Efficient Resource Allocation in Cognitive Networks

Yaqot, Abdullah; Hoeher, Peter Adam

doi:10.1109/tvt.2016.2642785

Cited by 10 publications

(8 citation statements)

References 32 publications

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“…The development of MCS has resulted in various novel sensing applications. Some typical examples include the air quality inspection application Common Sense for air quality monitoring [5] by the University of California Berkeley, the Creek Watch application to evaluate city water resources [15] by IBM, and the Nericell system [16] by Microsoft to monitor road and traffic condition implemented by piggybacking on smartphones that users carry with them in normal course. MCS has attracted much attention from researchers due to advantages such as ubiquitous sensor nodes, good participant mobility, low maintenance cost, and rich sensing data types.…”

Section: Related Workmentioning

confidence: 99%

“…We use the sample data to estimate the value of sensing area. Our Kriging weights are derived through minimizing the estimator error variance; that is, min ∈ var ( * − ) (16) under the unbiasedness constraint, given by the following.…”

Section: Kriging Interpolatingmentioning

confidence: 99%

“…The mathematical expectation of the sample radio environment information is zero. Assuming the intrinsic stationarity and utilizing Lagrange multiplier optimization algorithm to minimize the estimator error variance (16) under the unbiasedness constraint (17), the Kriging weights in (8) can be calculated as…”

Section: Kriging Interpolatingmentioning

confidence: 99%

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Radio Environment Map Construction by Kriging Algorithm Based on Mobile Crowd Sensing

Han

Liao

et al. 2019

Wireless Communications and Mobile Computing

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In the IoT era, 5G will enable various IoT services such as broadband access everywhere, high user and devices mobility, and connectivity of massive number of devices. Radio environment map (REM) can be applied to improve the utilization of radio resources for the access control of IoT devices by allocating them reasonable wireless spectrum resources. However, the primary problem of constructing REM is how to collect the large scale of data. Mobile crowd sensing (MCS), leveraging the smart devices carried by ordinary people to collect information, is an effective solution for collecting the radio environment information for building the REM. In this paper, we build a REM collecting prototype system based on MCS to collect the data required by the radio environment information. However, limited by the budget of the platform, it is hard to recruit enough participants to join the sensing task to collect the radio environment information. This will make the radio environment information of the sensing area incomplete, which cannot describe the radio information accuracy. Considering that the Kriging algorithm has been widely used in geostatistics principle for spatial interpolation for Kriging giving the best unbiased estimate with minimized variance, we utilize the Kriging interpolation algorithm to infer complete radio environment information from collected sample radio environment information data. The interpolation performance is analyzed based on the collected sample radio environment information data. We demonstrate experiments to analyze the Kriging interpolation algorithm interpolation results and error and compared them with the nearest neighbor (NN) and the inverse distance weighting (IDW) interpolation algorithms. Experiment results show that the Kriging algorithm can be applied to infer radio environment information data based on the collected sample data and the Kriging interpolation has the least interpolation error.

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Section: Related Workmentioning

confidence: 99%

Section: Kriging Interpolatingmentioning

confidence: 99%

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Radio Environment Map Construction by Kriging Algorithm Based on Mobile Crowd Sensing

Han

Liao

et al. 2019

Wireless Communications and Mobile Computing

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“…To attain a beneficial performance, it is required that we use an appropriate resource allocation strategy. Hence, the authors in surveys [12][13][14] express some excellent techniques for allocating CR networks radio resources. As a general expression, the issue of resource allocation can be considered in two categories based on cognitive or non-cognitive networks [15,16].…”

Section: Introductionmentioning

confidence: 99%

Resource allocation algorithm for downlink MIMO‐OFDMA based cognitive radio networks in spectrum underlay scenario

Mohammadi

Andargoli

2020

IET Communications

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“…It was shown that the EE can be further improved by using this constraint. Recently, in order to improve the SE of the secondary network, MIMO techniques and orthogonal frequency-division multiplexing (OFDM) techniques were exploited in CRNs and the optimal resource allocation strategy was designed [12].…”

Section: Introductionmentioning

confidence: 99%

Resource Allocation Based on Deep Neural Networks for Cognitive Radio Networks

Zhou

Zhang

et al. 2018

2018 IEEE/CIC International Conference on Communications in China (ICCC)

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Resource allocation is of great importance in the next generation wireless communication systems, especially for cognitive radio networks (CRNs). Many resource allocation strategies have been proposed to optimize the performance of CRNs. However, it is challenging to implement these strategies and achieve real-time performance in wireless systems since most of them need accurate and timely channel state information and/or other network statistics. In this paper a resource allocation strategy based on deep neural networks (DNN) is proposed and the training method is presented to train the neural networks. Simulation results show that our proposed strategy based on DNN is efficient in terms of the computation time compared with the conventional resource allocation schemes.

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Efficient Resource Allocation in Cognitive Networks

Cited by 10 publications

References 32 publications

Radio Environment Map Construction by Kriging Algorithm Based on Mobile Crowd Sensing

Radio Environment Map Construction by Kriging Algorithm Based on Mobile Crowd Sensing

Resource allocation algorithm for downlink MIMO‐OFDMA based cognitive radio networks in spectrum underlay scenario

Resource Allocation Based on Deep Neural Networks for Cognitive Radio Networks

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