Dynamic Link Scheduling in Wireless Networks Through Fuzzy-Enhanced Deep Learning
In this paper, we present the Learning Greedy Link Scheduling (LGLS) algorithm, a novel approach for optimizing link scheduling in wireless networks. By integrating deep learning and fuzzy logic, LGLS predicts link quality probabilities, which provide critical topological information to dynamically...
Saved in:
| Main Authors: | Maryam Abbasalizadeh, Krishnaa Vellamchety, Pranathi Rayavaram, Sashank Narain |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Open Journal of the Communications Society |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10729871/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Context-aware link scheduling in static wireless networks with successive interference cancellation
by: Shao-he LV, et al.
Published: (2012-08-01) -
Deep belief network-based link quality prediction for wireless sensor network
by: Lin-lan LIU, et al.
Published: (2017-11-01) -
Optimization of Job Scheduling in the Cloud Computing Environment Using the Fuzzy Particle Swarm Optimization Algorithm
by: Shabnam Gharaeian, et al.
Published: (2020-09-01) -
Link duration probability based cross-layer routing protocol for epoch-based movement
by: SUN Jie, et al.
Published: (2010-01-01) -
Prioritize Effective Factors of Scheduling in Tehran Metro Station with Fuzzy TOPSIS Model
by: Vinh Phuc Dung, et al.
Published: (2022-10-01)