Joint Subcarrier and Transmission Power Allocation in OFDMA-Based WPT System for Mobile-Edge Computing in IoT Environment

Han, Jaeseob; Lee, Gyeong Ho; Park, Sang‐Don; Choi, Jun Kyun

doi:10.1109/jiot.2021.3103768

Cited by 20 publications

(10 citation statements)

References 49 publications

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“…where E c is the transmission power budget the PT has, P c is the transmission power constraint under each orthogonal band, and c k is the rectifier's operation limit of EHI k . The constraints (10) and (11) delineate that the PT should determine the amount of transmission power to the EHI k by taking into account the transmission power constraint under each orthogonal band and total power budget, respectively.…”

Section: Total Received Power Maximization (Trpm)mentioning

confidence: 99%

“…The pervasive nature of IoT technology is expected to encompass every aspect of our lives. However, a major challenge arises as most IoT sensors operate on battery power, leading to an energy poverty issue [8][9][10][11][12][13][14]. To address this challenge, the concept of radio-frequency (RF) wireless power transfer technology has been extensively explored.…”

Section: Introductionmentioning

confidence: 99%

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Power and Frequency Band Allocation Mechanisms for WPT System with Logarithmic-Based Nonlinear Energy Harvesting Model

et al. 2023

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Wireless power transmission (WPT) is expected to play a crucial role in supporting the perpetual operations of Internet of Things (IoT) devices, thereby contributing significantly to IoT services. However, the development of efficient power allocation algorithms has remained a longstanding challenge. This paper addresses the aforementioned challenge by proposing a novel strategy, called energy poverty-based device selection (EPDS), in conjunction with energy beamforming, where orthogonal frequency bands are allocated to energy harvesting IoT devices (EHIs). To solve two power allocation problems, a logarithmic-based nonlinear energy harvesting model (NEHM) is introduced. The first problem tackled is the total received power maximization (TRPM), which is initially presented and, then, solved optimally in closed-form by incorporating Karush–Kuhn–Tucker (KKT) conditions with the modified water-filling algorithm. The second problem formulated is the common received power maximization (CRPM), which takes into account energy fairness considerations. To assess the proposed algorithms and gain insights into the effects of mobility, the mobility of EHIs is modeled as a one-dimensional random walk. Extensive numerical results are provided to validate the advantages of the proposed algorithms. Both the TRPM and CRPM algorithms exhibit exceptional performance in terms of total and minimum received energy, respectively. Furthermore, in comparison to round-robin scheduling, the EPDS demonstrates superior performance in terms of minimum received energy. This paper highlights the impact of the proposed energy harvesting (EH) model, demonstrating 12.68% and 3.69% higher values than the linear model for the minimum and total received energy, respectively.

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Section: Total Received Power Maximization (Trpm)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Power and Frequency Band Allocation Mechanisms for WPT System with Logarithmic-Based Nonlinear Energy Harvesting Model

et al. 2023

Self Cite

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“…[31] considered non-linear EH model in intelligent reflecting surface (IRS) assisted SWIPT terahertz secure systems, and proposed a robust beamforming design to minimise the total transmit power. Also, current studies have introduced nonlinear EH model into MEC system [32][33][34][35]. The authors in ref.…”

Section: Introductionmentioning

confidence: 99%

“…The authors in ref. [33] formulated a max–min energy fairness problem and a power sum maximisation problem with considering non‐linear EH model in OFDMA‐based wireless powered MEC system. The authors in ref.…”

Section: Introductionmentioning

confidence: 99%

Joint offloading and beamforming design in full‐duplex multi‐hop SWIPT MEC systems under non‐linear energy harvesting model

Liu

et al. 2022

IET Communications

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In Internet of Things scenarios, the problem of restricted energy and limited computation capability of user node usually exists. To address the problem, a multiple-input-multipleoutput full-duplex multi-hop relay simultaneous wireless information and power transfer (SWIPT) mobile-edge computing (MEC) system is proposed in this paper, where energy harvesting model is non-linear. With the aid of MEC and SWIPT, user node equipped with power splitting receiver can locally execute computation tasks or offload partial or all of it to access point associated with an MEC server by using its battery energy, and harvest energy to replenish its battery while downloading the computation results. In addition, the system introduces full-duplex multi-hop relay and non-linear energy model to improve network coverage and model practical energy harvesting circuit. Aiming at minimising the system energy consumption, an energy efficient optimisation problem is formulated while satisfying the latency and energy constraints. Since the original problem is non-linear and non-convex, it is converted into two subproblems and successive convex approximation-based algorithm and geometric programming-based algorithm based alternating optimisation technique are adopted to solve them. Numerical results verify the superiority of the proposed optimisation scheme compared with other benchmark schemes.

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“…Te capabilities of edge computing rely on edge servers, which are usually deployed with base stations. It is expected that more IoTs applications will be carried out based on MEC in the future, and the massive data they generate will have great demands on network resources such as bandwidth, computing power, and storage [3,4]. Terefore, in future, multiple MEC servers will be needed to jointly provide services for diferent applications.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning with Variable Sampling Rate for Traffic Prediction in 6G MEC IoT

Peng

Ding

2022

Discrete Dynamics in Nature and Society

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The high-speed development of mobile broadband networks and IoT applications has brought about massive data transmission and data processing, and severe traffic congestion has adversely affected the fast-growing networks and industries. To better allocate network resources and ensure the smooth operation of communications, predicting network traffic becomes an important tool. We investigate in detail the impact of variable sampling rate on traffic prediction and propose a high-speed traffic prediction method using machine learning and recurrent neural networks. We first investigate a VSR-NLMS adaptive prediction method to perform time series prediction dataset transformation. Then, we propose a VSR-LSTM algorithm for real-time prediction of network traffic. Finally, compared with the traditional traffic prediction algorithm based on fixed sampling rate (FSR-LSTM), we simulate the prediction accuracy of the VSR-LSTM algorithm based on the variable sampling rate proposed. The experiment shows that VSR-LSTM has higher traffic prediction accuracy because its sampling rate varies with the traffic.

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Joint Subcarrier and Transmission Power Allocation in OFDMA-Based WPT System for Mobile-Edge Computing in IoT Environment

Cited by 20 publications

References 49 publications

Power and Frequency Band Allocation Mechanisms for WPT System with Logarithmic-Based Nonlinear Energy Harvesting Model

Power and Frequency Band Allocation Mechanisms for WPT System with Logarithmic-Based Nonlinear Energy Harvesting Model

Joint offloading and beamforming design in full‐duplex multi‐hop SWIPT MEC systems under non‐linear energy harvesting model

Machine Learning with Variable Sampling Rate for Traffic Prediction in 6G MEC IoT

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