Ten years of research in spectrum sensing and sharing in cognitive radio

Lu, Lu; Zhou, Xiangwei; Onunkwo, Uzoma; Li, Geoffrey Ye

doi:10.1186/1687-1499-2012-28

Cited by 217 publications

(133 citation statements)

References 134 publications

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“…Since spectrum is a limited resource, efforts are focused on more flexible and dynamic spectrum sharing and licensing schemes. This has paved the way for smart sensing and adaptation technology called cognitive radio (CR), which allows unlicensed terminals to opportunistically access underutilized licensed bands [3]. Therefore, opportunistic spectrum access via CR and improved signal processing via DAS-MCC are expected to mitigate the spectrum scarcity problem for future applications.…”

Section: A Backgroundmentioning

confidence: 99%

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System level simulation and radio resource management for distributed antenna systems with cognitive radio and multi-cell cooperation

Sámano‐Robles¹,

Gameiro

Pereira

2015

2015 Fourth International Conference on Future Generation Communication Technology (FGCT)

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The performance of wireless networks will experience a considerable improvement by the use of novel technologies such as distributed antenna systems (DASs), multi-cell cooperation (MCC), and cognitive radio (CR). These solutions have shown considerable gains at the physical-layer (PHY). However, several issues remain open in the system-level evaluation, radio resource management (RRM), and particularly in the design of billing/licensing schemes for this type of system. This paper proposes a system-level simulator (SLS) that will help in addressing these issues. The paper focuses on the description of the modules of a generic SLS that need a modification to cope with the new transmission/economic paradigms. An advanced RRM solution is proposed for a multi-cell DAS with two levels of cooperation: inside the cell (intra-cell) to coordinate the transmission of distributed nodes within the cell, and between cells (inter-cell or MCC) to adapt cell transmissions according to the collected inter-cell interference measurements. The RRM solution blends network and financial metrics using the theory of multiobjective portfolio optimization. The core of the RRM solution is an iterative weighted least squares (WLS) optimization algorithm that aims to schedule in a fair manner as many terminals as possible across all the radio resources of the available frequency bands (licensed and non-licensed), while considering different economic metrics. The RRM algorithm includes joint terminal scheduling, link adaptation, space division multiplexing, spectrum selection, and resource allocation. Abstract-The performance of wireless networks will experience a considerable improvement by the use of novel technologies such as distributed antenna systems (DASs), multi-cell cooperation (MCC), and cognitive radio (CR). These solutions have shown considerable gains at the physical-layer (PHY). However, several issues remain open in the system-level evaluation, radio resource management (RRM), and particularly in the design of billing/licensing schemes for this type of system. This paper proposes a system-level simulator (SLS) that will help in addressing these issues. The paper focuses on the description of the modules of a generic SLS that need a modification to cope with the new transmission/economic paradigms. An advanced RRM solution is proposed for a multi-cell DAS with two levels of cooperation: inside the cell (intra-cell) to coordinate the transmission of distributed nodes within the cell, and between cells (inter-cell or MCC) to adapt cell transmissions according to the collected inter-cell interference measurements. The RRM solution blends network and financial metrics using the theory of multiobjective portfolio optimization. The core of the RRM solution is an iterative weighted least squares (WLS) optimization algorithm that aims to schedule in a fair manner as many terminals as possible across all the radio resources of the available frequency bands (licensed and non-licensed), while considering different economic metrics....

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Section: A Backgroundmentioning

confidence: 99%

“…Most of the existing literature on these technologies has been focused on physical (PHY) layer aspects (see [1]- [3]). However, several other issues need to be addressed before these technologies are commercialized.…”

Section: B Open Issues and Previous Workmentioning

confidence: 99%

System level simulation and radio resource management for distributed antenna systems with cognitive radio and multi-cell cooperation

Sámano‐Robles¹,

Gameiro

Pereira

2015

2015 Fourth International Conference on Future Generation Communication Technology (FGCT)

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“…In [6], an overview of cognitive radio system sensing techniques such as local spectrum sensing technique based on hypothesis testing criteria etc. It also include various techniques to improve the sensing performance .…”

Section: Survey Of Relevant Literaturementioning

confidence: 99%

Overview of Cognitive Radio Networks

Berwal¹,

Sindhu²

2017

ijarcs

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In today's era, there is rapid increase in use of wireless technologies. The main backbone of wireless technology is radio spectrum. As radio spectrum is allocated to the fixed users due to which it becomes sometimes underutilized and sometimes scarce. To efficiently use the spectrum bands, Cognitive radio (CR) technology was developed which adapts to the real-time changes in the parameters without causing any interference to other networks. Cognitive radio network helps to allocate the bands to the unlicensed users too when they are not used by the fixed (licensed) users. This paper gives an overview of the CR networks.

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“…Cognitive radio is an effective approach to improve spectrum efficiency by enabling SU to reuse the spectrum band of PU [5]. In order to avoid interfering with PU, SU should sense whether the band is being used by PU [6].…”

Section: Introductionmentioning

confidence: 99%

Analysis and optimization of sensing agility in cognitive radio

Sun¹,

Peng²,

Gao³

et al. 2016

Proceedings of the 2016 3rd International Conference on Materials Engineering, Manufacturing Technology and Control

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Abstract. Cognitive radio (CR) technology provides a new approach to solve the problem of wireless spectrum resource scarcity by allowing secondary users (SUs) to access the licensed spectrum in case that SUs do not interfere primary user (PU). One of the most challenging tasks in CR is spectrum sensing. This paper concentrates on the state change of PU's disappearance and analyzes the agility of spectrum sensing via evaluating its sensing time. Some expressions for sensing time are deduced. Based on these expressions, this paper also investigates how to optimize the sensing agility by choosing an appropriate detection period. Simulation results are provided to illustrate the validity of agility analysis and agility optimization in this paper.

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Ten years of research in spectrum sensing and sharing in cognitive radio

Cited by 217 publications

References 134 publications

System level simulation and radio resource management for distributed antenna systems with cognitive radio and multi-cell cooperation

System level simulation and radio resource management for distributed antenna systems with cognitive radio and multi-cell cooperation

Overview of Cognitive Radio Networks

Analysis and optimization of sensing agility in cognitive radio

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