalty technique to develop a cross-layer scheduler that operates in a slotted-time and distributed manner. At each epoch it selects an EHN for transmission and computes the transmit power. As an added advantage the power control algorithm still retains the optimal water-filling solution. Through simulations the proposed solution is compared against a conventional max-rate scheduler and is shown to better enforce fairness, stabilize the battery levels, and minimize the required battery capacity.

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“…The transmission rate of the small base station is maximized under the condition that the SNR of the macro user is ensured. In [13], a heuristic algorithm of joint power control and sleep-wake mechanism is proposed. Considering energy causality and battery capacity, the capacity of the network is maximized by using the renewable energy collected by the small cells.…”

Section: Introductionmentioning

confidence: 99%

“…Considering energy causality and battery capacity, the capacity of the network is maximized by using the renewable energy collected by the small cells. However, the algorithms in [12] and [13] both consider the optimization of the personal utility of the small cells, while ignoring the impact of the mutual interference between the small cells on the total performance of the system. In this paper, we combine the cooperative game problem with energy harvesting technology in the small cell network for the first time, and study the problem of interference management of small cells based on coalitional game with the aim of maximizing overall system utility.…”

Section: Introductionmentioning

confidence: 99%

Interference Management Algorithm Based on Coalitional Game for Energy-Harvesting Small Cells

Chen¹,

Zhu²,

Zhao³

2017

KSII TIIS

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For the downlink energy-harvesting small cell network, this paper proposes an interference management algorithm based on distributed coalitional game. The cooperative interference management problem of the energy-harvesting small cells is modeled as a coalitional game with transfer utility. Based on the energy harvesting strategy of the small cells, the time sharing mode of the small cells in the same coalition is determined, and an optimization model is constructed to maximize the total system rate of the energy-harvesting small cells. Using the distributed algorithm for coalition formation proposed in this paper, the stable coalition structure, optimal time sharing strategy and optimal power distribution are found to maximize the total utility of the small cell system. The performance of the proposed algorithm is discussed and analyzed finally, and it is proved that this algorithm can converge to a stable coalition structure with reasonable complexity. The simulations show that the total system rate of the proposed algorithm is superior to that of the non-cooperative algorithm in the case of dense deployment of small cells, and the proposed algorithm can converge quickly.

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Capacity maximization of energy-harvesting small cells with dynamic sleep mode operation in heterogeneous networks

Cited by 18 publications

References 12 publications

Cell Zooming with Masked Data for Off-Grid Small Cell Networks: Distributed Optimization Approach

Cell Zooming with Masked Data for Off-Grid Small Cell Networks: Distributed Optimization Approach

Fair Scheduling for Energy Harvesting Nodes

Interference Management Algorithm Based on Coalitional Game for Energy-Harvesting Small Cells

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