Spectrum Exploration and Exploitation for Cognitive Radio: Recent Advances

Lundén, Jarmo; Koivunen, Visa; Poor, H. Vincent

doi:10.1109/msp.2014.2338894

Cited by 125 publications

(78 citation statements)

References 59 publications

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“…[5], and became popular after FCC (Federal Communications Commission) recommended it as a promising solution for supporting dynamic spectrum access [6] . Since then, a lot of researches have been carried out [7][8][9] . Here, we focus on two CR paradigms related to HetNet: OSA (Opportunistic Spectrum Access) and CSA (Concurrent Spectrum Access).…”

Section: Crmentioning

confidence: 99%

Wireless big data: transforming heterogeneous networks to smart networks

Huang

Tan

Liang

2017

J. Commun. Inf. Netw.

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In HetNets (Heterogeneous Networks), each network is allocated with fixed spectrum resource and provides service to its assigned users using specific RAT (Radio Access Technology). Due to the high dynamics of load distribution among different networks, simply optimizing the performance of individual network can hardly meet the demands from the dramatically increasing access devices, the consequent upsurge of data traffic, and dynamic user QoE (Quality-of-Experience). The deployment of smart networks, which are supported by SRA (Smart Resource Allocation) among different networks and CUA (Cognitive User Access) among different users, is deemed a promising solution to these challenges. In this paper, we propose a framework to transform HetNets to smart networks by leveraging WBD (Wireless Big Data), CR (Cognitive Radio) and NFV (Network Function Virtualization) techniques. CR and NFV support resource slicing in spectrum, physical layers, and network layers, while WBD is used to design intelligent mechanisms for resource mapping and traffic prediction through powerful AI (Artificial Intelligence) methods. We analyze the characteristics of WBD and review possible AI methods to be utilized in smart networks. In particular, the potential of WBD is revealed through high level view on SRA, which intelligently maps radio and network resources to each network for meeting the dynamic traffic demand, as well as CUA, which allows mobile users to access the best available network with manageable cost, yet achieving target QoS (Quality-of-Service) or QoE.

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Section: Crmentioning

confidence: 99%

Wireless big data: transforming heterogeneous networks to smart networks

Huang

Tan

Liang

2017

J. Commun. Inf. Netw.

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“…Distributed cooperative spectrum sensing is a well-researched area in the literature of wireless sensor networks (WSNs) [1], [15][16][17][18][19]. We can adopt such detection strategy in the context of CR networks.…”

Section: Motivation and Related Workmentioning

confidence: 99%

“…particularly as new wireless applications and technologies are continuously being developed [1]. The present spectrum allocation strategy worked perfectly in the past since it provided an optimum solution by reducing interference between active wireless users.…”

Section: Introductionmentioning

confidence: 96%

A Mathematical Approach for Hidden Node Problem in Cognitive Radio Networks

2017

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“…DSA seeks a more efficient usage of the available spectrum by allowing access of unlicensed (secondary) users while limiting interference to licensed (primary) users. This requires secondary users to have spectrum sensing capabilities [1] as well as access policies [15] for exploiting spectrum holes. Important effects such as large/small-scale fading, the hidden node problem, noise uncertainty, low SNR operation, etc., appear in the design and analysis of suitable detection schemes, and may be tackled, albeit with different effectivity, via cooperative detection (with limited data exchanges between sensors) and multiantenna detection (with centralized access to all data samples).…”

Section: Introductionmentioning

confidence: 99%

Multiantenna GLR Detection of Rank-One Signals With Known Power Spectral Shape Under Spatially Uncorrelated Noise

Sala-Alvarez

Vazquez-Vilar

López-Valcarce

et al. 2016

IEEE Trans. Signal Process.

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Abstract-We establish the generalized likelihood ratio (GLR) test for a Gaussian signal of known power spectral shape and unknown rank-one spatial signature in additive white Gaussian noise with an unknown diagonal spatial correlation matrix. This is motivated by spectrum sensing problems in dynamic spectrum access (DSA), in which the temporal correlation of the primary signal can be assumed known up to a scaling, and where the noise is due to an uncalibrated receive array. For spatially independent identically distributed (i.i.d.) noise, the corresponding GLR test reduces to a scalar optimization problem, whereas the GLR detector in the general non-i.i.d. case yields a more involved expression, which can be computed via alternating optimization methods. Low signal-to-noise ratio (SNR) approximations to the detectors are given, together with an asymptotic analysis showing the influence on detection performance of the signal power spectrum and SNR distribution across antennas. Under spatial rank-P conditions, we show that the rank-one GLR detectors are consistent with a statistical criterion that maximizes the output energy of a beamformer operating on filtered data. Simulation results support our theoretical findings in that exploiting prior knowledge on the signal power spectrum can result in significant performance improvement.

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Spectrum Exploration and Exploitation for Cognitive Radio: Recent Advances

Cited by 125 publications

References 59 publications

Wireless big data: transforming heterogeneous networks to smart networks

Wireless big data: transforming heterogeneous networks to smart networks

A Mathematical Approach for Hidden Node Problem in Cognitive Radio Networks

Multiantenna GLR Detection of Rank-One Signals With Known Power Spectral Shape Under Spatially Uncorrelated Noise

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