Access Strategy for Hybrid Underlay-Overlay Cognitive Radios With Energy Harvesting

Usman, Muhammad; Koo, Insoo

doi:10.1109/jsen.2014.2324565

Cited by 93 publications

(63 citation statements)

References 13 publications

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“…Interestingly, the trend reverses for the minimum secondary outage probability, once the peak power constraint becomes active with the relaxation of the primary outage constraint. APPENDIX A PROOF OF (4) Let K be Pr…”

Section: Discussionmentioning

confidence: 99%

Interference-Assisted Wireless Energy Harvesting in Cognitive Relay Network with Multiple Primary Transceivers

Kalamkar

Banerjee

2015

2015 IEEE Global Communications Conference (GLOBECOM)

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Abstract-We consider a spectrum sharing scenario, where a secondary network coexists with a primary network of multiple transceivers. The secondary network consists of an energyconstrained decode-and-forward secondary relay which assists the communication between a secondary transmitter and a destination in the presence of the interference from multiple primary transmitters. The secondary relay harvests energy from the received radiofrequency signals, which include the information signal from the secondary transmitter and the primary interference. The harvested energy is then used to decode the secondary information and forward it to the secondary destination. At the relay, we adopt a time switching policy due to its simplicity that switches between the energy harvesting and information decoding over time. Specifically, we derive a closed-form expression for the secondary outage probability under the primary outage constraint and the peak power constraint at both secondary transmitter and relay. In addition, we investigate the effect of the number of primary transceivers on the optimal energy harvesting duration that minimizes the secondary outage probability. By utilizing the primary interference as a useful energy source in the energy harvesting phase, the secondary network achieves a better outage performance.

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Section: Discussionmentioning

confidence: 99%

Interference-Assisted Wireless Energy Harvesting in Cognitive Relay Network with Multiple Primary Transceivers

Kalamkar

Banerjee

2015

2015 IEEE Global Communications Conference (GLOBECOM)

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“…The access strategy with the ability of energy harvesting is presented in [35]. SU is equipped with a rechargeable battery and uses both overlay and underlay techniques to increase the maximum power.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Technique for Spectrum Sharing in Cognitive Radio Networks for the Internet of Things

Zarif¹,

Moghadam²,

Imani³

2018

IJCA

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Cognitive Radio (CR) has been introduced and developed for wireless networks. CR is playing an important role in wireless spectrum and with the help of CR, senders can choose the best spectrum for communication. Spectrum Sharing is one of the components of CR architecture which is responsible for distributing the spectrum among users according to their needs. In addition, it is one of the key challenges to improve the wireless network performance. How to access the spectrum is an important issue in spectrum sharing. Primary Users (PUs) and Secondary Users (SUs) access the spectrum bands based on the overlay and underlay spectrum sharing techniques but SUs are limited in both overlay and underlay. After analyzing the existing mechanisms in this paper, we provide a new mechanism to improve SUs accessing the spectrum. Our mechanism works based on SUs' location and the distance between sender and receiver. The proposed mechanism in this paper shows that SUs can own the spectrum permanently without any interferences with PUs. Also, there is no need for SUs to change or leave the spectrum when PUs return. The proposed method is very useful and efficient due to increasing the performance of CR in different wireless networks. Our proposed method can be considered as a step towards the development of IoT and support the future devices in terms of spectrum access. Our proposed mechanism requires no additional hardware, therefore, its implementation is costless and simple.

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“…Considering temporal variation of PU activity, the PU tends to maintain its state once switched from the other state, i.e., the PU stays on a channel for at least a few slots after occupying it, or the PU will not occupy a channel for at least a few slots when switched to an idle state. From this viewpoint, positive correlation of the PU traffic [32][33][34] is considered, i.e., p II > p IB and p BB >p BI where is the transition probability of the PU from state a to state b, and subscript I, B indicates idle state, busy state, respectively. A moving window for recording the history of PU activity is adopted.…”

Section: B Maximizing Sleep Timementioning

confidence: 99%

Energy-Efficient Infrastructure Sensor Network for Ad Hoc Cognitive Radio Network

2016

Self Cite

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Abstract-We propose an energy-efficient network architecture that consists of ad hoc (mobile) cognitive radios (CRs) and infrastructure wireless sensor nodes. The sensor nodes within communications range of each CR are grouped into a cluster and the clusters of CRs are regularly updated according to the random mobility of the CRs. We reduce the energy consumption and the end-to-end delay of the sensor network by dividing each cluster into disjoint subsets with overlapped sensing coverage of primary user (PU) activity. Respective subset of a CR provides target detection and false alarm probabilities. Substantial energy efficiency is achieved by activating only one subset of the cluster, while putting the rest of the subsets in the cluster into sleep mode. Additional gain in energy efficiency is obtained by two promising propositions : selecting nodes from the active subset for actual sensing and switching the active subset to sleep mode by scheduling. The sensor nodes for actual spectrum sensing are chosen considering their respective time durations for sensing. Even the only active subset is switched to sleep mode for a certain number of time slots, utilizing the history of PU activity. We compare the proposed CR network with existing approaches to demonstrate the network performance in terms of the energy consumption and the end-to-end delay.Index Terms-ad hoc cognitive radio network, cluster and subsets, infrastructure sensor network, subset scheduling, spectrum sensing, sensor network-based spectrum sensing I. INTRODUCTIONCCORDING to the Federal Communications Commission (FCC), utilization of the statically assigned spectrum varies from 15% to 85%, depending upon spatio-temporal variations [1,2]. In order for a secondary user, which cannot be active when the primary user (PU) is active, to utilize the spectrum licensed to a PU, the activity of the PU should be closely monitored [3]. One possible approach is to use cognitive radio (CR) transceivers for spectrum sensing and sending their observations to a fusion center to determine the presence of the PU signal [4,5]. However, this approach incurs high cost and high energy consumption.A more appealing approach is to perform sensing via cost-effective and dedicated sensor network [6,7]. Use of the sensor network for spectrum sensing is being explored by regulatory bodies like the FCC, which has invited experts to draft proposals for the use of a sensor network with low cost/energy/delay for enhanced spectrum sensing [8]. Energy-efficient spectrum sensing by a sensor network offers advantages such as more effective detection of a weak PU signal (by location diversity of the sensor nodes) and better protection of the PU due to high reliability in detection. Furthermore, this approach is more appropriate for mobile CRs where cooperative spectrum sensing is more difficult in the absence of a fusion center and cooperation between the CR users cannot be easily achieved. However, there are still certain challenges/disadvantages in such a network, which are yet to be re...

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Access Strategy for Hybrid Underlay-Overlay Cognitive Radios With Energy Harvesting

Abstract: In 2005, he joined the University of Ulsan, Ulsan, Korea, where he is currently a Full Professor. His current research interests include next generation wireless communication systems and wireless sensor networks.

Cited by 93 publications

References 13 publications

Interference-Assisted Wireless Energy Harvesting in Cognitive Relay Network with Multiple Primary Transceivers

Interference-Assisted Wireless Energy Harvesting in Cognitive Relay Network with Multiple Primary Transceivers

Hybrid Technique for Spectrum Sharing in Cognitive Radio Networks for the Internet of Things

Energy-Efficient Infrastructure Sensor Network for Ad Hoc Cognitive Radio Network

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