Probability of Packet Loss in Energy Harvesting Nodes With Cognitive Radio Capabilities

Wu, Shanai; Shin, Yoan; Kim, Jin Young; Kim, Dong In

doi:10.1109/lcomm.2016.2539158

Cited by 15 publications

(6 citation statements)

References 12 publications

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“…After solving, the steady-state probability of the busy channel is obtained as Pr(H 1 ) = (1 − p 0 )/(2 − p 1 − p 0 ), and the steady-state probability of the idle channel is achieved 12 by Pr(H 0 ) = 1 − Pr (H 1 ) . Figure 5 illustrates Markov model for channel state in cognitive radio system.…”

Section: The Proposed Three-dimensional Continuous-time Markov Modelmentioning

confidence: 99%

“…10,11 In another study, Markov model was proposed to obtain the energy states of the SU equipped with a capacity-fixed battery in an energy harvesting cognitive radio network. 12 The probability of packet loss was derived to maximize the total throughput. In addition, a twodimensional sensing method was developed to optimize the PU performance based on hidden input in the Markov model.…”

mentioning

confidence: 99%

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Throughput optimization using simultaneous sensing and transmission in energy harvesting cognitive radio networks

Moradi

Farrokhi

Ghazizade

2018

Int J Communication

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A three-dimensional continuous-time Markov model is proposed for an energy harvesting cognitive radio system, where each secondary user (SU) harvests energy from the ambient environment and attempts to transmit data packets on spectrum holes in an infinite queuing buffer. Unlike most previous works, the SU can perform spectrum sensing, data transmission, and energy harvesting simultaneously. We determine active probability of the SU transmitter, where the average energy consumption for both spectrum sensing and data transmission should not exceed the amount of harvested energy. Then, we formulate achievable throughput of secondary network as a convex optimization problem under average transmit and interference energy constraints. The optimal pair of controlled energy harvesting rate and data packet rate is derived for proposed model. Results indicate that no trade-off is available among harvesting, sensing/receiving, and transmitting. The SU capability for self-interference cancelation affects the maximum throughput. We develop this work under hybrid channels including overlay and underlay cases and propose a hybrid solution to achieve the maximum throughput. Simulation results verify that our proposed strategy outperforms the efficiency of the secondary network compared to the previous works. KEYWORDS throughput, optimization, energy harvesting, spectrum sensing, Markov model, self-interference cancelation 1 | INTRODUCTIONDuring recent years, energy harvesting cognitive radio has been emerged as a novel technology to exploit unused spectrum band and energy from the surrounding environment. Opportunistic spectrum access is a practical architecture for discovering spectrum holes and supports the primary user (PU) from harmful interference in a cognitive radio. In many cognitive radio scenarios, the SUs have a battery-power source for sensing spectrum and transmitting data. Energy harvesting offers a useful solution, which allows the SUs to harvest energy from the environmental energy sources such as surrounding radio power, mechanical vibrations, solar, and other natural phenomena.During the last decade, energy crisis has attracted a lot of attention. Renewable energy sources can reduce the prices and the demand for conventional energy supplies. In addition, efficient use of energy is regarded as an important issue for wireless communication services. In order to solve such problems, a lot of works such as energy-efficient protocol design and energy-saving hardware have been implemented. 1,2 Energy harvesting has been considered as promising technology which can store unlimited energy unlike battery-power systems. Furthermore, spectrum scarcity is another prominent problem in wireless communication networks. Dynamic spectrum access technique can improve the network efficiency and simplify the spectrum reuse. It allows the SUs to access the white spaces when the licensed spectrum band is not occupied by the PUs and share the spectrum until the PUs are appropriately protected. 3,4 A large body of research has been c...

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Section: The Proposed Three-dimensional Continuous-time Markov Modelmentioning

confidence: 99%

mentioning

confidence: 99%

Throughput optimization using simultaneous sensing and transmission in energy harvesting cognitive radio networks

Moradi

Farrokhi

Ghazizade

2018

Int J Communication

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“…where g p is the SU measured value of the PU SNR, λ is the received signal strength at cognitive SU from PU, and s w 2 is the noise power [18]. The access probability is the transition probability from concurrent state l to the next state l þ 1.…”

Section: Theoretical Analysismentioning

confidence: 99%

“…The optimization problem is solved numerically using MALAB Genetic Algorithm. The results are compared with the results of Monte Carlo simulations in [18] that are based on mathematical iterations. Genetic algorithms are evolutionary search techniques used to identify approximate solutions for optimization problems.…”

Section: Derivation Of the Throughputmentioning

confidence: 99%

“…Literature where CRNs are merged with WSNs that aided IoT applications has been analyzed in [14][15][16][17]. Additionally, there is no significant work that has considered the issue of analyzing system performance of CR-WSN based on RF EH battery models, except partially in [18,19], but they did not deduce the system operating parameters neither their optimized values for the best system performance. In [20], two algorithms are proposed to improve the energy consumption by improving the packet size of the CR-WSN while considering delay time.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Operational Parameters for 5G Energy Harvesting Cognitive Wireless Sensor Networks

Mahmoud

ElAttar

Saafan

et al. 2017

IETE Technical Review

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Dense communication networks have been investigated recently to enable communications of low power wireless devices such as Internet-of-things (IoT) applications in the fifth cellular generation (5G). The wireless sensor network (WSN) is one of the principal technologies of IoT as it plays a critical role in numerous industries such as agriculture, healthcare, and environmental applications. Despite the advantages of the WSN, it is yet difficult to be deployed due to the scarcity of the radio spectrum with the increasing popularity of wireless applications. Therefore, merging of two technologies WSN and cognitive radio network (CRN), as cognitive radio wireless sensor network (CR-WSN), became essential for IoT applications. Another major challenge in such systems is the power constrain and delay sensitivity in such numerous wireless devices. Radio-frequency energy harvesting (EH) capability is supposed to merge with such systems in order to efficiently power and enhance the overall system energy. Thus, this paper discusses a CR-WSN model based on EH in a non-ordinary M/M/1 Markovian battery model with the proposal of a frame structure of the wireless node's charging and sensing time. The contribution of the power obtained from harvesting is derived with the proposal of a realistic power required/harvested model in RF EH CRN. Moreover, the power efficiency of the CR-WSN model is calculated and the derivation of the transmission delay is introduced in the same model. Furthermore, combined optimization of the required power, probability of packet loss and the transmission delay is proposed to validate the overall system performance with the recommended system operational parameters. KEYWORDS Cognitive radio network (CRN); Cognitive radio sensor network (CRWSN); Internet of things (IoT); Markovian model; Power efficiency (PE); Radio frequency energy harvesting (RF EH); Wireless sensor network (WSN)

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Multi-threshold control by a single-server queuing model with a service rate depending on the amount of harvested energy

Kim

Dudin

et al. 2018

Performance Evaluation

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Probability of Packet Loss in Energy Harvesting Nodes With Cognitive Radio Capabilities

Cited by 15 publications

References 12 publications

Throughput optimization using simultaneous sensing and transmission in energy harvesting cognitive radio networks

Throughput optimization using simultaneous sensing and transmission in energy harvesting cognitive radio networks

Optimal Operational Parameters for 5G Energy Harvesting Cognitive Wireless Sensor Networks

Multi-threshold control by a single-server queuing model with a service rate depending on the amount of harvested energy

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