Fair Optimal Resource Allocation in Cognitive Radio Networks With Co-Channel Interference Mitigation

Xu, Woping; Qiu, Runhe; Cheng, Julian

doi:10.1109/access.2018.2845460

Cited by 12 publications

(10 citation statements)

References 29 publications

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“…The authors of [11,14] introduced the concept of the interference weight which impacts on the signal quality, network density and mobility. Xu et al in [22] mentioned about the fairness metric modelling as a function of the signal to interference plus noise ratio (SINR). Based on how fairer communication is adequate for different CR applications/purposes, their SINR requirements would be different.…”

Section: Background and Related Workmentioning

confidence: 99%

Interference mitigation and capacity enhancement of cognitive radio networks using modified greedy algorithm/channel assignment and power allocation techniques

2020

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Section: Background and Related Workmentioning

confidence: 99%

Interference mitigation and capacity enhancement of cognitive radio networks using modified greedy algorithm/channel assignment and power allocation techniques

2020

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“…To keep the long-term power budget of SU, the transmit power constraint is also used. Then, it is given by (11) in section Ⅲ…”

Section: Optimal Ee Under the Conventional Interference Power Constraintmentioning

confidence: 99%

“…The conventional interference power constraint by reducing the transmission power of SU has been always used to protect the interests of PU in many researches. In [11], a general multi-ring model in the heterogeneous CR network is proposed to investigate the relationship between the SUs' distribution and the interference to PU. The interference generated from heterogeneous CR network is investigated by employing several strategies, such as the SU's transmission power control, the secondary transmitters competitive strategy and the secondary receivers association scheme.…”

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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“…Regarding the first type of interference, a reasonable threshold is commonly set to guarantee the QoS of PUs. For others, orthogonal transmission, power control, and interference constraints are usually used for interference elimination [ 10 ]. In this paper, we expect to achieve CCI mitigation through optimal resource allocation under interference constraints, including channel selection and power adaptation.…”

Section: Introductionmentioning

confidence: 99%

A Graph Convolutional Network-Based Deep Reinforcement Learning Approach for Resource Allocation in a Cognitive Radio Network

Zhao

Qin

Song

et al. 2020

Sensors

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Cognitive radio (CR) is a critical technique to solve the conflict between the explosive growth of traffic and severe spectrum scarcity. Reasonable radio resource allocation with CR can effectively achieve spectrum sharing and co-channel interference (CCI) mitigation. In this paper, we propose a joint channel selection and power adaptation scheme for the underlay cognitive radio network (CRN), maximizing the data rate of all secondary users (SUs) while guaranteeing the quality of service (QoS) of primary users (PUs). To exploit the underlying topology of CRNs, we model the communication network as dynamic graphs, and the random walk is used to imitate the users’ movements. Considering the lack of accurate channel state information (CSI), we use the user distance distribution contained in the graph to estimate CSI. Moreover, the graph convolutional network (GCN) is employed to extract the crucial interference features. Further, an end-to-end learning model is designed to implement the following resource allocation task to avoid the split with mismatched features and tasks. Finally, the deep reinforcement learning (DRL) framework is adopted for model learning, to explore the optimal resource allocation strategy. The simulation results verify the feasibility and convergence of the proposed scheme, and prove that its performance is significantly improved.

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Fair Optimal Resource Allocation in Cognitive Radio Networks With Co-Channel Interference Mitigation

Cited by 12 publications

References 29 publications

Interference mitigation and capacity enhancement of cognitive radio networks using modified greedy algorithm/channel assignment and power allocation techniques

Interference mitigation and capacity enhancement of cognitive radio networks using modified greedy algorithm/channel assignment and power allocation techniques

Energy Efficiency Optimization for multiuser OFDM-based Cognitive Heterogeneous networks

A Graph Convolutional Network-Based Deep Reinforcement Learning Approach for Resource Allocation in a Cognitive Radio Network

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