Energy Management for Secure Transmission in Wireless Powered Communication Networks

Zhang, Shubin; Kong, Shuaiying; Chi, Kaikai; Huang, Liang

doi:10.1109/jiot.2021.3079455

Cited by 29 publications

(6 citation statements)

References 45 publications

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“…A increasing number of IoT devices are taking important responsibilities in industrial production and providing daily convenience, including intelligent plant, automatic drive, smart city and so on [1]. However, the demand for high intelligence brings new challenges to traditional IoT devices with limited battery capacity and poor computing capacity [2], [3]. Specifically, in the 5G and upcoming 6G eras, an increasing number of applications require the real-time data and corresponding processing results to guarantee high reliability [4].…”

Section: Introductionmentioning

confidence: 99%

DRL-Based Computation Rate Maximization for Wireless Powered Multi-AP Edge Computing

Zhang,

Bao,

Chi

et al. 2024

IEEE Trans. Commun.

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In the ongoing 5G and upcoming 6G eras, the intelligent Internet of Things (IoT) network will take increasingly important responsibility for industrial production, daily life and so on. The IoT devices with limited battery size and computing ability cannot meet many applications brought out by the data-driven artificial intelligence technique. The combination of wireless power transfer (WPT) and edge computing is regarded as an effective solution to this dilemma. IoT devices can collect radio frequency energy provided by hybrid access points (HAPs) to process data locally or offload data to the edge servers of HAPs. However, how to efficiently make offloading decisions and allocate resource is challenging, especially for the networks with multiple HAPs. In this paper, we consider the sum computation rate maximization problem for a WPT empowered IoT network with multiple HAPs and IoT devices. The problem is formulated as a mixed-integer nonlinear programming problem. To solve this problem efficiently, we decompose it into a top-problem of optimizing offloading decisions and a sub-problem of optimizing time allocation under the given offloading decisions. We propose a deep reinforcement learning (DRL) based algorithm to output the near-optimal offloading decision and design an efficient algorithm based on Lagrangian duality method to obtain the consequent optimal time allocation. Simulations verified that the proposed DRL-based algorithm can achieve more than 95 percent of the maximal computation rate with low complexity. Compared with the common actor-critic algorithm, the proposed algorithm has the substantial advantage in convergence speed, achieved computation rate and running time.

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Section: Introductionmentioning

confidence: 99%

DRL-Based Computation Rate Maximization for Wireless Powered Multi-AP Edge Computing

Zhang,

Bao,

Chi

et al. 2024

IEEE Trans. Commun.

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“…Along with the solution to spectrum scarcity, energy harvesting (EH) technology is an effective approach to alleviate energy scarcity, which allows IoT devices with EH functionality to harvest energy from ambient energy resources, e.g. solar, wind, ambient radio-frequency (RF) energy, and so on [6,7].…”

Section: Introductionmentioning

confidence: 99%

Throughput maximisation for multi‐channel energy harvesting cognitive radio networks with hybrid overlay/underlay transmission

et al. 2022

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This paper focuses on the issue of joint time and power allocation in multi‐channel energy harvesting CR networks (EH‐CRNs), where the multi‐antenna secondary transmitter (ST) opportunistically accesses the licensed subchannels by a hybrid overlay/underlay transmission approach. To improve spectrum efficiency and energy efficiency of the EH‐CRNs, the ST scavenges energy from the radio‐frequency signal radiated by the primary transmitter, and exploits the harvested energy for data transmission through subchannels of different states in overlay/underlay mode simultaneously. Moreover, under the interference power constraint, energy constraint, and maximum power constraint, the secondary throughput is improved by optimising the allocation of subchannels, the time scheduling between energy harvesting and data transmission, and the power allocation of the ST among different subchannels. A subchannel allocation scheme with low time complexity is proposed, and the secondary throughput optimisation problem is formulated with respect to the time scheduling and power allocation of the ST. Then it is proved the problem is convex, and the problem is solved by a proposed joint time and power allocation algorithm. Numerical results show that the proposed scheme has an advantage of secondary throughput over the other schemes. Finally, the impacts of key relevant factors on the secondary throughput are explored.

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“…The traditional energy harvesting is to harvest the energy from the natural environment, such as wind and solar energy, but these energy sources are not stable over time and undergo significant long-term variations [16]. Due to the fast development of wireless power transmission (WPT) technology, it has been a good solution to supply radio frequency (RF) energy to IoT devices as WPT can provide stable energy supply through transmitting RF energy [17][18][19][20][21]. The application of WPT in MEC, called wireless powered MEC (WP-MEC), combines the advantages of WPT and MEC and is considered to be an ideal solution that can conquer the energy constraint and computing resource constraint of IIoT devices [22][23][24][25][26][27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

DRL based offloading of industrial IoT applications in wireless powered mobile edge computing

et al. 2022

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Mobile edge computing is the network technology for providing computing resources of edge computing server to Internet of Things (IoT) applications. Additionally, wireless power transfer (WPT) technology can provide stable energy supplying to IIoT nodes and to overcome the limited node lifetime problem faced when using batteries. In this paper, the wireless powered mobile edge computing network is considered where the edge computing server transfers RF energy to IIoT nodes which use harvested energy to offload partial computation workload based on OFDMA and also conduct local computation. The aim is to maximise the weighted sum computation rate by jointly optimising the WPT duration and the amount of energy used for offloading at each node for each time frame. This paper proposes an offloading approach based on deep reinforcement learning which is able to quickly obtain the near‐optimal offloading solutions. Specifically, the original offloading problem is decomposed into the sub‐problem of optimising the energy allocated for offloading under a given WPT duration and the top‐problem of optimising the WPT duration. Simulation results demonstrate that the proposed algorithm can achieve the near‐optimal weighted sum computation rate with very low complexity, which is tailored for the practical dynamic‐channel environment.

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Energy Management for Secure Transmission in Wireless Powered Communication Networks

Cited by 29 publications

References 45 publications

DRL-Based Computation Rate Maximization for Wireless Powered Multi-AP Edge Computing

DRL-Based Computation Rate Maximization for Wireless Powered Multi-AP Edge Computing

Throughput maximisation for multi‐channel energy harvesting cognitive radio networks with hybrid overlay/underlay transmission

DRL based offloading of industrial IoT applications in wireless powered mobile edge computing

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