Dynamical Spectrum Sharing and Medium Access Control for Heterogeneous Cognitive Radio Networks
This paper tackles the issue of spectrum sharing and medium access control among heterogeneous secondary users. Two solutions are proposed in this paper. The first solution can be used in centralized fashion where a central entity exists which decides transmission power for all secondary users. This...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2016-05-01
|
| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2016/3630593 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849702227876249600 |
|---|---|
| author | Ahmed Mohamedou Aduwati Sali Borhanuddin Ali Mohamed Othman |
| author_facet | Ahmed Mohamedou Aduwati Sali Borhanuddin Ali Mohamed Othman |
| author_sort | Ahmed Mohamedou |
| collection | DOAJ |
| description | This paper tackles the issue of spectrum sharing and medium access control among heterogeneous secondary users. Two solutions are proposed in this paper. The first solution can be used in centralized fashion where a central entity exists which decides transmission power for all secondary users. This solution tries to minimize the time required by secondary users to clear their queues. The second solution assumes the autonomy of secondary users where the decision to update transmission power is distributed among users. Dynamical system approach is used to model system behavior. The trajectory of interference noise level suffered by secondary users is used to update transmission power at the beginning of each time frame based on the proposed dynamic power assignment rule. This rule couples the responses of all secondary users in a way which simplifies future interference noise forecasting. A forecasting engine based on deep neural network is proposed. This engine gives secondary users the ability to acquire useful knowledge from surrounding wireless environment. As a result, better transmission power allocation is achieved. Evaluation experiments have confirmed that adopting deep neural network can improve the performance by 46% on average. All of the proposed solutions have achieved an outstanding performance. |
| format | Article |
| id | doaj-art-29a80c7a2f834a919f4e2482e9264868 |
| institution | DOAJ |
| issn | 1550-1477 |
| language | English |
| publishDate | 2016-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-29a80c7a2f834a919f4e2482e92648682025-08-20T03:17:43ZengWileyInternational Journal of Distributed Sensor Networks1550-14772016-05-011210.1155/2016/3630593Dynamical Spectrum Sharing and Medium Access Control for Heterogeneous Cognitive Radio NetworksAhmed Mohamedou0Aduwati Sali1Borhanuddin Ali2Mohamed Othman3 Department of Computer and Communication Systems Engineering, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia Department of Computer and Communication Systems Engineering, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia Department of Computer and Communication Systems Engineering, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia Department of Communication Technology and Networks, Universiti Putra Malaysia, 43400 Serdang, Selangor, MalaysiaThis paper tackles the issue of spectrum sharing and medium access control among heterogeneous secondary users. Two solutions are proposed in this paper. The first solution can be used in centralized fashion where a central entity exists which decides transmission power for all secondary users. This solution tries to minimize the time required by secondary users to clear their queues. The second solution assumes the autonomy of secondary users where the decision to update transmission power is distributed among users. Dynamical system approach is used to model system behavior. The trajectory of interference noise level suffered by secondary users is used to update transmission power at the beginning of each time frame based on the proposed dynamic power assignment rule. This rule couples the responses of all secondary users in a way which simplifies future interference noise forecasting. A forecasting engine based on deep neural network is proposed. This engine gives secondary users the ability to acquire useful knowledge from surrounding wireless environment. As a result, better transmission power allocation is achieved. Evaluation experiments have confirmed that adopting deep neural network can improve the performance by 46% on average. All of the proposed solutions have achieved an outstanding performance.https://doi.org/10.1155/2016/3630593 |
| spellingShingle | Ahmed Mohamedou Aduwati Sali Borhanuddin Ali Mohamed Othman Dynamical Spectrum Sharing and Medium Access Control for Heterogeneous Cognitive Radio Networks International Journal of Distributed Sensor Networks |
| title | Dynamical Spectrum Sharing and Medium Access Control for Heterogeneous Cognitive Radio Networks |
| title_full | Dynamical Spectrum Sharing and Medium Access Control for Heterogeneous Cognitive Radio Networks |
| title_fullStr | Dynamical Spectrum Sharing and Medium Access Control for Heterogeneous Cognitive Radio Networks |
| title_full_unstemmed | Dynamical Spectrum Sharing and Medium Access Control for Heterogeneous Cognitive Radio Networks |
| title_short | Dynamical Spectrum Sharing and Medium Access Control for Heterogeneous Cognitive Radio Networks |
| title_sort | dynamical spectrum sharing and medium access control for heterogeneous cognitive radio networks |
| url | https://doi.org/10.1155/2016/3630593 |
| work_keys_str_mv | AT ahmedmohamedou dynamicalspectrumsharingandmediumaccesscontrolforheterogeneouscognitiveradionetworks AT aduwatisali dynamicalspectrumsharingandmediumaccesscontrolforheterogeneouscognitiveradionetworks AT borhanuddinali dynamicalspectrumsharingandmediumaccesscontrolforheterogeneouscognitiveradionetworks AT mohamedothman dynamicalspectrumsharingandmediumaccesscontrolforheterogeneouscognitiveradionetworks |