A Game Theory Based Hybrid NOMA for Efficient Resource Optimization in Cognitive Radio Networks

Kumar, Ajeet; Kumar, Krishan

doi:10.1109/tnse.2021.3116669

Cited by 7 publications

(2 citation statements)

References 49 publications

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“…In Reference 17, the authors have presented an auction framework and Stackelberg game‐oriented optimal network's resource selection (AFSOS) scheme to attain the acceptable interference power constraints and highest payoff in CRNs. The work presented in Reference 18 suggests a preference relation game (PRG) scheme using a cooperative coalitional game (CCG) in hybrid NOMA (HNOMA) to maximize the energy efficiency in CRNs. In Reference 19, authors have presented a network‐assisted networks' resource selection (NANS) technique to ensure the guaranteed QoS and maintain mobility in CRNs.…”

Section: Related Workmentioning

confidence: 99%

Cooperation and energy harvesting based spectrum sensing schemes for green cognitive radio networks

Srivastava

Kaur

2022

Trans Emerging Tel Tech

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Cognitive radio network (CRNs) with energy harvesting approach to identify unused spectrum bands and utilize them opportunistically is observed as an evolving technology to handle spectrum scarcity problems. In CRNs, the secondary users (SUs) continuously sense the primary user's (PUs) spectrum to secure the data transmission of PUs and to access the vacant PU's spectrum for their data transmission. For designing future green networks, it is essential that the spectrum sensing process must be accurate, rapid, and energy‐efficient. Therefore, in this work, three sensing schemes are proposed. The first is a cooperation‐based joint cooperation spectrum sensing (JCSS) scheme in which the cooperative CRNs permit the SUs, working in an identical band, to cooperate, and this cooperation approach decreases sensing time and improves sensing probability for vacant spectrum. The result shows that the sensing probability for a vacant spectrum is increased by 34%. The proposed JCSS scheme considers the inherent asymmetry property of the network; therefore, it helps to improve the overall rapidity gain of the networks. The other two proposed sensing schemes, preset spectrum sensing (PSS) and viscous spectrum sensing (VSS), are based on energy harvesting that analyzes the percentage of energy‐saving for various states. The suggested schemes are better in terms of scalability because, in these schemes, it is not essential to sense all spectrums, which leads to a reduction in sensing time.

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Section: Related Workmentioning

confidence: 99%

Cooperation and energy harvesting based spectrum sensing schemes for green cognitive radio networks

Srivastava

Kaur

2022

Trans Emerging Tel Tech

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“…With the rapid emergence of next generation (5G) [1][2][3] communication technology, some new features have emerged in wireless communications, such as small delay, high bandwidth, low energy consumption, etc. It promotes the development of some new technologies, such as non-orthogonal multiple access (NOMA) [4,5] technology. Wireless sensor networks (WSNs) [6,7], as a vital part of 5G technology and a crucial branch of the IoT [8], has once again evolved into the topical issues for researchers.…”

Section: Introductionmentioning

confidence: 99%

3D many-objective DV-hop localization model with NSGA3

Wang,

Li,

Cai

2023

Soft Comput

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Wireless sensor location is a challenging task issue in the Internet of Things (IoT). Distance vector-hop (DV-hop) algorithm provides a range-free positioning scheme, but its position prediction method based on least square method brings a large positioning error. To overcome this issue, this paper constructs a three-dimensional (3D) many-objective positioning model. Specifically, we consider many factors, such as the error characteristics of the estimated distance, the distribution characteristics of nodes and the computational cost. Based on these factors, we propose a many-objective 3D-DV-hop positioning model, and propose a data preprocessing strategy and outlier removal strategy. Finally, a fashionable many-objective optimization algorithm is employed to solve the model. The experimental results show that the model proposed in this paper has great advantages in accuracy and robustness, and is superior to the current single and multi-objective positioning model.

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Globally Optimal Resource Allocation and Time Scheduling in Downlink Cognitive CRAN Favoring Big Data Requests

et al. 2022

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This paper is concerned with a cognitive cloud radio access network (CRAN) with a special attention to efficient and reliable transmission of big data. In particular, the paper focuses on optimizing performance of secondary users (SUs) in the downlink. Existing approaches either try to maximize the number of accepted SUs or the sum data rate of accepted SUs. The first approach unfairly favors users with small data requests, whereas the second approach allocates most resources to users with better channel conditions. In contrast, this paper develops a novel approach that favors big data requests while simultaneously maintaining a certain degree of fairness among SUs. To this end, we first introduce a novel objective function that allows us to jointly optimize deadline-aware time scheduling, spectrum allocation, SU selection, and remote radio head (RRH) allocation for SUs. Second, we demonstrate that finding the global optimum solution for the formulated problem entails the enumeration of all colorful independent sets on a generalized interval graph which is known to be NP-hard. Third, we propose a dynamic programming (DP) approach which yields the global optimum solution at a reduced computational cost. Fourth, we analyze the complexity of the proposed DP approach and numerically compare its performance against existing baseline algorithms. Numerical and simulation results revealed that our solution favors big data users while incurring only a small degradation in the fairness index. Our proposed solution is practical for small-to-medium size networks. Furthermore, it offers a benchmark against which any new sub-optimal low-complexity algorithm can be compared to determine how far from the global optimum its performance is.

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A Game Theory Based Hybrid NOMA for Efficient Resource Optimization in Cognitive Radio Networks

Cited by 7 publications

References 49 publications

Cooperation and energy harvesting based spectrum sensing schemes for green cognitive radio networks

Cooperation and energy harvesting based spectrum sensing schemes for green cognitive radio networks

3D many-objective DV-hop localization model with NSGA3

Globally Optimal Resource Allocation and Time Scheduling in Downlink Cognitive CRAN Favoring Big Data Requests

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