Optimal Resource Allocation and EE-SE Trade-Off in Hybrid Cognitive Gaussian Relay Channels

Hong, Xuemin; Zheng, Chao; Jing, Wang; Shi, Jianghong; Wang, Cheng-Xiang

doi:10.1109/twc.2015.2417550

Cited by 18 publications

(2 citation statements)

References 43 publications

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“…Spectrum-and energy-efficient resource allocation schemes have been studied and proposed in various scenarios. However, simultaneously optimizing spectrum and energy efficiency is not possible in most cases [67], [116]. Therefore, the tradeoff between spectrum and energy efficiency plays an important role in resource allocation with different network architectures and requirements.…”

Section: Spectrum-and Energy-efficient Designsmentioning

confidence: 99%

Intelligent Wireless Communications Enabled by Cognitive Radio and Machine Learning

Zhou,

Sun,

et al. 2017

Preprint

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The ability to intelligently utilize resources to meet the need of growing diversity in services and user behavior marks the future of wireless communication systems. Intelligent wireless communications aims at enabling the system to perceive and assess the available resources, to autonomously learn to adapt to the perceived wireless environment, and to reconfigure its operating mode to maximize the utility of the available resources. The perception capability and reconfigurability are the essential features of cognitive radio while modern machine learning techniques project great potential in system adaptation. In this paper, we discuss the development of the cognitive radio technology and machine learning techniques and emphasize their roles in improving spectrum and energy utility of wireless communication systems. We describe the state-of-the-art of relevant techniques, covering spectrum sensing and access approaches and powerful machine learning algorithms that enable spectrum-and energy-efficient communications in dynamic wireless environments. We also present practical applications of these techniques and identify further research challenges in cognitive radio and machine learning as applied to the existing and future wireless communication systems.

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Section: Spectrum-and Energy-efficient Designsmentioning

confidence: 99%

Intelligent Wireless Communications Enabled by Cognitive Radio and Machine Learning

Zhou,

Sun,

et al. 2017

Preprint

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“…On the other hand, the cognitive RR has low transmitted power, potentially broader bandwidth and low reliability. Essentially, these two different RRs are complementary in nature so, integrated system design is required for efficient utilization of both [44]. Thus, joint utilization mechanisms of licensed and cognitive RRs remain one of the research challenges in 5G-based CR cellular networks [45].…”

Section: Introductionmentioning

confidence: 99%

On Platform to Enable the Cognitive Radio Over 5G Networks

Hindia

Qamar

Ojukwu

et al. 2020

Wireless Pers Commun

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With the increase in the number of communication devices, the requirement for higher bandwidth is essential. To achieve this goal, research and industrial communities have both suggested that future wireless systems will take advantage of the numerous emerging technologies. Utilization of Cognitive Radio (CR) for the next-generation Fifth Generation (5G) communication technology is the major advancement for getting a higher bandwidth in a cellular communication network. In this paper, we present a comprehensive study of CR from the perspectives of spectrum allocation schemes, impact and role of MAC layer in spectrum sensing and sharing, CR application in multi-hop wireless networks, and challenges associated with channel selection and packet routing in multi-hop heterogeneous CR networks. This paper also presents the analysis, in literature, of a range of intelligent routing protocols that are considered viable for packets routing in CR networks. The need to address the issue of spectrum depletion and the apparent underutilization of available scarce spectrum resources in existing wireless networks is the primary motivation behind this study. Considering the fact that CR technology can potentially maximize the utilization of bulk of the unused communication spectrum bands for the future 5G of wireless network and beyond.

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Novel Analytical Model for Resource Allocation Over Cognitive Radio in 5G Networks

P¹,

Sundaraguru²

2019

Computational Statistics and Mathematical Modeling Methods in Intelligent Systems

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Optimal Resource Allocation and EE-SE Trade-Off in Hybrid Cognitive Gaussian Relay Channels

Cited by 18 publications

References 43 publications

Intelligent Wireless Communications Enabled by Cognitive Radio and Machine Learning

Intelligent Wireless Communications Enabled by Cognitive Radio and Machine Learning

On Platform to Enable the Cognitive Radio Over 5G Networks

Novel Analytical Model for Resource Allocation Over Cognitive Radio in 5G Networks

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