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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Main Authors: Sung-Jeen Jang, Sang-Jo Yoo
Format: Article
Language:English
Published: SpringerOpen 2018-06-01
Series:EURASIP Journal on Wireless Communications and Networking
Subjects:
Online Access:http://link.springer.com/article/10.1186/s13638-018-1155-9
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spelling 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
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