Distributed algorithm under cooperative or competitive priority users in cognitive networks
Abstract 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...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2020-07-01
|
| Series: | EURASIP Journal on Wireless Communications and Networking |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13638-020-01738-w |
| _version_ | 1828475351100030976 |
|---|---|
| author | Mahmoud Almasri Ali Mansour Christophe Moy Ammar Assoum Christophe Osswald Denis Lejeune |
| author_facet | Mahmoud Almasri Ali Mansour Christophe Moy Ammar Assoum Christophe Osswald Denis Lejeune |
| author_sort | Mahmoud Almasri |
| collection | DOAJ |
| description | Abstract 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), ε-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 multi-user 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 multi-user 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. |
| first_indexed | 2024-12-11T06:11:32Z |
| format | Article |
| id | doaj.art-395f73525e4f4d7aa68166f56ce0b873 |
| institution | Directory Open Access Journal |
| issn | 1687-1499 |
| language | English |
| last_indexed | 2024-12-11T06:11:32Z |
| publishDate | 2020-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Wireless Communications and Networking |
| spelling | doaj.art-395f73525e4f4d7aa68166f56ce0b8732022-12-22T01:18:06ZengSpringerOpenEURASIP Journal on Wireless Communications and Networking1687-14992020-07-012020113110.1186/s13638-020-01738-wDistributed algorithm under cooperative or competitive priority users in cognitive networksMahmoud Almasri0Ali Mansour1Christophe Moy2Ammar Assoum3Christophe Osswald4Denis Lejeune5LABSTICC, UMR 6285 CNRS, ENSTA BretagneLABSTICC, UMR 6285 CNRS, ENSTA BretagneUniv Rennes, CNRSLebanese University, Faculty of ScienceLABSTICC, UMR 6285 CNRS, ENSTA BretagneLABSTICC, UMR 6285 CNRS, ENSTA BretagneAbstract 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), ε-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 multi-user 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 multi-user 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.http://link.springer.com/article/10.1186/s13638-020-01738-wCooperative or competitive priority accessCognitive radioOpportunistic spectrum accessMulti-armed bandit algorithmsUpper bound of regret |
| spellingShingle | Mahmoud Almasri Ali Mansour Christophe Moy Ammar Assoum Christophe Osswald Denis Lejeune Distributed algorithm under cooperative or competitive priority users in cognitive networks EURASIP Journal on Wireless Communications and Networking Cooperative or competitive priority access Cognitive radio Opportunistic spectrum access Multi-armed bandit algorithms Upper bound of regret |
| title | Distributed algorithm under cooperative or competitive priority users in cognitive networks |
| title_full | Distributed algorithm under cooperative or competitive priority users in cognitive networks |
| title_fullStr | Distributed algorithm under cooperative or competitive priority users in cognitive networks |
| title_full_unstemmed | Distributed algorithm under cooperative or competitive priority users in cognitive networks |
| title_short | Distributed algorithm under cooperative or competitive priority users in cognitive networks |
| title_sort | distributed algorithm under cooperative or competitive priority users in cognitive networks |
| topic | Cooperative or competitive priority access Cognitive radio Opportunistic spectrum access Multi-armed bandit algorithms Upper bound of regret |
| url | http://link.springer.com/article/10.1186/s13638-020-01738-w |
| work_keys_str_mv | AT mahmoudalmasri distributedalgorithmundercooperativeorcompetitivepriorityusersincognitivenetworks AT alimansour distributedalgorithmundercooperativeorcompetitivepriorityusersincognitivenetworks AT christophemoy distributedalgorithmundercooperativeorcompetitivepriorityusersincognitivenetworks AT ammarassoum distributedalgorithmundercooperativeorcompetitivepriorityusersincognitivenetworks AT christopheosswald distributedalgorithmundercooperativeorcompetitivepriorityusersincognitivenetworks AT denislejeune distributedalgorithmundercooperativeorcompetitivepriorityusersincognitivenetworks |