Reinforcement Learning for Dynamic Spectrum Management in WCDMA
Low use of licensed spectrum imposes a need for the advanced spectrum management for wise spectrum usage with the release of unneeded frequency bands for the secondary markets and opportunistic access. In this paper we present the possibilities to apply reinforcement learning in WCDMA to enable the...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Telecommunications Society, Academic Mind
2009-06-01
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| Series: | Telfor Journal |
| Subjects: | |
| Online Access: | http://journal.telfor.rs/Published/Vol1No1/Vol1No1_A2.pdf |
| _version_ | 1818210420485259264 |
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| author | R. Agustí O. Sallent J. Pérez-Romero N. Vučević |
| author_facet | R. Agustí O. Sallent J. Pérez-Romero N. Vučević |
| author_sort | R. Agustí |
| collection | DOAJ |
| description | Low use of licensed spectrum imposes a need for the advanced spectrum management for wise spectrum usage with the release of unneeded frequency bands for the secondary markets and opportunistic access. In this paper we present the possibilities to apply reinforcement learning in WCDMA to enable the autonomous decision on spectrum repartition among cells and release of frequency bands for possible secondary usage. The proposed solution increases spectrum efficiency while ensuring maximum outage probability constraints in WCDMA uplink. We give two possible approaches to implement reinforcement learning in this problem area and compare their behavior. Simulations demonstrate the capability of two methods to successfully achieve desired goals. |
| first_indexed | 2024-12-12T05:16:19Z |
| format | Article |
| id | doaj.art-a1baf0047b0e429d8c4264b844996a7c |
| institution | Directory Open Access Journal |
| issn | 1821-3251 |
| language | English |
| last_indexed | 2024-12-12T05:16:19Z |
| publishDate | 2009-06-01 |
| publisher | Telecommunications Society, Academic Mind |
| record_format | Article |
| series | Telfor Journal |
| spelling | doaj.art-a1baf0047b0e429d8c4264b844996a7c2022-12-22T00:36:45ZengTelecommunications Society, Academic MindTelfor Journal1821-32512009-06-011169Reinforcement Learning for Dynamic Spectrum Management in WCDMAR. AgustíO. SallentJ. Pérez-RomeroN. VučevićLow use of licensed spectrum imposes a need for the advanced spectrum management for wise spectrum usage with the release of unneeded frequency bands for the secondary markets and opportunistic access. In this paper we present the possibilities to apply reinforcement learning in WCDMA to enable the autonomous decision on spectrum repartition among cells and release of frequency bands for possible secondary usage. The proposed solution increases spectrum efficiency while ensuring maximum outage probability constraints in WCDMA uplink. We give two possible approaches to implement reinforcement learning in this problem area and compare their behavior. Simulations demonstrate the capability of two methods to successfully achieve desired goals.http://journal.telfor.rs/Published/Vol1No1/Vol1No1_A2.pdf dynamic spectrum managementreinforcement learningWCDMA |
| spellingShingle | R. Agustí O. Sallent J. Pérez-Romero N. Vučević Reinforcement Learning for Dynamic Spectrum Management in WCDMA Telfor Journal dynamic spectrum management reinforcement learning WCDMA |
| title | Reinforcement Learning for Dynamic Spectrum Management in WCDMA |
| title_full | Reinforcement Learning for Dynamic Spectrum Management in WCDMA |
| title_fullStr | Reinforcement Learning for Dynamic Spectrum Management in WCDMA |
| title_full_unstemmed | Reinforcement Learning for Dynamic Spectrum Management in WCDMA |
| title_short | Reinforcement Learning for Dynamic Spectrum Management in WCDMA |
| title_sort | reinforcement learning for dynamic spectrum management in wcdma |
| topic | dynamic spectrum management reinforcement learning WCDMA |
| url | http://journal.telfor.rs/Published/Vol1No1/Vol1No1_A2.pdf |
| work_keys_str_mv | AT ragusti reinforcementlearningfordynamicspectrummanagementinwcdma AT osallent reinforcementlearningfordynamicspectrummanagementinwcdma AT jperezromero reinforcementlearningfordynamicspectrummanagementinwcdma AT nvucevic reinforcementlearningfordynamicspectrummanagementinwcdma |