Online Learning Based Transmission Scheduling for Delay-Sensitive Data Over a Fading Channel With Imperfect Channel State Information
This paper considers the problem of transmission scheduling of delay-sensitive data over a point-to-point correlated Rayleigh fading channel with channel estimation errors. According to the imperfect channel state information (CSI) and the buffer state, the transmit power and the modulation and codi...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2017-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/7982619/ |
| _version_ | 1818411537848598528 |
|---|---|
| author | Jun-Bo Wang Nan Li Jin-YUAN Wang Yong-Peng Wu Ming Cheng Ming Chen |
| author_facet | Jun-Bo Wang Nan Li Jin-YUAN Wang Yong-Peng Wu Ming Cheng Ming Chen |
| author_sort | Jun-Bo Wang |
| collection | DOAJ |
| description | This paper considers the problem of transmission scheduling of delay-sensitive data over a point-to-point correlated Rayleigh fading channel with channel estimation errors. According to the imperfect channel state information (CSI) and the buffer state, the transmit power and the modulation and coding scheme are determined to jointly maximize the energy efficiency, and minimize transmission delay and overflow probability. To account of the effects of the channel estimation errors, the CSI imperfection is modeled as uncertain sets using the ellipsoidal approximation. Then, the joint optimization problem is formulated using the weighted sum method. Using the idea of online learning, two algorithms are proposed to schedule the delay-sensitive data for the situations with and without the uncertainty bound of channel estimation, respectively. Numerical results indicate that the proposed online learning-based scheduling algorithms can tackle the imperfect CSI issue and improve the system performance in terms of the energy efficiency, transmission delay, and overflow probability. Moreover, the convergence times are very short, which highlights the feasibility of the proposed online learning-based scheduling for practical systems. |
| first_indexed | 2024-12-14T10:33:00Z |
| format | Article |
| id | doaj.art-58dab1621e3847d8a4a037272c36c11d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T10:33:00Z |
| publishDate | 2017-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-58dab1621e3847d8a4a037272c36c11d2022-12-21T23:06:04ZengIEEEIEEE Access2169-35362017-01-015132251323510.1109/ACCESS.2017.27275197982619Online Learning Based Transmission Scheduling for Delay-Sensitive Data Over a Fading Channel With Imperfect Channel State InformationJun-Bo Wang0Nan Li1Jin-YUAN Wang2https://orcid.org/0000-0001-5745-4554Yong-Peng Wu3Ming Cheng4https://orcid.org/0000-0001-6332-2443Ming Chen5National Mobile Communications Research Laboratory, Southeast University, Nanjing, ChinaNational Mobile Communications Research Laboratory, Southeast University, Nanjing, ChinaNational Mobile Communications Research Laboratory, Southeast University, Nanjing, ChinaDepartment of Electronic Engineering, Shanghai Jiao Tong University, Minhang, ChinaNational Mobile Communications Research Laboratory, Southeast University, Nanjing, ChinaNational Mobile Communications Research Laboratory, Southeast University, Nanjing, ChinaThis paper considers the problem of transmission scheduling of delay-sensitive data over a point-to-point correlated Rayleigh fading channel with channel estimation errors. According to the imperfect channel state information (CSI) and the buffer state, the transmit power and the modulation and coding scheme are determined to jointly maximize the energy efficiency, and minimize transmission delay and overflow probability. To account of the effects of the channel estimation errors, the CSI imperfection is modeled as uncertain sets using the ellipsoidal approximation. Then, the joint optimization problem is formulated using the weighted sum method. Using the idea of online learning, two algorithms are proposed to schedule the delay-sensitive data for the situations with and without the uncertainty bound of channel estimation, respectively. Numerical results indicate that the proposed online learning-based scheduling algorithms can tackle the imperfect CSI issue and improve the system performance in terms of the energy efficiency, transmission delay, and overflow probability. Moreover, the convergence times are very short, which highlights the feasibility of the proposed online learning-based scheduling for practical systems.https://ieeexplore.ieee.org/document/7982619/Transmission schedulingimperfect channel state informationdelay sensitiveonline learning |
| spellingShingle | Jun-Bo Wang Nan Li Jin-YUAN Wang Yong-Peng Wu Ming Cheng Ming Chen Online Learning Based Transmission Scheduling for Delay-Sensitive Data Over a Fading Channel With Imperfect Channel State Information IEEE Access Transmission scheduling imperfect channel state information delay sensitive online learning |
| title | Online Learning Based Transmission Scheduling for Delay-Sensitive Data Over a Fading Channel With Imperfect Channel State Information |
| title_full | Online Learning Based Transmission Scheduling for Delay-Sensitive Data Over a Fading Channel With Imperfect Channel State Information |
| title_fullStr | Online Learning Based Transmission Scheduling for Delay-Sensitive Data Over a Fading Channel With Imperfect Channel State Information |
| title_full_unstemmed | Online Learning Based Transmission Scheduling for Delay-Sensitive Data Over a Fading Channel With Imperfect Channel State Information |
| title_short | Online Learning Based Transmission Scheduling for Delay-Sensitive Data Over a Fading Channel With Imperfect Channel State Information |
| title_sort | online learning based transmission scheduling for delay sensitive data over a fading channel with imperfect channel state information |
| topic | Transmission scheduling imperfect channel state information delay sensitive online learning |
| url | https://ieeexplore.ieee.org/document/7982619/ |
| work_keys_str_mv | AT junbowang onlinelearningbasedtransmissionschedulingfordelaysensitivedataoverafadingchannelwithimperfectchannelstateinformation AT nanli onlinelearningbasedtransmissionschedulingfordelaysensitivedataoverafadingchannelwithimperfectchannelstateinformation AT jinyuanwang onlinelearningbasedtransmissionschedulingfordelaysensitivedataoverafadingchannelwithimperfectchannelstateinformation AT yongpengwu onlinelearningbasedtransmissionschedulingfordelaysensitivedataoverafadingchannelwithimperfectchannelstateinformation AT mingcheng onlinelearningbasedtransmissionschedulingfordelaysensitivedataoverafadingchannelwithimperfectchannelstateinformation AT mingchen onlinelearningbasedtransmissionschedulingfordelaysensitivedataoverafadingchannelwithimperfectchannelstateinformation |