All-Powerful Learning Algorithm for the Priority Access in Cognitive Network

Almasri, Mahmoud; Mansour, Ali; Moy, Christophe; Assoum, A.; Osswald, Christophe; Jeune, Denis Le

doi:10.23919/eusipco.2019.8903016

Cited by 6 publications

(6 citation statements)

References 11 publications

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“…The following are five recent papers on multi-user access in CRN. (Almasri et al, 2019(Almasri et al, , 2020 analyze opportunistic access to the spectrum in CR for one or more secondary users (SU) through a priority access policy called All-Powerful Learning (APL). The strategy implemented for multi-user analysis is for users to analyze channel opportunities separately without any cooperation or prior knowledge of available channels.…”

Section: Literature Reviewmentioning

confidence: 99%

Spectral decision for cognitive radio networks in a multi-user environment

Giral¹,

Hernández²,

Salgado³

2021

Heliyon

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Cognitive radio networks promote better spectral efficiency of the electric radio spectrum. The vast majority of current spectral decision models for cognitive radio networks evaluate their performance based on a single secondary user. In reality, the network can experience multiple requests from spectral opportunities. Based on this, the intent of this article is to present and evaluate a spectral decision model for cognitive radio networks in a multi-user environment taking into account the effect of the decisions of the SU on the usefulness of the other SU. To achieve this, a spectral decision model was developed that allows secondary users to share relevant information before accessing the spectrum so that they can select the most appropriate spectral opportunities. The evaluation and validation of the model was performed using three multicriteria decision-making algorithms under the metric of the number of total handoffs in a conventional scenario and a real scenario, in the conventional scenario, only users that match the input of the multiuser module are included; in the real scenario, in addition to the conventional users, users that enter and leave at random times are included, a feature that alters the models for estimating the behavior of the radio environment. The results show better performance of the TOPSIS algorithm over VIKOR and SAW. The most important contribution of this work is the evaluation of the performance of the spectral decision algorithms implemented in a multi-user environment that allows multiple access and exchange of information between users, with experimental spectral occupation data.

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Section: Literature Reviewmentioning

confidence: 99%

Spectral decision for cognitive radio networks in a multi-user environment

Giral¹,

Hernández²,

Salgado³

2021

Heliyon

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“…Based on our policy, All-Powerful Learning (APL), proposed in [26], we extend -UCB to consider multiple SUs (see algorithm 5).…”

Section: -Ucb For the Multi-priority Accessmentioning

confidence: 99%

Distributed Competitive Decision Making Using Multi-Armed Bandit Algorithms

Almasri

Mansour

Moy

et al. 2021

Wireless Pers Commun

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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“…• Competitive policy for the priority learning access (PLA): To manage a decentralized secondary network, we propose a learning policy, called PLA, that takes the priority access into account. To the best of our knowledge, PLA represents the first competitive learning policy that successfully handles the priority dynamic access where the number of SUs changes over time [38], while only the priority access or the dynamic access are considered in several learning policies, such as musical chair and dynamic musical chair [31], MEGA [32], SLK [33], and kth MAB [34]. In [38], PLA shows its superiority under UCB and TS compared to SLK, MEGA, musical chair, and dynamic musical chair.…”

Section: Contributions and Paper Organizationmentioning

confidence: 99%

Distributed algorithm under cooperative or competitive priority users in cognitive networks

Almasri

Mansour

Moy

et al. 2020

J Wireless Com Network

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Opportunistic spectrum access (OSA) problem in cognitive radio (CR) networks allows a secondary (unlicensed) user (SU) to access a vacant channel allocated to a primary (licensed) user (PU). By finding the availability of the best channel, i.e., the channel that has the highest availability probability, a SU can increase its transmission time and rate. To maximize the transmission opportunities of a SU, various learning algorithms are suggested: Thompson sampling (TS), upper confidence bound (UCB),-greedy, etc. In our study, we propose a modified UCB version called AUCB (Arctan-UCB) that can achieve a logarithmic regret similar to TS or UCB while further reducing the total regret, defined as the reward loss resulting from the selection of non-optimal channels. To evaluate AUCB's performance for the multiuser case, we propose a novel uncooperative policy for a priority access where the kth user should access the kth best channel. This manuscript theoretically establishes the upper bound on the sum regret of AUCB under the single or multiuser cases. The users thus may, after finite time slots, converge to their dedicated channels. It also focuses on the Quality of Service AUCB (QoS-AUCB) using the proposed policy for the priority access. Our simulations corroborate AUCB's performance compared to TS or UCB.

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All-Powerful Learning Algorithm for the Priority Access in Cognitive Network

Cited by 6 publications

References 11 publications

Spectral decision for cognitive radio networks in a multi-user environment

Spectral decision for cognitive radio networks in a multi-user environment

Distributed Competitive Decision Making Using Multi-Armed Bandit Algorithms

Distributed algorithm under cooperative or competitive priority users in cognitive networks

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