Distributed opportunistic spectrum sharing in cognitive radio networks

Hawa, Mohammed; AlAmmouri, Ahmad; Alhiary, Ala; Alhamad, Nidal

doi:10.1002/dac.3147

Cited by 25 publications

(20 citation statements)

References 51 publications

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“…Cognitive Radio (CR) is considered as a creative answer for relieve spectrum shortage issue by empowering Dynamic Spectrum Access (DSA), intended to placate the current clashes between the regularly expanding spectrum request development and at present wasteful spectrum use [1][2]. The fundamental thought of DSA is to give appropriate arrangements that permit sharing the radio spectrum among a few radio correspondence frameworks for upgrading the general spectrum use [3]. With the appearance of CR as a key empowering agent of DSA, a few papers broadcasted the requirement for Cognitive Radio Networks (CRNs), which permit a remote correspondence framework dependent on the alleged intellectual cycle that empowers watching nature, acting and figuring out how to enhance its execution [4].…”

Section: Introductionmentioning

confidence: 99%

Spectrum Signals Handoff in LTE Cognitive Radio Networks Using Reinforcement Learning

Babu¹,

Vemuru²

2019

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In this paper we build up a cognitive radio system (CRN) test bed to exhibit the utilization of support learning and exchange learning plans for spectrum handoff choices. By thinking about the channel status (inactive or active) and channel condition (as far as packet failure rate), the sender node plays out the learning-based spectrum handoff. The ideal power assignment of spectrum sharing clients is performed by Galactic Swarm Optimization (GSO) Algorithm. In reinforcement learning, the quantity of system perceptions required to accomplish the ideal choices is frequently and restrictively high, because of the complex CRN condition. At the point when a node encounters new channel conditions, the process is restarted with preparation notwithstanding when the comparable channel condition has been experienced previously. To relieve this issue, an exchange learning based spectrum handoff method is actualized, which empowers a node to gain from its neighboring node(s) to enhance its execution. The exploratory outcome will show that the machine learning based spectrum handoff performs better in the long term and adequately uses the accessible spectrum.

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Section: Introductionmentioning

confidence: 99%

Spectrum Signals Handoff in LTE Cognitive Radio Networks Using Reinforcement Learning

Babu¹,

Vemuru²

2019

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“…To improve QoS provision in dynamic spectrum access, the mobility and the interference probability must be reduced by allocating the whitespaces that are the most appropriate to QoS requested by the SUs . A large body of research has been dedicated for studying the QoS for the secondary radio spectrum . The Federal Communications Commission (FCC) has already allowed the secondary access in a cognitive manner in underutilized portions of the TV bands in its Institute of Electrical and Electronics Engineers (IEEE) 802.22 standard and 802.11af standard .…”

Section: Introductionmentioning

confidence: 99%

“…12 A large body of research has been dedicated for studying the QoS for the secondary radio spectrum. [13][14][15][16][17][18][19][20][21][22][23][24] The Federal Communications Commission (FCC) has already allowed the secondary access in a cognitive manner in underutilized portions of the TV bands in its Institute of Electrical and Electronics Engineers (IEEE) 802.22 standard 25 and 802.11af standard. 26 Some of the other standards developed for the CRs are the IEEE 802.…”

Section: Introductionmentioning

confidence: 99%

“…A centralized architecture [32][33][34] for the cognitive operation is assumed for the SSM that is providing service to the users in the geographic area of its location. The distributed architecture for cognitive operation 13,[35][36][37][38][39][40][41][42][43][44][45][46] has not been used due to the lack of a coordination methodology among the different SSM entities and may result in some contention problems. 13 In addition, the radio environment is assumed to have been sensed already and the spectrum sensing has been left out of the scope of this paper.…”

Section: Introductionmentioning

confidence: 99%

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QoS optimization‐based dynamic secondary spectrum access model

Khan

Jamali

2018

Trans Emerging Tel Tech

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Cognitive radio (CR) is believed to be the future communication technology that provides the solution to the radio spectrum scarcity problem. Quality of service (QoS) and secondary radio spectrum pricing are two important aspects of the ongoing research on CR. The primary users (PUs) have guaranteed QoS while the secondary users (SUs) can make only opportunistic access to the radio spectrum. The SUs coexist with the PUs on a noninterfering basis and must vacate the bands under their use, if required by the PUs. Good QoS can be exercised by an allocation of whitespaces with QoS appropriate to the SUs that minimize the mobility probability. If the pricing issues are not addressed properly, they may result in economic viability challenges and/or pose existential threats to the CR technology. This research proposes a QoS optimization‐based dynamic secondary spectrum access model using genetic algorithm along with a pricing and a payment methodology for the sales to secondary spectrum markets. The trade‐off between the spectrum allocation and the economic parameters for the secondary radio spectrum trade is considered. An extensive simulation is created to analyze the QoS optimization and pricing for the secondary radio spectrum trade. The results indicate that the profitability and sales in the secondary spectrum market are based on the fitness of the offered QoS with respect to that requested by the SUs. An increase in the QoS provided to the SUs is realized at the cost of a slight increase in the unit cost of the secondary spectrum.

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“…Thus, the CR network can allow a simultanoeus SU and PU transmissions provided that SU does not interfere too much with the PU. This SU's transmission strategy is called spectrum sharing [5]- [8].…”

Section: Introduction and Related Workmentioning

confidence: 99%

Ergodic capacity for fading channels in cognitive radio networks

Masmoudi

2018

2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

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Abstract-In this paper, we consider a Cognitive Radio (CR) system where two type of users try to access to the primary spectrum : a primary user (PU) owning the spectrum license and a secondary user (SU) who does not own the spectrum license. However, the secondary communication is allowed to coexist with the primary communication as long as the interference caused by SU to PU is below a tolerable threshold. We study the optimization problem which maximizes the SU's achievable ergodic capacity under different types of power constraints and for different fading channel models. Our goal is to calculate the optimal power allocation strategies for these optimization problems. We show that modelling with Rayleigh fading for the channel between SU transmitter and PU receiver is an advantageous way to ameliorate the SU ergodic capacity. Furthermore, we consider four combinations of power constraints, since the interference power constraint and the transmit power constraint can be restricted by a peak or an average threshold. We also show that the SU ergodic capacity under average transmit power constraint and average interference power constraint outperforms the one with peak power constraints. In this case, we propose a novel decoupling method. Our method reduces the complexity of the initial problem and makes our initial problem easier to solve.Index Terms-Cognitive radio, fading channels, power allocation, ergodic capacity, interference and transmit power constraints.

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Distributed opportunistic spectrum sharing in cognitive radio networks

Cited by 25 publications

References 51 publications

Spectrum Signals Handoff in LTE Cognitive Radio Networks Using Reinforcement Learning

Spectrum Signals Handoff in LTE Cognitive Radio Networks Using Reinforcement Learning

QoS optimization‐based dynamic secondary spectrum access model

Ergodic capacity for fading channels in cognitive radio networks

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