Open Access
Issue |
JNWPU
Volume 41, Number 5, Octobre 2023
|
|
---|---|---|
Page(s) | 887 - 894 | |
DOI | https://doi.org/10.1051/jnwpu/20234150887 | |
Published online | 11 December 2023 |
- AFAQUI M S, GARCIA-VILLEGAS E, LOPEZ-AGUILERA E, et al. Dynamic sensitivity control of access pointsfor IEEE 802.11ax[C]//2016 IEEE International Conference on Communications, 2016: 1–7 [Google Scholar]
- IWAI K, OHNUMA T, SHIGENO H, et al. Improving of fairness by dynamic sensitivity control and transmission power control with access point cooperation in dense WLAN[C]//2019 16th IEEE Annual Consumer Communications & Networking Conference, 2019: 1–4 [Google Scholar]
- WONG S H, YANG H, LU S, et al. Robust rate adaptation for 802.11 wireless networks[C]//Proceedings of the 12th Annual International Conference on Mobile Computing and Networking, 2006: 146–157 [Google Scholar]
- LING X, YEUNG K L. Joint access point placement and channel assignment for 802.11 wireless LANS[J]. IEEE Trans on Wireless Communications. 2006, 5(10): 2705–2711 [Article] [CrossRef] [Google Scholar]
- KHOUKHI L, BADIS H, MERGHEM-BOULAHIA L, et al. Admission control in wireless Ad Hoc networks: a survey[J]. EURASIP Journal on Wireless Communications and Networking, 2013, 2013(1): 1–13 [Article] [CrossRef] [Google Scholar]
- MHATRE V P, PAPAGIANNAKI K, BACCELLI F. Interference mitigation through power control in high density 802.11 WLANs[C]//IEEE INFOCOM 2007-26th IEEE International Conference on Computer Communications, 2007: 535–543 [Google Scholar]
- WILHELMI F, CANO C, NEU G, et al. Collaborative spatial reuse in wireless networks via selfish multi-armed bandits[J]. Ad Hoc Networks, 2019, 88: 129–141 [Article] [CrossRef] [Google Scholar]
- MENG F, CHEN P, WU L, et al. Power allocation in multi-user cellular networks: deep reinforcementlearning approaches[J]. IEEE Trans on Wireless Communications, 2020, 19(10): 6255–6267 [Article] [Google Scholar]
- WILHELMI F, MUOZ S B, CANO C, et al. Spatial reuse in IEEE 802.11ax WLANs[J]. Computer Communications, 2021, 170: 65–83 [Article] [CrossRef] [Google Scholar]
- LIN W, BO L, MAO Y, et al. Integrated link-system level simulation platform for the next generation WLAN-IEEE 802.11ax[C]//Global Communications Conference, 2017 [Google Scholar]
- JAIN R, CHIU D M W, HAWE W. A quantitative measure of fairness and discrimination for resource allocation in shared computer system[J/OL](1998-09-24)[2022-11-17]. https://arxiv.org/abs/cs/9809099 [Google Scholar]
- ZHU J, SONG Y, JIANG D, et al. A new deep-Q-learning-based transmission scheduling mechanism for the cognitive internet of things[J]. IEEE Internet of Things Journal, 2018, 5(4): 2375–2385 [Article] [CrossRef] [Google Scholar]
- SUN H, CHEN X, SHI Q, et al. Learning to optimize: training deep neural networks for interference management[J]. IEEE Trans on Signal Processing, 2018, 66(20): 5438–5453 [Article] [NASA ADS] [CrossRef] [Google Scholar]
- BENNIS M, NIYATO D. A Q-learning based approach to interference avoidance in self-organized femtocell networks[C]//2010 IEEE Globecom Workshops, 2010: 706–710 [Google Scholar]
- MURUGAN P, DURAIRAJ S. Regularization and optimization strategies in deep convolutional neural network[J/OL]. (2017-12-13)[2022-11-17]. https://arxiv.org/abs/1712.04711 [Google Scholar]
- WILHELMI F, CANO C, NEU G, et al. Collaborative spatial reuse in wireless networks via selfish multi-armed bandits[J]. Ad Hoc Networks, 2019, 88: 129–141 [Article] [CrossRef] [Google Scholar]
- CAO X, MA R, LIU L, et al. A machine learning-based algorithm for joint scheduling and power control in wireless networks[J]. IEEE Internet of Things Journal, 2018, 5(6): 4308–4318 [Article] [CrossRef] [Google Scholar]
- NASIR Y S, GUO D. Multi-agent deep reinforcement learning for dynamic power allocation in wireless networks[J]. IEEE Journal on Selected Areas in Communications, 2019, 37(10): 2239–2250 [Article] [CrossRef] [Google Scholar]
- OKAMOTO H, NISHIO T, MORIKURA M, et al. Machine-learning-based throughput estimation using images for mmwave communications[C]//2017 IEEE 85th Vehicular Technology Conference, 2017 [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.