Hybrid Energy Ratio Allocation Algorithm in a Multi-Base-Station Collaboration System

Han, Dongsheng; Li, Shijie; Chen, Zhixiong

doi:10.1109/access.2019.2946285

Cited by 12 publications

(8 citation statements)

References 23 publications

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“…The authors of [15] investigate the problem of minimizing energy consumption in H-AP networks with caching and propose a low-complexity hierarchical solution algorithm that effectively reduces system power consumption by optimizing terminal bandwidth and energy cooperation mechanisms. The authors of [16] propose a hybrid energy ratio allocation algorithm, which effectively improves the impact of renewable energy on the communication system and reduces the energy cost of the system.…”

Section: Introductionmentioning

confidence: 99%

Energy Efficiency Optimization for SWIPT-Enabled IoT Network with Energy Cooperation

Cao

Zhong

Peng

et al. 2022

Sensors

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As an advanced technology, simultaneous wireless information and power transfer (SWIPT), combined with the internet of things (IoT) devices, can effectively extend the online cycle of the terminal. To cope with the fluctuation of energy harvesting by the hybrid access points (H-AP), the energy cooperation base station is introduced to realize the sharing of renewable energy. In this paper, we study the SWIPT-enabled IoT networks with cooperation. Our goal is to maximize the energy efficiency of the system, and at the same time, we need to meet the energy harvesting constraints, user quality of service (QoS) constraints and transmission power constraints. We jointly solve the power allocation, time switching and energy cooperation problems. Because this problem is a nonlinear programming problem, it is difficult to solve directly, so we use the alternating variable method, the iterative algorithm is used to solve the power allocation and time switching problem, and the matching algorithm is used to solve the energy cooperation problem. Simulation results show that the proposed algorithm has obvious advantages in energy efficiency performance compared with the comparison algorithm. At the same time, it is also proved that the introduction of energy cooperation technology can effectively reduce system energy consumption and improve system energy efficiency.

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Section: Introductionmentioning

confidence: 99%

Energy Efficiency Optimization for SWIPT-Enabled IoT Network with Energy Cooperation

Cao

Zhong

Peng

et al. 2022

Sensors

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“…The MFC system, which integrates UAV, exerts the advantages of flexible network distribution in fog computing and enhances the network reliability and task offloading capability. Analyzing the offloading mode, reducing the total delay and reducing the integrated energy cost [16] can increase the network resource utilization.…”

Section: Introductionmentioning

confidence: 99%

A Multilevel Mobile Fog Computing Offloading Model Based on UAV-Assisted and Heterogeneous Network

Chen

Xiao

Han

2020

Wireless Communications and Mobile Computing

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The mobile fog computing (MFC) network that integrates unmanned aerial vehicles (UAV) fully exerts its advantages of flexible deployment, load balance, and rapid response. Under complex network environment, proposing a reasonable offloading model and according resource optimization of the MFC network is important to satisfy high-requirement offloading standard. In this paper, a multilevel MFC offloading model where UAV and fog nod undertake relay nodes and offloading computing nodes are established for computation-intensive and latency-critical tasks, considering heterogeneous network selection , dynamic channel quality and central cloud access . With the total system utility optimality function including reward function maximization as the goal, the MDP algorithm is applied to solve the best offloading decision of the computing task and the balanced load mode of the MFC network. Finally, the simulation section verifies the excellent performance of the proposed multilevel MFC offloading model in network resource utilization. Simulation results show that the model can optimize the relative position of service nodes in MFC network and ensure the offloading reliability of terminal equipment.

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“…However, communication flows of cellular network will be doubled generally every year [ 5 ]. Data statistics show that the power consumption of base stations (BSs) accounts for approximately 50%–80% of the total power consumption for the information and communication technological industry [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Mixed Energy Supply of IoT Network Based on Matching Game and Convex Optimization

Han

Liu

2020

Sensors

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The interaction capability provided by the Internet of Things (IoT) significantly increases communication between human and machine, changing our lives gradually. However, the abundant constructions of 5G small base stations (SBSs) and large-scaled access of IoT terminal equipment (TE) will surely cause a dramatic increase in energy expense costs of a wireless communication system. In this study, we designed a bilateral random model of TE allocation and energy decisions in IoT, and proposed a mixed energy supply algorithm based on a matching game and convex optimization to minimize the energy expense cost of the wireless communication system in IoT. This study divided the problem of minimizing energy expense cost of the system into two steps. First, the random allocation problem of TEs in IoT was modeled to a matching game problem. This step is to obtain the TE matching scheme that minimizes the energy consumption of the whole system on the basis of guaranteeing the quality of service of TEs. Second, the energy decision problem of SBS was modeled into a convex optimization problem. The energy purchase scheme of SBSs with the minimum energy expense cost of the system was obtained by solving the optimal solution of the convex optimization. According to the simulation results, the proposed mixed energy supply scheme can decrease the energy expense cost of the system effectively.

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Hybrid Energy Ratio Allocation Algorithm in a Multi-Base-Station Collaboration System

Cited by 12 publications

References 23 publications

Energy Efficiency Optimization for SWIPT-Enabled IoT Network with Energy Cooperation

Energy Efficiency Optimization for SWIPT-Enabled IoT Network with Energy Cooperation

A Multilevel Mobile Fog Computing Offloading Model Based on UAV-Assisted and Heterogeneous Network

Optimization of Mixed Energy Supply of IoT Network Based on Matching Game and Convex Optimization

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