Modeling of Hybrid Energy Harvesting Communication Systems

Altınel, Doğay; Kurt, Güneş Karabulut

doi:10.1109/tgcn.2019.2908086

Cited by 23 publications

(6 citation statements)

References 17 publications

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“…Figure 6 presents the standard deviation of consumed energy versus the different number of body sensors deployed. The proposed modified Q-learning scheme gives the best performance with an average standard deviation of 10.33, compared with 15.65 using the classical Q-learning scheme, 19.98 using the PEH-QoS scheme from the literature [35] and 26.57 using the random power allocation scheme. This is because of the fact that the proposed modified Q-learning scheme is able to maintain the fairness of each body sensor by means of selecting the optimal transmission mode and relay body sensors to distribute the energy consumption to the relay sensors with more residual energy.…”

Section: Results and Analysismentioning

confidence: 97%

“…In this paper, we define the energy efficiency (EE) of WBANs as the ratio of the transmission rate to the consumed transmission power. Equation (19) gives the energy efficiency of the n-th body sensor in the k-th time slot.…”

Section: Energy Efficiency Modelmentioning

confidence: 99%

“…Various types of energy sources can be exploited as energy supplies, for instance, thermal, locomotion, and electromagnetic wave [17,18]. Meanwhile, in the existing literature, several energy harvesting models have also been investigated in the literature [19][20][21]. Therefore, energy harvesting (EH)-powered WBANs (EH-WBANs) have the potential ability to achieve the infinite lifetime and perpetual operation, which is called energy neutral operation (ENO) [22].…”

mentioning

confidence: 99%

“…After the first iteration, from current state-action pair, we can obtain an immediate reward and a new state (the data and energy queue lengths of each body sensor). Then, the algorithm updates the state-action pair, as expressed by Equation (19). Once either all Q-values or a certain number of iterations is reached, the algorithm will terminate.…”

mentioning

confidence: 99%

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Reinforcement Learning (RL)-Based Energy Efficient Resource Allocation for Energy Harvesting-Powered Wireless Body Area Network

Xie

Zhang

et al. 2019

Sensors

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Wireless body area networks (WBANs) have attracted great attention from both industry and academia as a promising technology for continuous monitoring of physiological signals of the human body. As the sensors in WBANs are typically battery-driven and inconvenient to recharge, an energy efficient resource allocation scheme is essential to prolong the lifetime of the networks, while guaranteeing the rigid requirements of quality of service (QoS) of the WBANs in nature. As a possible alternative solution to address the energy efficiency problem, energy harvesting (EH) technology with the capability of harvesting energy from ambient sources can potentially reduce the dependence on the battery supply. Consequently, in this paper, we investigate the resource allocation problem for EH-powered WBANs (EH-WBANs). Our goal is to maximize the energy efficiency of the EH-WBANs with the joint consideration of transmission mode, relay selection, allocated time slot, transmission power, and the energy constraint of each sensor. In view of the characteristic of the EH-WBANs, we formulate the energy efficiency problem as a discrete-time and finite-state Markov decision process (DFMDP), in which allocation strategy decisions are made by a hub that does not have complete and global network information. Owing to the complexity of the problem, we propose a modified Q-learning (QL) algorithm to obtain the optimal allocation strategy. The numerical results validate the effectiveness of the proposed scheme as well as the low computation complexity of the proposed modified Q-learning (QL) algorithm.

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Section: Results and Analysismentioning

confidence: 97%

Section: Energy Efficiency Modelmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Reinforcement Learning (RL)-Based Energy Efficient Resource Allocation for Energy Harvesting-Powered Wireless Body Area Network

Xie

Zhang

et al. 2019

Sensors

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“…Writing 7and (9) into the matrix form, the result (2) follows. Now, consider the case that in period τ 2 , let the system state be (x, j) at time t + Δ, where 0 < x < κ u .…”

Section: Balance Equationsmentioning

confidence: 99%

Modeling and performance analysis of energy harvesting wireless communication systems with reliable energy backup

Tang

Tan

Liu

2020

Int J Communication

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Summary Energy harvesting wireless communication system (EH‐WCS) has the capability of harvesting energy for system operations from the surrounding renewable energy sources. However, the randomness and instability of the harvested energy will result in the depletion of the energy consumption. To provide reliable communication services with the quality of service (QoS) guarantee, it is necessary for the EH‐WCS to use a reliable energy backup (REB) for supplying energy to the system during the failure of its primary energy source. In this paper, a novel stochastic model, i.e., the extended Markov fluid flow model, is proposed to describe the EH‐WCS with REB. The Kolmogorov forward equations of the system model are derived. By solving the corresponding equations, we obtain the stationary distributions of the key performance metrics for the EH‐WCS with REB, including the average energy consumption rate of the EH‐WCS, the residual energy distribution, the average energy supply rate by REB, the packet queue length in data buffer, the data queue delay, and the packet blocking probability. A numerical example is provided to investigate the theoretical results, and the effects of the system parameters on the performance are further studied numerically. Both the theoretical insights and the numerical analyses are believed to be important for the design of EH‐WCSs.

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