Methods of Hybrid Cognitive Radio Network: A Survey

Kaur, Rajbir; Buttar, Avtar Singh; Anand, Jayant

doi:10.1109/iceca.2018.8474906

Cited by 6 publications

(4 citation statements)

References 16 publications

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“…4 Problem of power control and joint beamforming in MU relay networks for CR is tackled using game theory in Iranpanah et al 7 To further improve the spectrum utilization, both overlay and underlay approaches get combined under signal to interference and noise ratio (SINR) constraints to make a hybrid CR. A survey for different methods of hybrid CR in which these two modes are merged using different techniques to efficiently utilize the radio spectrum is given in Kaur et al 8 In MU multiple-access wirelesses systems, SUs can achieve their objectives (e.g. optimal throughput and QoS) either by cooperating with other SUs or competing with them.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Capacity maximization in multi-user multiple input multiple output cognitive radio system through optimal beamforming design using game theory

Bindu

Reddy

2019

International Journal of Electrical Engineering & Education

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Section: Introductionmentioning

confidence: 99%

RETRACTED: Capacity maximization in multi-user multiple input multiple output cognitive radio system through optimal beamforming design using game theory

Bindu

Reddy

2019

International Journal of Electrical Engineering & Education

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“…Radio. Cognitive Radio (CR) is defined as an intelligent software-defined radio that senses the environments for detecting available channels and accordingly changes its transmitter or receiver parameters such as operating frequency, bandwidth, modulation, and transmitting power [96]. CR realizes a highly reliable communication and increases the utilization of spectrum resources.…”

Section: Underwater Acoustic Sensor Network Based On Cognitivementioning

confidence: 99%

Advances in Software-Defined Technologies for Underwater Acoustic Sensor Networks: A Survey

et al. 2019

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Underwater Acoustic Sensor Networks (UASNs) are an important technical means to explore the ocean realm. However, most UASNs rely on hardware infrastructures with poor flexibility and versatility. The systems typically deploy in a redundant manner, which not only leads to waste but also causes serious signal interference due to multiple noises in designated underwater regions. Software-Defined Networking (SDN) is a novel network paradigm, which provides an innovative approach to improve flexibility and reduce development risks greatly. Although SDN and UASNs are hot topics, there are currently few studies built on both. In this paper, we provide a comprehensive review on the advances in software-defined UASNs. First, we briefly present the background, and then we review the progress of the Software-Defined Radio (SDR), Cognitive Radio (CR), and SDN. Next, we introduce the current issues and potential research areas. Finally, we conclude the paper and present discussions. Based on this work, we hope to inspire more active studies and take a further step on software-defined UASNs with high performances.

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“…e scarcity of spectral resources has become more apparent in recent years, as wireless networking technology has advanced and the need for data transmission has grown. Researchers have found that the depletion of spectral resources is just an illusion, while the root cause is low spectral utilization [1][2][3]. As one of the critical technologies that can make rational use of spectral resources and effectively improve spectral utilization, cognitive radio (CR) has become a research hotspot [4].…”

Section: Introductionmentioning

confidence: 99%

A Multichannel Allocation Strategy Based on Preemption Threshold and Preemption Probability in Cognitive Radio Networks

Zhao

Xiang

2021

Mobile Information Systems

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In traditional multichannel cognitive radio networks (CRNs), users are split into two different priorities. Because of the low priority of secondary users (SUs), SU packets’ transmissions are easily interrupted by primary users (PUs). In this paper, two control parameters, called preemption threshold H and preemption probability q, are used to regulate the preemption behavior of PU packets to improve the transmission performance of SU packets. When all channels in the system are occupied, the preemption behavior of PU packets will be adjusted according to the amount of SU packets that are transmitting in the system. If the amount is larger than H, the recently arrived PU packet either preempts a channel with probability q or leaves the system with probability 1 − q . The central controller manages the system’s channel usage right and determines a series of access behaviors of user packets. Considering the possible imperfect sensing, a discrete-time queueing model is developed with the proposed preemption control mechanism. Then we obtain some performance index expressions of PU and SU packets founded on the system’s state transition matrix and make the corresponding performance figures through numerical experiment. Finally, we construct a system utility function and determine the optimal preemption threshold and preemption probability through the seagull optimization algorithm (SOA). Experimental data show that the proposed mechanism by setting preemption threshold and preemption probability can significantly reduce SU packets’ outage rate and improve SU packets’ throughput rate.

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Methods of Hybrid Cognitive Radio Network: A Survey

Cited by 6 publications

References 16 publications

RETRACTED: Capacity maximization in multi-user multiple input multiple output cognitive radio system through optimal beamforming design using game theory

RETRACTED: Capacity maximization in multi-user multiple input multiple output cognitive radio system through optimal beamforming design using game theory

Advances in Software-Defined Technologies for Underwater Acoustic Sensor Networks: A Survey

A Multichannel Allocation Strategy Based on Preemption Threshold and Preemption Probability in Cognitive Radio Networks

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