Secondary User QoE Enhancement Through Learning Based Predictive Spectrum Access in Cognitive Radio Networks

Agarwal, Anirudh; Dubey, Shivangi; Gangopadhyay, R.; Debnath, Soumitra

doi:10.1007/978-3-319-40352-6_14

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…In the work of Agarwal et al, PBS has been shown to perform significantly better than the RS, but this PBS does not incorporate an important parameter, ie, the probability of availability ( P avail ) of a PU channel, which is defined as

P_{avail} = 1 - S O P .

…”

Section: Dca With Enhanced Su Qoementioning

confidence: 99%

Predictive spectrum occupancy probability‐based spatio‐temporal dynamic channel allocation map for future cognitive wireless networks

Agarwal

Gangopadhyay

2018

Trans Emerging Tel Tech

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For a cognitive radio (CR) user to dynamically access the available primary user channels, the spectrum sensing data is required at various locations; however, the data is not generally available at the points present in between the two spectrum sensors. To obtain this data, it is also not feasible to conduct the spectrum measurement surveys at every location. This calls for an interpolation‐based spectrum occupancy data collection in the space. Moreover, the information about the future primary user activity is necessary for highly enhanced dynamic channel allocation with better quality of experience (QoE) of CR users in terms of improved channel utilization and reduced spectrum sensing energy requirement. Most of the existing works are inclined toward either spatial spectrum interpolation or temporal spectrum prediction. In this work, a novel hybridized approach combining the Kriging‐based statistical spatial interpolation and the recurrent neural networks–based temporal prediction of the spectrum has been proposed to obtain the spectrum occupancy probability information using empirical spectrum measurements data in cellular CR networks. Furthermore, the aforementioned framework has been utilized for dynamic channel allocation with the help of a newly investigated CR channel allocation scheme. The corresponding results have been validated through various CR QoE measures and compared with the existing channel allocation strategies, where it is found that the proposed channel allocation scheme significantly outperforms the other schemes with the enhanced QoE of CR. Finally, a spatio‐temporal channel allocation map has been presented for secondary user to perform its best possible CR operation with efficient dynamic spectrum access.

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P_{avail} = 1 - S O P .

…”

Section: Dca With Enhanced Su Qoementioning

confidence: 99%

Predictive spectrum occupancy probability‐based spatio‐temporal dynamic channel allocation map for future cognitive wireless networks

Agarwal

Gangopadhyay

2018

Trans Emerging Tel Tech

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“…In particular, we model the arrival process of PUs as an IPP. is model has been reported suitable for web browsing [16] or data traffic [17].…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Cognitive Radio Networks with Imperfect Spectrum Sensing and Bursty Primary User Traffic

Salameh

Bruneel

Wittevrongel

2020

Mathematical Problems in Engineering

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In this paper, we introduce a four-dimensional continuous-time Markov chain model to evaluate the performance of cognitive radio networks. In such networks, secondary (unlicensed) users may opportunistically use the frequency channels not currently occupied by primary (licensed) users in order to increase the utilization of the wireless spectrum. Secondary users perform channel sensing before as well as during transmission in order not to interfere with primary users. The proposed model assumes that primary users arrive according to a bursty arrival process and moreover takes the possible occurrence of sensing errors (false alarms and misdetections) into account. Several performance measures including the collision rate between primary and secondary users, the blocking probabilities of primary or secondary users, and the mean delay of secondary users are derived and illustrated through numerical examples. The results show that the system performance strongly depends on the degree of burstiness in the arrival process of primary users. It is also observed that the quality of service of the primary network can be seriously compromised due to misdetection by secondary users.

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“…Each of these techniques has been implemented in three different PU data traffic scenarios, which are representative of some known statistical traffic models such as Poisson, interrupted Poisson (IPP) and self‐similar (SS) traffic [10]. The most accurate ML technique is then utilised in one data traffic model for a multiple PU channel scenario, thereby claiming the improvement in SU spectrum utilisation, reduction in spectrum sensing energy as well as a reduction in the CSF [11]. Furthermore, in order to prove the efficacy and versatility of the above framework for enhanced DSA, it is extended to a more realistic scenario, i.e.…”

Section: Introductionmentioning

confidence: 99%

Learning‐based predictive dynamic spectrum access framework: a practical perspective for enhanced QoE of secondary users

et al. 2018

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Secondary User QoE Enhancement Through Learning Based Predictive Spectrum Access in Cognitive Radio Networks

Cited by 3 publications

References 8 publications

Predictive spectrum occupancy probability‐based spatio‐temporal dynamic channel allocation map for future cognitive wireless networks

Predictive spectrum occupancy probability‐based spatio‐temporal dynamic channel allocation map for future cognitive wireless networks

Performance Evaluation of Cognitive Radio Networks with Imperfect Spectrum Sensing and Bursty Primary User Traffic

Learning‐based predictive dynamic spectrum access framework: a practical perspective for enhanced QoE of secondary users

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