Radio Environment Maps for 5G Cognitive Radio Network

Κακάλου, Ιωάννα; Psannis, Kostas E.; Goudos, Sotirios K.; Yioultsis, Traianos V.; Kantartzis, Nikolaos V.; Ishibashi, Yutaka

doi:10.1109/mocast.2019.8741554

Cited by 15 publications

(10 citation statements)

References 12 publications

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“…The database server performs hypothesis testing using Eqs. ( 13), (14), and (7) in each mesh and determines whether the BS has been relocated during the night after the observation. If the alternative hypothesis 1 is accepted in the th mesh, the database server registers the new average received signal power value¯, + for the th mesh.…”

Section: Radio Map Updating Based On Welch's -Testmentioning

confidence: 99%

Dynamic Radio Map Using Statistical Hypothesis Testing

Katagiri

Sato

Inage

et al. 2021

IEEE Trans. Cogn. Commun. Netw.

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Private fifth-generation mobile communication networks (5G) have garnered considerable attention for providing various communication services. In private 5G systems, such as those employed in smart factories, it is necessary to appropriately manage the interference power among diverse systems because each nonpublic operator deploys a gNodeB in the local area and utilizes a similar allocated frequency. Several researchers have discussed the use of a radio map, which stores the spatial distribution of received signal power values as a tool for managing intra-system interference. Radio maps are typically applied to television white space, where the location of the base station (BS) is fixed and the average value of the received signal power exhibits time-invariant characteristics. However, in private 5G networks, the estimation accuracy using a conventional radio map is lowered because the location of the BS may be changed when the factory is rearranged. In this study, a database server is used to infer a significant difference in the average values of the received signal power by means of Welch's -test; the server then updates the radio map based on the test results. The simulation results indicate that the proposed method can detect changes in the BS location and update the radio map accurately.

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Section: Radio Map Updating Based On Welch's -Testmentioning

confidence: 99%

Dynamic Radio Map Using Statistical Hypothesis Testing

Katagiri

Sato

Inage

et al. 2021

IEEE Trans. Cogn. Commun. Netw.

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“…The work carried out by Halabian [16] has presented an optimization concept for resource allocation that is carried out in a distributed manner. Kakalou et al [17] have used the concept of an environmental map of radio considering the heterogeneous environment for the cognitive radio network. Khan et al [18] have used the evidence theory for formulating effective feature extraction schemes over cognitive radio for obtaining better spectral efficiency.…”

Section: A the Backgroundmentioning

confidence: 99%

Novel Analytical Framework for harnessing Cognitive Radio Resource Optimization in 5G Networks

B P,

Sundaraguru*

2019

IJITEE

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The adoption of cognitive radio technology is characterized by various beneficial characteristics that can facilitate better spectrum sensing performance in a 5G network and thereby acting as a boosting element towards a high data transmission rate. However, it is also characterized by various challenges that limit the significant development in resource utilization in 5G. Therefore, this paper introduces a novel and simplified mechanism that facilitates the 5G network to perform better in data transmission and its associated quality of it. The proposed system also performs modeling using practical constraints associated with the usage of cognitive radio over 5G networks using a convex optimization approach. The model is simulated using practical environmental parameters to prove that the proposed system excels better performance in faster processing and quality signal in contrast to the existing resource allocation scheme exercised in 5G networks.

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“…REMs represent an important method of information management 6 for the operation of cognitive radio networks, 7,8 replacing or complementing spectrum sensing information 9 and spectrum utilization in wireless networks. 10 They provide a precise awareness of the electromagnetic environment in the spatial domain by processing the measurement of the received signal power, which can be gathered by sample capable devices like sensors fixedly deployed within a smart city context.…”

Section: Introductionmentioning

confidence: 99%

Radio environment map construction by adaptive ordinary Kriging algorithm based on affinity propagation clustering

Xia

Zha

Huang

et al. 2020

International Journal of Distributed Sensor Networks

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In the era of 5G mobile communication, radio environment maps are increasingly viewed as a powerful weapon for the optimization of spectrum resources, especially in the field of autonomous vehicles. However, due to the constraint of limited resources when it comes to sensor networks, it is crucial to select a suitable scale of sensor measurements for radio environment map construction. This article proposes an adaptive ordinary Kriging algorithm based on affinity propagation clustering as a novel spatial interpolation method for the construction of the radio environment map, which can provide precise awareness of signal strength at locations where no measurements are available. Initially, a semivariogram is obtained from all the sensor measurements. Then, in order to select the minimum scale of measurements and at the same time guarantee accuracy, the affinity propagation clustering is introduced in the selection of sensors. Moreover, the sensor estimation groups are created based on the clustering result, and estimation results are obtained by ordinary Kriging. In the end, the simulation of the proposed algorithm is analyzed through comparisons with three conventional algorithms: inverse distance weighting, nearest neighbor, and ordinary Kriging. As a result, the conclusion can be drawn that the proposed algorithm is superior to others in accuracy as well as in efficiency.

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Radio Environment Maps for 5G Cognitive Radio Network

Cited by 15 publications

References 12 publications

Dynamic Radio Map Using Statistical Hypothesis Testing

Dynamic Radio Map Using Statistical Hypothesis Testing

Novel Analytical Framework for harnessing Cognitive Radio Resource Optimization in 5G Networks

Radio environment map construction by adaptive ordinary Kriging algorithm based on affinity propagation clustering

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