Modelling and performance analysis of RF energy harvesting cognitive radio networks

Ronghe, Sushil B.; Kulkarni, Varada Potnis

doi:10.1109/cesys.2016.7889819

Cited by 2 publications

(1 citation statement)

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“…In [22], the authors investigate the end-to-end throughput optimization problem of radio frequency energy harvesting cognitive radio wireless sensor networks (RF-EH CR-WSN) based systems. A modified EH scheme is suggested to increase the normalized average throughput of RF-EH CR-WSN in [23]. Authors in [24,25] discuss various techniques to truncate energy consumption in WSN based on a CRN to utilize the frequency spectrum more efficiently.…”

Section: Introductionmentioning

confidence: 99%

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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