Category-Based 802.11ax Target Wake Time Solution
IEEE 802.11ax newly introduced Target Wake Time (TWT) function which enables joint reduction of device power consumption and wireless medium congestion through negotiated TWT wake interval and duration. We develop a novel solution which can dynamically configure station (STA) wake interval and durat...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9481882/ |
| _version_ | 1818455518927126528 |
|---|---|
| author | Wenxun Qiu Guanbo Chen Khuong N. Nguyen Abhishek Sehgal Peshal Nayak Junsu Choi |
| author_facet | Wenxun Qiu Guanbo Chen Khuong N. Nguyen Abhishek Sehgal Peshal Nayak Junsu Choi |
| author_sort | Wenxun Qiu |
| collection | DOAJ |
| description | IEEE 802.11ax newly introduced Target Wake Time (TWT) function which enables joint reduction of device power consumption and wireless medium congestion through negotiated TWT wake interval and duration. We develop a novel solution which can dynamically configure station (STA) wake interval and duration based on run time recognition of the Quality of Service (QoS) requirement including the required latency and throughput. Specifically, a Machine Learning (ML) based Network Service Detection (NSD) module is developed which can detect the current network service in real time and update the STA wake interval according to the corresponding service latency requirement. Moreover, a novel data time estimation model is developed which can estimate the required throughput and wake duration with the observed throughput, linkspeed, and contention level. In addition, a state-machine based method is developed to detect three different traffic types (random, stable, bursty) and their throughput variation pattern for further optimization of the STA wake duration. Our solution is implemented and extensively tested on commercial smart mobile platform under various network conditions and use cases. The results show that our ML based NSD could reach 99.2% and 96.5% accuracy respectively for coarse-grain and fine-grain network service classification, which ensures our TWT solution can accurately recognize and satisfy the QoS requirements. More importantly, while maintaining the QoS and user experience, our solution can substantially reduce the Wi-Fi duty cycle to 29.6% on average, which leads to lower device power consumption and network contention level. |
| first_indexed | 2024-12-14T22:12:03Z |
| format | Article |
| id | doaj.art-7a769a2482c543a9bc09960489a76fe2 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T22:12:03Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7a769a2482c543a9bc09960489a76fe22022-12-21T22:45:44ZengIEEEIEEE Access2169-35362021-01-01910015410017210.1109/ACCESS.2021.30969409481882Category-Based 802.11ax Target Wake Time SolutionWenxun Qiu0Guanbo Chen1https://orcid.org/0000-0003-2564-3195Khuong N. Nguyen2https://orcid.org/0000-0003-2631-350XAbhishek Sehgal3https://orcid.org/0000-0001-7128-6438Peshal Nayak4https://orcid.org/0000-0001-7128-6147Junsu Choi5Standards and Mobility Innovation Laboratory, Samsung Research America, Plano, TX, USAStandards and Mobility Innovation Laboratory, Samsung Research America, Plano, TX, USAStandards and Mobility Innovation Laboratory, Samsung Research America, Plano, TX, USAStandards and Mobility Innovation Laboratory, Samsung Research America, Plano, TX, USAStandards and Mobility Innovation Laboratory, Samsung Research America, Plano, TX, USASamsung Electronics Company Ltd., Suwon, South KoreaIEEE 802.11ax newly introduced Target Wake Time (TWT) function which enables joint reduction of device power consumption and wireless medium congestion through negotiated TWT wake interval and duration. We develop a novel solution which can dynamically configure station (STA) wake interval and duration based on run time recognition of the Quality of Service (QoS) requirement including the required latency and throughput. Specifically, a Machine Learning (ML) based Network Service Detection (NSD) module is developed which can detect the current network service in real time and update the STA wake interval according to the corresponding service latency requirement. Moreover, a novel data time estimation model is developed which can estimate the required throughput and wake duration with the observed throughput, linkspeed, and contention level. In addition, a state-machine based method is developed to detect three different traffic types (random, stable, bursty) and their throughput variation pattern for further optimization of the STA wake duration. Our solution is implemented and extensively tested on commercial smart mobile platform under various network conditions and use cases. The results show that our ML based NSD could reach 99.2% and 96.5% accuracy respectively for coarse-grain and fine-grain network service classification, which ensures our TWT solution can accurately recognize and satisfy the QoS requirements. More importantly, while maintaining the QoS and user experience, our solution can substantially reduce the Wi-Fi duty cycle to 29.6% on average, which leads to lower device power consumption and network contention level.https://ieeexplore.ieee.org/document/9481882/802.11axtarget wake timemachine learningservice classificationtraffic detection |
| spellingShingle | Wenxun Qiu Guanbo Chen Khuong N. Nguyen Abhishek Sehgal Peshal Nayak Junsu Choi Category-Based 802.11ax Target Wake Time Solution IEEE Access 802.11ax target wake time machine learning service classification traffic detection |
| title | Category-Based 802.11ax Target Wake Time Solution |
| title_full | Category-Based 802.11ax Target Wake Time Solution |
| title_fullStr | Category-Based 802.11ax Target Wake Time Solution |
| title_full_unstemmed | Category-Based 802.11ax Target Wake Time Solution |
| title_short | Category-Based 802.11ax Target Wake Time Solution |
| title_sort | category based 802 11ax target wake time solution |
| topic | 802.11ax target wake time machine learning service classification traffic detection |
| url | https://ieeexplore.ieee.org/document/9481882/ |
| work_keys_str_mv | AT wenxunqiu categorybased80211axtargetwaketimesolution AT guanbochen categorybased80211axtargetwaketimesolution AT khuongnnguyen categorybased80211axtargetwaketimesolution AT abhisheksehgal categorybased80211axtargetwaketimesolution AT peshalnayak categorybased80211axtargetwaketimesolution AT junsuchoi categorybased80211axtargetwaketimesolution |