Joint optimization of cognitive RF energy harvesting and channel access using Markovian Multi-Armed Bandit problem

Sangare, Fahira; Han, Zhu

doi:10.1109/iccw.2017.7962705

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…Our objective is to learn the channel and thus allow the CR system to optimize its operation. In the early work reported in [18], [19], we examined various channel sensing strategies for selecting a pair of channels, one for energy harvesting and one for data transmission. Greedy-based opportunistic sensing was proposed in [18] with the objective of maximizing the transmission rate per energy harvesting time.…”

Section: Introductionmentioning

confidence: 99%

“…Greedy-based opportunistic sensing was proposed in [18] with the objective of maximizing the transmission rate per energy harvesting time. Gittins index solution to the multi-armed bandit problem was studied in [19] to maximize the long term reward, defined as the product of the transmission rate and the harvested energy. In this paper, we propose to jointly select energy harvesting and channel access in a CR environment under a common well-defined objective: maximizing the transmission rate that is empowered by the harvested energy.…”

Section: Introductionmentioning

confidence: 99%

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Learning Frameworks for Dynamic Joint RF Energy Harvesting and Channel Access

Sangare¹,

Nguyen

Han

2019

IEEE Access

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The fifth generation mobile networks (5G) envision to interconnect the massive number of devices with a wide range of characteristics and demands for 2020 and beyond. With more radio-frequency (RF) bands to support multiple frequency transmission, separate antennas, and RF chips may be required for widely separated frequency bands. The development of cognitive radio systems, in which a wireless transceiver automatically adapts its communication parameters to network and user demands, is therefore crucial to the successful roll-out of the 5G. This paper introduces an innovative CR system that performs the spectrum sensing for the joint energy harvesting and channel access. A prototype test bed is employed to detect and convert RF signals from different bands to a DC voltage for powering a sensor board that communicates over another channel to an access point. The system's objective is to use the harvested energy for concurrent data transmission. We model the selection of channels to maximize this objective as a multi-armed bandit (MAB) problem. Depending on the type of the MAB problem, three spectrum sensing strategies are developed for joint energy harvesting and channel access. The first one, applicable for stochastic MAB, conceptualizes some formulations of the exploration/exploitation balancing technique of the available frequency bands. The second strategy is an opportunistic sensing framework for the Markovian MAB when the state of the underlying Markov process is partially observed. The third strategy is based on the Gittins index allocation framework for the fully observed Markovian MAB. The simulation results show that lower regrets can be obtained with more information on the underlying Markov process of the MAB. INDEX TERMS Markov decision process, multi-armed bandit, RF energy harvesting, Gittins index, POMDP.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Learning Frameworks for Dynamic Joint RF Energy Harvesting and Channel Access

Sangare¹,

Nguyen

Han

2019

IEEE Access

Self Cite

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“…The growth of the number of connected devices such as sensors and smart phones in fifth cellular generation (5G) and Internet of things (IoT) is expected to address a lot of demands by 2020 [1]. Wireless sensor networks (WSNs) have been adopted to enable the large number of connected devices in the IoT and 5G in many industries.…”

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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Comparative Performance Evaluation of Intrusion Detection Methods for In-Vehicle Networks

Wang

Qin

et al. 2018

IEEE Access

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Joint optimization of cognitive RF energy harvesting and channel access using Markovian Multi-Armed Bandit problem

Cited by 5 publications

References 15 publications

Learning Frameworks for Dynamic Joint RF Energy Harvesting and Channel Access

Learning Frameworks for Dynamic Joint RF Energy Harvesting and Channel Access

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

Comparative Performance Evaluation of Intrusion Detection Methods for In-Vehicle Networks

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