Performance of secondary user with combined RF and non-RF based energy-harvesting in cognitive radio network

Bhowmick, Abhijit; Roy, Sanjay Dhar; Kundu, Sumit

doi:10.1109/ants.2015.7413665

Cited by 17 publications

(25 citation statements)

References 9 publications

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“…Thus, for a cooperative network, the overall probabilities of detection ( Q d ), missed detection ( Q m ), and false alarm ( Q f ) can be expressed as

Q_{d} false(t_{s}, p false) = 1 - {false(1 - P_{d} false(t_{s}, p false) false)}^{N}

Q_{m} false(t_{s}, p false) = 1 - Q_{d} false(t_{s}, p false); Q_{f} false(t_{s}, p false) = 1 - {false(1 - P_{f} false(t_{s}, p false) false)}^{N} .

As shown in Figure , there is a possibility that the PU may again reoccupy the given channel even when the CR is transmitting. Thus, we consider 2‐state Markov chain to describe the activity of PU . The duration of PU's busy and idle states is exponentially distributed with mean values α 0 and α 1 under H 0 (PU absent) and H 1 (PU present) hypotheses, respectively, as

P_{B} false(t false) = α_{1}^{- 1} \exp^{false(- t false/ α_{1} false)}; P_{I} false(t false) = α_{0}^{- 1} \exp^{false(- t false/ α_{0} false)}

, where P ( H 0 ) = α 0 /( α 0 + α 1 ) and P ( H 1 ) = α 1 /( α 0 + α 1 ).…”

Section: Network Model and Theoretical Discussionmentioning

confidence: 99%

“…Thus, we consider 2‐state Markov chain to describe the activity of PU . The duration of PU's busy and idle states is exponentially distributed with mean values α 0 and α 1 under H 0 (PU absent) and H 1 (PU present) hypotheses, respectively, as

P_{B} false(t false) = α_{1}^{- 1} \exp^{false(- t false/ α_{1} false)}; P_{I} false(t false) = α_{0}^{- 1} \exp^{false(- t false/ α_{0} false)}

, where P ( H 0 ) = α 0 /( α 0 + α 1 ) and P ( H 1 ) = α 1 /( α 0 + α 1 ). Now, we consider some important probabilities in connection with the random activity of PU, such as probability to be busy, i.e.,

P_{b u s y} = \int_{0}^{t_{s}} P_{B} false(t false) d t = 1 - e^{- t_{s} false/ α_{1}}

and probability of being idle, i.e.,

P_{i d l e} = \int_{0}^{t_{s}} P_{I} false(t false) d t = 1 - e^{- t_{s} false/ α_{0}}

over sensing time; probability of disappear, i.e.,

P_{d i s} = \int_{0}^{t_{r}} P_{B} false(t false) d t = 1 - e^{- t_{r} false/ α_{0}}

from the spectrum band and probability of reappear, i.e.,

P_{r e} = \int_{0}^{t_{r}} P_{I} false(t false) d t = 1 - e^{- t_{r} false/ α_{1}}

to the given spectrum band during transmission time, t r = T C R − t s .…”

Section: Network Model and Theoretical Discussionmentioning

confidence: 99%

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Joint impact of sensing time and SPRF parameter on the performance of a continuous energy harvesting cooperative cognitive radio network

Das

Bhowmick

Roy

et al. 2016

Int J Communication

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Summary Joint impact of sensing time and signal power raise factor is studied for an improved energy detector–based energy harvesting cooperative cognitive radio network. All the cognitive radio nodes harvest energy either from radio frequency resources or from non–radio frequency resources. The probability density function of harvested energy from both the sources is exponentially distributed. Novel theoretical expressions for harvested energy and throughput are derived. Impact of several sensing parameters and a device constraint on the outage is studied. Optimal values of sensing time and signal power raise factor parameter pair are estimated for maximum harvested energy and maximum throughput. Energy efficiency of the network is also evaluated, and impact of sensing time on it is indicated.

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“…Thus, for a cooperative network, the overall probabilities of detection ( Q d ), missed detection ( Q m ), and false alarm ( Q f ) can be expressed as

Q_{d} false(t_{s}, p false) = 1 - {false(1 - P_{d} false(t_{s}, p false) false)}^{N}

Q_{m} false(t_{s}, p false) = 1 - Q_{d} false(t_{s}, p false); Q_{f} false(t_{s}, p false) = 1 - {false(1 - P_{f} false(t_{s}, p false) false)}^{N} .

P_{B} false(t false) = α_{1}^{- 1} \exp^{false(- t false/ α_{1} false)}; P_{I} false(t false) = α_{0}^{- 1} \exp^{false(- t false/ α_{0} false)}

, where P ( H 0 ) = α 0 /( α 0 + α 1 ) and P ( H 1 ) = α 1 /( α 0 + α 1 ).…”

Section: Network Model and Theoretical Discussionmentioning

confidence: 99%

P_{B} false(t false) = α_{1}^{- 1} \exp^{false(- t false/ α_{1} false)}; P_{I} false(t false) = α_{0}^{- 1} \exp^{false(- t false/ α_{0} false)}

P_{b u s y} = \int_{0}^{t_{s}} P_{B} false(t false) d t = 1 - e^{- t_{s} false/ α_{1}}

and probability of being idle, i.e.,

P_{i d l e} = \int_{0}^{t_{s}} P_{I} false(t false) d t = 1 - e^{- t_{s} false/ α_{0}}

over sensing time; probability of disappear, i.e.,

P_{d i s} = \int_{0}^{t_{r}} P_{B} false(t false) d t = 1 - e^{- t_{r} false/ α_{0}}

from the spectrum band and probability of reappear, i.e.,

P_{r e} = \int_{0}^{t_{r}} P_{I} false(t false) d t = 1 - e^{- t_{r} false/ α_{1}}

to the given spectrum band during transmission time, t r = T C R − t s .…”

Section: Network Model and Theoretical Discussionmentioning

confidence: 99%

Joint impact of sensing time and SPRF parameter on the performance of a continuous energy harvesting cooperative cognitive radio network

Das

Bhowmick

Roy

et al. 2016

Int J Communication

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“…where P p is the PU transmission power and f gh is the distribution of channel gain. During the transmission time, harvesting by CR is carried out based on the sensing decision [19], with the following possibilities:…”

Section: Harvesting Of Energymentioning

confidence: 99%

Cognitive radio network with continuous energy‐harvesting

Bhowmick¹,

Roy

Kundu

2016

Int J Communication

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SUMMARYIn this paper, the performance of a cooperative cognitive radio (CR) network is investigated under continuous energy harvesting scenario. A CR node harvests energy from both the sources: non-radio frequency (RF) signal (ambient sources) or from RF signal (primary user signal). It harvests from non-RF signal during sensing time of its detection cycle, and from both the sources, RF signal and non-RF signal, during transmission time as per sensing decision. Several novel analytical expressions are developed to indicate the harvested energy, energy reward, energy cost in a detection frame, and throughput. The performance of the CR network is investigated to maximize the throughput considering energy causality constraints and collision constraints. Analytical results are validated through extensive simulation results.

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“…According to (9), the channel j's selection probability (P i j ) from the player i at time t c can be captured as follows:…”

Section: Cognitive Radio Sensing Mechanismmentioning

confidence: 99%

“…data on an idle channel, but also harvest RF energy from PUs' transmission on a busy channel [7][8][9].…”

mentioning

confidence: 99%

Adaptive cognitive radio energy-harvesting scheme using sequential game approach

Kim

2017

J Wireless Com Network

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Radio frequency (RF) energy transfer and harvesting techniques have recently become alternative methods to overcome the barriers that prevent the real-world wireless device deployment. For the next-generation wireless networks, they can be key techniques. In this study, we develop a novel energy-harvesting scheme for the cognitive radio (CR) network system. Using the sequential game model, data transmission and energy harvesting in each device are dynamically scheduled. Our approach can capture the wireless channel state while considering multiple device interactions. In a distributed manner, individual devices adaptively adjust their decisions based on the current system information while maximizing their payoffs. When the channel selection collision occurs, our sequential bargaining process coordinates this problem to optimize a social fairness. Finally, we have conducted extensive simulations. The results demonstrate that the proposed scheme achieves an excellent performance for the energy efficiency and spectrum utilization. The main contribution of our work lies in the fact that we shed some new light on the trade-off between individual wireless devices and CR network system.

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Performance of secondary user with combined RF and non-RF based energy-harvesting in cognitive radio network

Cited by 17 publications

References 9 publications

Joint impact of sensing time and SPRF parameter on the performance of a continuous energy harvesting cooperative cognitive radio network

Joint impact of sensing time and SPRF parameter on the performance of a continuous energy harvesting cooperative cognitive radio network

Cognitive radio network with continuous energy‐harvesting

Adaptive cognitive radio energy-harvesting scheme using sequential game approach

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