Q-learning-based dynamic joint control of interference and transmission opportunities for cognitive radio
Abstract In cognitive radio (CR) system, secondary user (SU) should use available channels opportunistically when the primary user (PU) does not exist. In CR network, SUs have to detect the PU signal with sufficient sensing time to guarantee the detection probability and minimize the interference to...
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Online Access: | http://link.springer.com/article/10.1186/s13638-018-1155-9 |
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doaj-1689d10960d1476688d37d84865ded1e2020-11-25T02:01:47ZengSpringerOpenEURASIP Journal on Wireless Communications and Networking1687-14992018-06-012018112410.1186/s13638-018-1155-9Q-learning-based dynamic joint control of interference and transmission opportunities for cognitive radioSung-Jeen Jang0Sang-Jo Yoo1Multimedia Network Lab, Inha UniversityMultimedia Network Lab, Inha UniversityAbstract In cognitive radio (CR) system, secondary user (SU) should use available channels opportunistically when the primary user (PU) does not exist. In CR network, SUs have to detect the PU signal with sufficient sensing time to guarantee the detection probability and minimize the interference to the PU, while the CR system should have enough data transmission time to maximize the transmission opportunity of the SU. Therefore, the sensing time and data transmission time of the SU are generally considered as main optimization parameters to maximize the throughput of the CR system. In this paper, a separate sensing node is designated and the sensing is continuously performed using the interference alignment (IA) technique. In this paper, the designated sensing node estimates the interference ratio and transmission opportunity loss ratio. To satisfy the primary user’s interference requirement and maximize secondary throughput, we proposed dynamic adjustment mechanism for sensing slot time and sensing report interval using reinforcement learning in time-varying communication environment. The experimental results show that the proposed approach can minimize the interference on PU and enhance the transmission opportunity of SUs.http://link.springer.com/article/10.1186/s13638-018-1155-9Cognitive radioInterference alignmentSpectrum sensingQ-learning |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Sung-Jeen Jang Sang-Jo Yoo |
spellingShingle |
Sung-Jeen Jang Sang-Jo Yoo Q-learning-based dynamic joint control of interference and transmission opportunities for cognitive radio EURASIP Journal on Wireless Communications and Networking Cognitive radio Interference alignment Spectrum sensing Q-learning |
author_facet |
Sung-Jeen Jang Sang-Jo Yoo |
author_sort |
Sung-Jeen Jang |
title |
Q-learning-based dynamic joint control of interference and transmission opportunities for cognitive radio |
title_short |
Q-learning-based dynamic joint control of interference and transmission opportunities for cognitive radio |
title_full |
Q-learning-based dynamic joint control of interference and transmission opportunities for cognitive radio |
title_fullStr |
Q-learning-based dynamic joint control of interference and transmission opportunities for cognitive radio |
title_full_unstemmed |
Q-learning-based dynamic joint control of interference and transmission opportunities for cognitive radio |
title_sort |
q-learning-based dynamic joint control of interference and transmission opportunities for cognitive radio |
publisher |
SpringerOpen |
series |
EURASIP Journal on Wireless Communications and Networking |
issn |
1687-1499 |
publishDate |
2018-06-01 |
description |
Abstract In cognitive radio (CR) system, secondary user (SU) should use available channels opportunistically when the primary user (PU) does not exist. In CR network, SUs have to detect the PU signal with sufficient sensing time to guarantee the detection probability and minimize the interference to the PU, while the CR system should have enough data transmission time to maximize the transmission opportunity of the SU. Therefore, the sensing time and data transmission time of the SU are generally considered as main optimization parameters to maximize the throughput of the CR system. In this paper, a separate sensing node is designated and the sensing is continuously performed using the interference alignment (IA) technique. In this paper, the designated sensing node estimates the interference ratio and transmission opportunity loss ratio. To satisfy the primary user’s interference requirement and maximize secondary throughput, we proposed dynamic adjustment mechanism for sensing slot time and sensing report interval using reinforcement learning in time-varying communication environment. The experimental results show that the proposed approach can minimize the interference on PU and enhance the transmission opportunity of SUs. |
topic |
Cognitive radio Interference alignment Spectrum sensing Q-learning |
url |
http://link.springer.com/article/10.1186/s13638-018-1155-9 |
work_keys_str_mv |
AT sungjeenjang qlearningbaseddynamicjointcontrolofinterferenceandtransmissionopportunitiesforcognitiveradio AT sangjoyoo qlearningbaseddynamicjointcontrolofinterferenceandtransmissionopportunitiesforcognitiveradio |
_version_ |
1724955863486038016 |