Resource allocation strategy against selfishness in cognitive radio
            <i>ad‐hoc</i>
            network based on Stackelberg game

Liu, Zhixin; Zhao, Mingye; Chan, Kit Yan; Liu, Yang; Ma, Kai

doi:10.1049/iet-com.2018.5166

Cited by 8 publications

(4 citation statements)

References 34 publications

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“…It is obvious that excessive SUs access to the system will influence the PUs' transmission, and a large number of interrupted SUs entering the buffer will increase the SUs' average latency. Hence, how to properly control the access behavior and retransmission behavior of SUs has become a key issue and research hotspot [19,20]. In [21], Turhan et al posed an access control strategy to adjust the acceptance of SUs in CRNs.…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis and Optimization of CRNs Based on Fixed Feedback Probability Mechanism with Two Classes of Secondary Users

Zhao

Liu

2019

Mathematical Problems in Engineering

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In this paper, we conduct a research based on the classified secondary users (SUs). SUs are divided into two categories: higher-priority SU1 and lower-priority SU2, and two types of users generate two types of packets, respectively. Due to the lowest spectrum usage rights of SU2 packets, the SU2 packets’ transmission is easily interrupted by other packets with higher rights. With the purpose of controlling the SU2 packets’ retransmission behavior, we introduce two system parameters, namely, feedback threshold T and feedback probability q. When the amount of SU2 packets in the buffer reaches the feedback threshold T, the interrupted SU2 packets either enter the buffer with probability q for retransmission or leave the channel by probability 1−q, where q is a fixed parameter. We construct a three-dimensional Markov model based on the presented retransmission control mechanism and derive some important performance indicators of SU2 packets based on the one-step transfer probability matrix and steady-state distribution. Then, we analyze the impact of some key parameters on the performance indicators through numerical experiments. Finally, we establish a cost function and use particle swarm optimization algorithm to optimize the feedback threshold and feedback probability.

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Section: Related Workmentioning

confidence: 99%

Performance Analysis and Optimization of CRNs Based on Fixed Feedback Probability Mechanism with Two Classes of Secondary Users

Zhao

Liu

2019

Mathematical Problems in Engineering

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“…For better results of sensing accuracy, cooperative sensing was studied in [26] to focus on wireless network hidden terminal problems. Recently researchers studied cooperative CRS [27].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning‐Based Power Control and Consensus Performance of Spectrum Sharing in the CR Network

Aslam

Ahmed

et al. 2021

Wireless Communications and Mobile Computing

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The cognitive radio network (CRN) is aimed at strengthening the system through learning and adjusting by observing and measuring the available resources. Due to spectrum sensing capability in CRN, it should be feasible and fast. The capability to observe and reconfigure is the key feature of CRN, while current machine learning techniques work great when incorporated with system adaption algorithms. This paper describes the consensus performance and power control of spectrum sharing in CRN. (1) CRN users are considered noncooperative users such that the power control policy of a primary user (PU) is predefined keeping the secondary user (SU) unaware of PU’s power control policy. For a more efficient spectrum sharing performance, a deep learning power control strategy has been developed. This algorithm is based on the received signal strength at CRN nodes. (2) An agent-based approach is introduced for the CR user’s consensus performance. (3) All agents reached their steady-state value after nearly 100 seconds. However, the settling time is large. Sensing delay of 0.4 second inside whole operation is identical. The assumed method is enough for the representation of large-scale sensing delay in the CR network.

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“…In the cognitive radio networks (CRNs), the radio spectrum is dynamically used under cognitive radio (CR) technology [1, 2]. The primary purpose of this dynamic spectrum utilisation is to solve the problem of spectrum scarcity due to the static nature of the traditional spectrum management [3].…”

Section: Introductionmentioning

confidence: 99%

Efficient‐spectrum management based on localisation of primary user position towards 5G

2020

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Resource allocation strategy against selfishness in cognitive radio ad‐hoc network based on Stackelberg game

Cited by 8 publications

References 34 publications

Performance Analysis and Optimization of CRNs Based on Fixed Feedback Probability Mechanism with Two Classes of Secondary Users

Performance Analysis and Optimization of CRNs Based on Fixed Feedback Probability Mechanism with Two Classes of Secondary Users

A Deep Learning‐Based Power Control and Consensus Performance of Spectrum Sharing in the CR Network

Efficient‐spectrum management based on localisation of primary user position towards 5G

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