An interference model for heterogeneous cognitive radio networks

Chen, Zengmao; Tang, Yunmin; Sun, Zhiguo; Zhang, Xiaolin; Ren, Liqun; Ning, Xiaoyan

doi:10.1109/iccchina.2017.8330325

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…In [6], a common method that constrain the interference power during the SU transmission is proposed to protect the QoS of PUs. In [9], the tradeoff between interference alignment and the sum rate of SU is investigated in the multi-user multi-antenna CR networks. In [10], the nonstationary minimum-variance distortionless response beamformer is designed to perform the fast speed of convergence and solve the problem of interference to PU.…”

Section: Introductionmentioning

confidence: 99%

Energy Efficiency Optimization for multiuser OFDM-based Cognitive Heterogeneous networks

2019

KSII TIIS

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Reducing the interference to the licensed mobile users and obtaining the energy efficiency are key issues in cognitive heterogeneous networks. A corresponding rate loss constraint is proposed to be used for the sensing-based spectrum sharing (SBSS) model in cognitive heterogeneous networks in this paper. Resource allocation optimization strategy is designed for the maximum energy efficiency under the proposed interference constraint together with average transmission power constraint. An efficiency algorithm is studied to maximize energy efficiency due to the nonconvex optimal problem. Furthermore, the relationship between the proposed protection criterion and the conventional interference constraint strategy under imperfect sensing condition for the SBSS model is also investigated, and we found that the conventional interference threshold can be regarded as the upper bound of the maximum rate loss that the primary user could tolerate. Simulation results have shown the effectiveness of the proposed protection criterion overcome the conventional interference power constraint.

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Section: Introductionmentioning

confidence: 99%

Energy Efficiency Optimization for multiuser OFDM-based Cognitive Heterogeneous networks

2019

KSII TIIS

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“…Therefore, accurate interference modeling [149] is vital for the selection of an actual optimal channel among the available heterogeneous channels. The interdependencies of the channel characterization parameters and highly dynamic CRNs with heterogeneous spectrum holes make the channel characterization a challenging research issue within the CRNs.…”

Section: A Channel Characterizationmentioning

confidence: 99%

Channel Assignment Algorithms in Cognitive Radio Networks: Taxonomy, Open Issues, and Challenges

Ahmed

Gani

Abolfazli

et al. 2016

IEEE Commun. Surv. Tutorials

151

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Abstract-The cognitive radio is an emerging technology that enables dynamic spectrum access in wireless networks. The cognitive radio is capable of opportunistically using the available portions of a licensed spectrum to improve the application performance for unlicensed users. The opportunistic use of the available channels in the wireless environment requires dynamic channel assignment to efficiently utilize the available resources while minimizing the interference in the network. A challenging aspect of such algorithms is the incorporation of the channels' diverse characteristics, highly dynamic network conditions with respect to primary users' activity, and different fragmented sizes of the available channels. This paper presents a comprehensive survey on the state-of-the-art channel assignment algorithms in cognitive radio networks. We also classify the algorithms by presenting a thematic taxonomy of the current channel assignment algorithms in cognitive radio networks. Moreover, the critical aspects of the current channel assignment algorithms in cognitive radio networks are analyzed to determine the strengths and weaknesses of such algorithms. The similarities and differences of the algorithms based on the important parameters, such as routing dependencies, channel models, assignment methods, execution model, and optimization objectives, are also investigated. We also discuss open research issues and challenges of channel assignment in the cognitive radio networks.

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Research on precoding techniques of multi-beam satellite communication

Jiang

2021

2021 2nd International Symposium on Computer Engineering and Intelligent Communications (ISCEIC)

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An interference model for heterogeneous cognitive radio networks

Cited by 3 publications

References 16 publications

Energy Efficiency Optimization for multiuser OFDM-based Cognitive Heterogeneous networks

Energy Efficiency Optimization for multiuser OFDM-based Cognitive Heterogeneous networks

Channel Assignment Algorithms in Cognitive Radio Networks: Taxonomy, Open Issues, and Challenges

Research on precoding techniques of multi-beam satellite communication

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